idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
|---|---|---|---|---|---|---|
55,201 | What is a "log-F" distribution? | It is a Type IV Generalized Logistic distribution.
Begin with the pdf of an $F(\nu_1, \nu_2)$ distribution ($\nu_1$ is "df1" and $\nu_2$ is "df2"), written $f(x)$. The pdf for the logarithm $y = \log(x)$ will be the coefficient of $dy$ in
$$f(\exp(y))\, |\mathrm d \exp(y)| =\frac{1}{B\left(\frac{\nu _1}{2},\frac{\nu ... | What is a "log-F" distribution? | It is a Type IV Generalized Logistic distribution.
Begin with the pdf of an $F(\nu_1, \nu_2)$ distribution ($\nu_1$ is "df1" and $\nu_2$ is "df2"), written $f(x)$. The pdf for the logarithm $y = \lo | What is a "log-F" distribution?
It is a Type IV Generalized Logistic distribution.
Begin with the pdf of an $F(\nu_1, \nu_2)$ distribution ($\nu_1$ is "df1" and $\nu_2$ is "df2"), written $f(x)$. The pdf for the logarithm $y = \log(x)$ will be the coefficient of $dy$ in
$$f(\exp(y))\, |\mathrm d \exp(y)| =\frac{1}{B\... | What is a "log-F" distribution?
It is a Type IV Generalized Logistic distribution.
Begin with the pdf of an $F(\nu_1, \nu_2)$ distribution ($\nu_1$ is "df1" and $\nu_2$ is "df2"), written $f(x)$. The pdf for the logarithm $y = \lo |
55,202 | Gibbs Sampling and Probability Notation | Very clean and clear presentation of the issue! This is exactly a hidden Ising model.
Your probabilities $\mathbb{P}(X_i=1|X_{[-i]},Y_i)$ and $\mathbb{P}(X_i=-1|X_{[-i]},Y_i)$ give you the way to simulate $X_i$ conditional on the others and on the observations. That those probabilities are non-integer is not an issue. ... | Gibbs Sampling and Probability Notation | Very clean and clear presentation of the issue! This is exactly a hidden Ising model.
Your probabilities $\mathbb{P}(X_i=1|X_{[-i]},Y_i)$ and $\mathbb{P}(X_i=-1|X_{[-i]},Y_i)$ give you the way to simu | Gibbs Sampling and Probability Notation
Very clean and clear presentation of the issue! This is exactly a hidden Ising model.
Your probabilities $\mathbb{P}(X_i=1|X_{[-i]},Y_i)$ and $\mathbb{P}(X_i=-1|X_{[-i]},Y_i)$ give you the way to simulate $X_i$ conditional on the others and on the observations. That those probabi... | Gibbs Sampling and Probability Notation
Very clean and clear presentation of the issue! This is exactly a hidden Ising model.
Your probabilities $\mathbb{P}(X_i=1|X_{[-i]},Y_i)$ and $\mathbb{P}(X_i=-1|X_{[-i]},Y_i)$ give you the way to simu |
55,203 | Gibbs Sampling and Probability Notation | Answer to the follow-up question
Unfortunately, your intuition about the smoothing distribution $\mathbb{P}(x|y)$ is not correct as $\mathbb{P}(x|y)$ is not the product of the full conditionals:
$$
\prod_{i=1}^9 \mathbb{P}(x_i|x_{[-i]},y) \ne \mathbb{P}(x|y)
$$
even up to a proportionality constant. And even less on a ... | Gibbs Sampling and Probability Notation | Answer to the follow-up question
Unfortunately, your intuition about the smoothing distribution $\mathbb{P}(x|y)$ is not correct as $\mathbb{P}(x|y)$ is not the product of the full conditionals:
$$
\p | Gibbs Sampling and Probability Notation
Answer to the follow-up question
Unfortunately, your intuition about the smoothing distribution $\mathbb{P}(x|y)$ is not correct as $\mathbb{P}(x|y)$ is not the product of the full conditionals:
$$
\prod_{i=1}^9 \mathbb{P}(x_i|x_{[-i]},y) \ne \mathbb{P}(x|y)
$$
even up to a propo... | Gibbs Sampling and Probability Notation
Answer to the follow-up question
Unfortunately, your intuition about the smoothing distribution $\mathbb{P}(x|y)$ is not correct as $\mathbb{P}(x|y)$ is not the product of the full conditionals:
$$
\p |
55,204 | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an exponential distribution? | The hazard function will be $\frac{f(t)}{1-F(t)}$.
Assuming lifetime to be exponential, the formula is given at that link; it's $\lambda$, the rate parameter of the exponential; you estimate that rate parameter $\lambda$ from the data exactly the same way you would for any exponential distribution.
The hourly mortalit... | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an ex | The hazard function will be $\frac{f(t)}{1-F(t)}$.
Assuming lifetime to be exponential, the formula is given at that link; it's $\lambda$, the rate parameter of the exponential; you estimate that rate | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an exponential distribution?
The hazard function will be $\frac{f(t)}{1-F(t)}$.
Assuming lifetime to be exponential, the formula is given at that link; it's $\lambda$, the rate parameter of the exponential; you estimate that ... | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an ex
The hazard function will be $\frac{f(t)}{1-F(t)}$.
Assuming lifetime to be exponential, the formula is given at that link; it's $\lambda$, the rate parameter of the exponential; you estimate that rate |
55,205 | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an exponential distribution? | The question asks about the relationship between hourly mortality and exponential distribution of lifespans.
The universe divides time into short ticks. For each bee a die is rolled. The die has many sides, most of them blank--but one is black. Death comes for the bee whenever the black side shows.
We are about to s... | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an ex | The question asks about the relationship between hourly mortality and exponential distribution of lifespans.
The universe divides time into short ticks. For each bee a die is rolled. The die has man | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an exponential distribution?
The question asks about the relationship between hourly mortality and exponential distribution of lifespans.
The universe divides time into short ticks. For each bee a die is rolled. The die has... | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an ex
The question asks about the relationship between hourly mortality and exponential distribution of lifespans.
The universe divides time into short ticks. For each bee a die is rolled. The die has man |
55,206 | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an exponential distribution? | Here's an approach
Get the histogram of the data
bl <- c(7.1, 2.3, 9.6, 25.8, 14.6, 12.8, 20.9, 30.0, 71.1, 36.9, 3.9, 18.2, 24.3, 55.8, 84.7,
13.6, 10.8, 18.2, 19.0, 34.8, 27.1, 54.2, 34.8, 65.8, 33.3, 26.5, 9.5, 44.2, 35.7, 2.3,
4.0, 25.9, 41.3)
b1h <- hist(bl, freq = FALSE, xlim = c(0, quantile(... | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an ex | Here's an approach
Get the histogram of the data
bl <- c(7.1, 2.3, 9.6, 25.8, 14.6, 12.8, 20.9, 30.0, 71.1, 36.9, 3.9, 18.2, 24.3, 55.8, 84.7,
13.6, 10.8, 18.2, 19.0, 34.8, 27.1, 54.2, 34.8, | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an exponential distribution?
Here's an approach
Get the histogram of the data
bl <- c(7.1, 2.3, 9.6, 25.8, 14.6, 12.8, 20.9, 30.0, 71.1, 36.9, 3.9, 18.2, 24.3, 55.8, 84.7,
13.6, 10.8, 18.2, 19.0, 34.8, 27.1, 54.2, 3... | R: Relationship between hourly mortality and lifespan given that the latter is approximated by an ex
Here's an approach
Get the histogram of the data
bl <- c(7.1, 2.3, 9.6, 25.8, 14.6, 12.8, 20.9, 30.0, 71.1, 36.9, 3.9, 18.2, 24.3, 55.8, 84.7,
13.6, 10.8, 18.2, 19.0, 34.8, 27.1, 54.2, 34.8, |
55,207 | Predict magnitude from angle in linear regression | Here, we want to predict a linear dependent variable from circular independent variables. There are several ways to approach this. The main thing to check is whether the relation between your dependent variable (let's say $Y$) and the circular predictor (say $\theta$) has a sinusoidal shape. This is often the case, but... | Predict magnitude from angle in linear regression | Here, we want to predict a linear dependent variable from circular independent variables. There are several ways to approach this. The main thing to check is whether the relation between your dependen | Predict magnitude from angle in linear regression
Here, we want to predict a linear dependent variable from circular independent variables. There are several ways to approach this. The main thing to check is whether the relation between your dependent variable (let's say $Y$) and the circular predictor (say $\theta$) h... | Predict magnitude from angle in linear regression
Here, we want to predict a linear dependent variable from circular independent variables. There are several ways to approach this. The main thing to check is whether the relation between your dependen |
55,208 | Predict magnitude from angle in linear regression | According to the title of your question, you want the linear regression. There is no linear function from circular argument (except the function $ f(x)=0 $ ). Therefore, you should use non-linear functions. I recommend to replace each circular variables $x$ by two variables $\sin x $ and $\cos x$ and use both for li... | Predict magnitude from angle in linear regression | According to the title of your question, you want the linear regression. There is no linear function from circular argument (except the function $ f(x)=0 $ ). Therefore, you should use non-linear fu | Predict magnitude from angle in linear regression
According to the title of your question, you want the linear regression. There is no linear function from circular argument (except the function $ f(x)=0 $ ). Therefore, you should use non-linear functions. I recommend to replace each circular variables $x$ by two va... | Predict magnitude from angle in linear regression
According to the title of your question, you want the linear regression. There is no linear function from circular argument (except the function $ f(x)=0 $ ). Therefore, you should use non-linear fu |
55,209 | How to build a function with the result of auto.arima in R? | You should use the following formula:
Y(t) = 0.3793*(Y(t-1) - 9132.46 - 22.0469*X(t-1)) + 9132.46 + 22.0469*X(t).
Example to replicate out-of-sample forecasts:
require(forecast)
set.seed(123)
n <- 100
xreg <- rnorm(n)
x <- arima.sim(n=n, model=list(ar=0.4)) + 2 + 0.8 * xreg
fit <- arima(x, order=c(1,0,0), include.mean... | How to build a function with the result of auto.arima in R? | You should use the following formula:
Y(t) = 0.3793*(Y(t-1) - 9132.46 - 22.0469*X(t-1)) + 9132.46 + 22.0469*X(t).
Example to replicate out-of-sample forecasts:
require(forecast)
set.seed(123)
n <- 10 | How to build a function with the result of auto.arima in R?
You should use the following formula:
Y(t) = 0.3793*(Y(t-1) - 9132.46 - 22.0469*X(t-1)) + 9132.46 + 22.0469*X(t).
Example to replicate out-of-sample forecasts:
require(forecast)
set.seed(123)
n <- 100
xreg <- rnorm(n)
x <- arima.sim(n=n, model=list(ar=0.4)) +... | How to build a function with the result of auto.arima in R?
You should use the following formula:
Y(t) = 0.3793*(Y(t-1) - 9132.46 - 22.0469*X(t-1)) + 9132.46 + 22.0469*X(t).
Example to replicate out-of-sample forecasts:
require(forecast)
set.seed(123)
n <- 10 |
55,210 | Logit Standard Error | For a binomial random variable $X\sim \text{Bin}(n,p)$, the sample proportion $k/n$ is a consistent estimator of the probability parameter $p$, $\hat p = k/n$. We then have the asymptotic normality result
$$\sqrt n (\hat p -p) \xrightarrow{d} N(0, p(1-p))$$
Applying the Delta Theorem,
$$\sqrt n (g(\hat p) -g(p)) \xrig... | Logit Standard Error | For a binomial random variable $X\sim \text{Bin}(n,p)$, the sample proportion $k/n$ is a consistent estimator of the probability parameter $p$, $\hat p = k/n$. We then have the asymptotic normality re | Logit Standard Error
For a binomial random variable $X\sim \text{Bin}(n,p)$, the sample proportion $k/n$ is a consistent estimator of the probability parameter $p$, $\hat p = k/n$. We then have the asymptotic normality result
$$\sqrt n (\hat p -p) \xrightarrow{d} N(0, p(1-p))$$
Applying the Delta Theorem,
$$\sqrt n (g... | Logit Standard Error
For a binomial random variable $X\sim \text{Bin}(n,p)$, the sample proportion $k/n$ is a consistent estimator of the probability parameter $p$, $\hat p = k/n$. We then have the asymptotic normality re |
55,211 | Logit Standard Error | $X\sim \text{Bin}(n,p)$
$p = X/n$
Since $p$ has a non-zero chance to be both 0 and 1, $E(\log(\frac{p}{1-p}))$, and also $\text{Var}(\log(\frac{p}{1-p}))$ are undefined.
If you want some other answer, you'll need to keep $p$ away from 0 and 1. | Logit Standard Error | $X\sim \text{Bin}(n,p)$
$p = X/n$
Since $p$ has a non-zero chance to be both 0 and 1, $E(\log(\frac{p}{1-p}))$, and also $\text{Var}(\log(\frac{p}{1-p}))$ are undefined.
If you want some other answer | Logit Standard Error
$X\sim \text{Bin}(n,p)$
$p = X/n$
Since $p$ has a non-zero chance to be both 0 and 1, $E(\log(\frac{p}{1-p}))$, and also $\text{Var}(\log(\frac{p}{1-p}))$ are undefined.
If you want some other answer, you'll need to keep $p$ away from 0 and 1. | Logit Standard Error
$X\sim \text{Bin}(n,p)$
$p = X/n$
Since $p$ has a non-zero chance to be both 0 and 1, $E(\log(\frac{p}{1-p}))$, and also $\text{Var}(\log(\frac{p}{1-p}))$ are undefined.
If you want some other answer |
55,212 | How to update a probability that a defendant is guilty after testimonies of multiple unreliable witnesses? | Instead of a tree, use a contingency table (which is the same thing, but lays out the calculations in a more convenient form). Instead of probabilities, perform the calculations in terms of odds.
Because the problem eventually concerns multiple witnesses, let's address the case where after $n-1$ witnesses have come f... | How to update a probability that a defendant is guilty after testimonies of multiple unreliable witn | Instead of a tree, use a contingency table (which is the same thing, but lays out the calculations in a more convenient form). Instead of probabilities, perform the calculations in terms of odds.
Be | How to update a probability that a defendant is guilty after testimonies of multiple unreliable witnesses?
Instead of a tree, use a contingency table (which is the same thing, but lays out the calculations in a more convenient form). Instead of probabilities, perform the calculations in terms of odds.
Because the pro... | How to update a probability that a defendant is guilty after testimonies of multiple unreliable witn
Instead of a tree, use a contingency table (which is the same thing, but lays out the calculations in a more convenient form). Instead of probabilities, perform the calculations in terms of odds.
Be |
55,213 | How to update a probability that a defendant is guilty after testimonies of multiple unreliable witnesses? | P(guilty) = 0.5
P(witness says guilty | guilty) = p (Witness is telling the truth)
P(witness says guilty | not guilty) = 1- p (Witness is telling a lie)
P(guilty and witness says guilty) = 0.5p
P(not guilty and witness says guilty) = 0.5(1-p)
P(witness says guilty) = 0.5[p + (1-p)] = 0.5
by bayes' rule
P(guilty... | How to update a probability that a defendant is guilty after testimonies of multiple unreliable witn | P(guilty) = 0.5
P(witness says guilty | guilty) = p (Witness is telling the truth)
P(witness says guilty | not guilty) = 1- p (Witness is telling a lie)
P(guilty and witness says guilty) = 0.5p
P | How to update a probability that a defendant is guilty after testimonies of multiple unreliable witnesses?
P(guilty) = 0.5
P(witness says guilty | guilty) = p (Witness is telling the truth)
P(witness says guilty | not guilty) = 1- p (Witness is telling a lie)
P(guilty and witness says guilty) = 0.5p
P(not guilty a... | How to update a probability that a defendant is guilty after testimonies of multiple unreliable witn
P(guilty) = 0.5
P(witness says guilty | guilty) = p (Witness is telling the truth)
P(witness says guilty | not guilty) = 1- p (Witness is telling a lie)
P(guilty and witness says guilty) = 0.5p
P |
55,214 | null hypothesis change | The form you are describing is commonly referred to as a one-tailed test. As $\alpha$ only now refers to a single tail of the distribution, the critical value will change. For example, on a Z test, the critical value for $\alpha = .05$ on a two-tailed test is $\approx 1.96$, but for a one tailed-test with the same al... | null hypothesis change | The form you are describing is commonly referred to as a one-tailed test. As $\alpha$ only now refers to a single tail of the distribution, the critical value will change. For example, on a Z test, | null hypothesis change
The form you are describing is commonly referred to as a one-tailed test. As $\alpha$ only now refers to a single tail of the distribution, the critical value will change. For example, on a Z test, the critical value for $\alpha = .05$ on a two-tailed test is $\approx 1.96$, but for a one taile... | null hypothesis change
The form you are describing is commonly referred to as a one-tailed test. As $\alpha$ only now refers to a single tail of the distribution, the critical value will change. For example, on a Z test, |
55,215 | What is meant by effective parameters in machine learning | Some models (here: linear regression) have parameters $\beta$:
$$ \hat{y} = \sum_{i\in\{1..p\}} \beta_i x_i + \beta_0 $$
For the same number of input features, more complex models (here: basis expansion to a quadratic model) have more parameters:
$$ \hat{y} = \sum_{i,j\in\{1..p\} \atop i\le j} \beta_{ij} x_ix_j + \sum_... | What is meant by effective parameters in machine learning | Some models (here: linear regression) have parameters $\beta$:
$$ \hat{y} = \sum_{i\in\{1..p\}} \beta_i x_i + \beta_0 $$
For the same number of input features, more complex models (here: basis expansi | What is meant by effective parameters in machine learning
Some models (here: linear regression) have parameters $\beta$:
$$ \hat{y} = \sum_{i\in\{1..p\}} \beta_i x_i + \beta_0 $$
For the same number of input features, more complex models (here: basis expansion to a quadratic model) have more parameters:
$$ \hat{y} = \s... | What is meant by effective parameters in machine learning
Some models (here: linear regression) have parameters $\beta$:
$$ \hat{y} = \sum_{i\in\{1..p\}} \beta_i x_i + \beta_0 $$
For the same number of input features, more complex models (here: basis expansi |
55,216 | What is meant by effective parameters in machine learning | "Effective parameters" can also be referred to as "effective degrees of freedom". In a linear model, we note that the leverages -- the amount the fitted value changes with the actual value $\partial \hat y_i \over \partial y_i $ -- can be added up to obtain the degrees of freedom of the model. This way of calculating d... | What is meant by effective parameters in machine learning | "Effective parameters" can also be referred to as "effective degrees of freedom". In a linear model, we note that the leverages -- the amount the fitted value changes with the actual value $\partial \ | What is meant by effective parameters in machine learning
"Effective parameters" can also be referred to as "effective degrees of freedom". In a linear model, we note that the leverages -- the amount the fitted value changes with the actual value $\partial \hat y_i \over \partial y_i $ -- can be added up to obtain the ... | What is meant by effective parameters in machine learning
"Effective parameters" can also be referred to as "effective degrees of freedom". In a linear model, we note that the leverages -- the amount the fitted value changes with the actual value $\partial \ |
55,217 | Formula for calculating sample size for hypergeometric distribution | First of all for background:
“The hypergeometric distribution applies to sampling without replacement from a finite population whose elements can be classified into two mutually exclusive categories like Pass/Fail” (Wikipedia)
That being said, if your sample size is extremely large it is possible that even without repl... | Formula for calculating sample size for hypergeometric distribution | First of all for background:
“The hypergeometric distribution applies to sampling without replacement from a finite population whose elements can be classified into two mutually exclusive categories l | Formula for calculating sample size for hypergeometric distribution
First of all for background:
“The hypergeometric distribution applies to sampling without replacement from a finite population whose elements can be classified into two mutually exclusive categories like Pass/Fail” (Wikipedia)
That being said, if your ... | Formula for calculating sample size for hypergeometric distribution
First of all for background:
“The hypergeometric distribution applies to sampling without replacement from a finite population whose elements can be classified into two mutually exclusive categories l |
55,218 | Formula for calculating sample size for hypergeometric distribution | Using the formula provided by Stan in his answer to his own question and plugging in the values for $N$, $p$ and $q$ in the question, i.e.
Population: $N = 1,000,000$
proportion of white marbles: $p = 0.001$
proportion of black marbles: $q = 1 - p$
and assuming a precision
$E = 0.05$
we end up with
$$
n = \frac{1,000... | Formula for calculating sample size for hypergeometric distribution | Using the formula provided by Stan in his answer to his own question and plugging in the values for $N$, $p$ and $q$ in the question, i.e.
Population: $N = 1,000,000$
proportion of white marbles: $p = | Formula for calculating sample size for hypergeometric distribution
Using the formula provided by Stan in his answer to his own question and plugging in the values for $N$, $p$ and $q$ in the question, i.e.
Population: $N = 1,000,000$
proportion of white marbles: $p = 0.001$
proportion of black marbles: $q = 1 - p$
an... | Formula for calculating sample size for hypergeometric distribution
Using the formula provided by Stan in his answer to his own question and plugging in the values for $N$, $p$ and $q$ in the question, i.e.
Population: $N = 1,000,000$
proportion of white marbles: $p = |
55,219 | Non-parametric version of paired t-test (Mann–Whitney U test) | The non-parametric analog of the paired $t$-test is the Wilcoxon. | Non-parametric version of paired t-test (Mann–Whitney U test) | The non-parametric analog of the paired $t$-test is the Wilcoxon. | Non-parametric version of paired t-test (Mann–Whitney U test)
The non-parametric analog of the paired $t$-test is the Wilcoxon. | Non-parametric version of paired t-test (Mann–Whitney U test)
The non-parametric analog of the paired $t$-test is the Wilcoxon. |
55,220 | Simulation of KS-test with estimated parameters | It is a Lilliefors test, and your first and third items are pretty close to how to do it.
The statement that Lilliefors test is only for the normal distribution is wrong. He did one for the exponential as well (you can even see that in the references at the bottom of the Wikipedia page you linked to), and the technique... | Simulation of KS-test with estimated parameters | It is a Lilliefors test, and your first and third items are pretty close to how to do it.
The statement that Lilliefors test is only for the normal distribution is wrong. He did one for the exponentia | Simulation of KS-test with estimated parameters
It is a Lilliefors test, and your first and third items are pretty close to how to do it.
The statement that Lilliefors test is only for the normal distribution is wrong. He did one for the exponential as well (you can even see that in the references at the bottom of the ... | Simulation of KS-test with estimated parameters
It is a Lilliefors test, and your first and third items are pretty close to how to do it.
The statement that Lilliefors test is only for the normal distribution is wrong. He did one for the exponentia |
55,221 | Fit measures for GMM Arellano-Bond estimator in R | If you are using the pgmm command from the package plm you do have good ways to assess model fit available to you (if this is not what you're using it's what you should be using). The standard way to test model fit would be to look at the $J$-test of overidentifying restrictions, also called the Sargan test (see here o... | Fit measures for GMM Arellano-Bond estimator in R | If you are using the pgmm command from the package plm you do have good ways to assess model fit available to you (if this is not what you're using it's what you should be using). The standard way to | Fit measures for GMM Arellano-Bond estimator in R
If you are using the pgmm command from the package plm you do have good ways to assess model fit available to you (if this is not what you're using it's what you should be using). The standard way to test model fit would be to look at the $J$-test of overidentifying res... | Fit measures for GMM Arellano-Bond estimator in R
If you are using the pgmm command from the package plm you do have good ways to assess model fit available to you (if this is not what you're using it's what you should be using). The standard way to |
55,222 | detect line in scatter | A Hough Transformation should help. It's mainly used for detecting lines is images, but (x,y) pairs can be considered a sparse representation of a bitmap image.
The idea is for each (x,y) point in the input, compute a list of (slope, intercept) pairs which represent "all" lines that pass through that point. "all" is co... | detect line in scatter | A Hough Transformation should help. It's mainly used for detecting lines is images, but (x,y) pairs can be considered a sparse representation of a bitmap image.
The idea is for each (x,y) point in the | detect line in scatter
A Hough Transformation should help. It's mainly used for detecting lines is images, but (x,y) pairs can be considered a sparse representation of a bitmap image.
The idea is for each (x,y) point in the input, compute a list of (slope, intercept) pairs which represent "all" lines that pass through ... | detect line in scatter
A Hough Transformation should help. It's mainly used for detecting lines is images, but (x,y) pairs can be considered a sparse representation of a bitmap image.
The idea is for each (x,y) point in the |
55,223 | LCA number of parameters & degrees of freedom | polca and mclust both performs Model-based cluster analysis, based on finite mixture models. However, polca is designed for Latent Class Analysis (LCA) which is the name for a particular class of mixture models suitable for categorical (polytomous) data. On the converse, mclust estimates Gaussian mixtures, so is suitab... | LCA number of parameters & degrees of freedom | polca and mclust both performs Model-based cluster analysis, based on finite mixture models. However, polca is designed for Latent Class Analysis (LCA) which is the name for a particular class of mixt | LCA number of parameters & degrees of freedom
polca and mclust both performs Model-based cluster analysis, based on finite mixture models. However, polca is designed for Latent Class Analysis (LCA) which is the name for a particular class of mixture models suitable for categorical (polytomous) data. On the converse, mc... | LCA number of parameters & degrees of freedom
polca and mclust both performs Model-based cluster analysis, based on finite mixture models. However, polca is designed for Latent Class Analysis (LCA) which is the name for a particular class of mixt |
55,224 | LCA number of parameters & degrees of freedom | As has been noted, poLCA only handles categorical data. So what does that mean for how it processes your data? From the help documentation for poLCA:
...Manifest variables must contain only integer values, and must be coded
with consecutive values from 1 to the maximum number of outcomes for
each variable.
That m... | LCA number of parameters & degrees of freedom | As has been noted, poLCA only handles categorical data. So what does that mean for how it processes your data? From the help documentation for poLCA:
...Manifest variables must contain only integer v | LCA number of parameters & degrees of freedom
As has been noted, poLCA only handles categorical data. So what does that mean for how it processes your data? From the help documentation for poLCA:
...Manifest variables must contain only integer values, and must be coded
with consecutive values from 1 to the maximum n... | LCA number of parameters & degrees of freedom
As has been noted, poLCA only handles categorical data. So what does that mean for how it processes your data? From the help documentation for poLCA:
...Manifest variables must contain only integer v |
55,225 | Studentized residual distribution | In my opinion there are two possible explanations here:
Externally studentized residuals are based on data with one observation deleted, this may account for the loss of the single degree of freedom.
There is inconsistency across books what $``k"$ actually refers to in a multiple regression model. If $k$ is the number... | Studentized residual distribution | In my opinion there are two possible explanations here:
Externally studentized residuals are based on data with one observation deleted, this may account for the loss of the single degree of freedom. | Studentized residual distribution
In my opinion there are two possible explanations here:
Externally studentized residuals are based on data with one observation deleted, this may account for the loss of the single degree of freedom.
There is inconsistency across books what $``k"$ actually refers to in a multiple regr... | Studentized residual distribution
In my opinion there are two possible explanations here:
Externally studentized residuals are based on data with one observation deleted, this may account for the loss of the single degree of freedom. |
55,226 | Assigning meaningful cluster name automatically | One technique for this is unsupervised multi-document keyword extraction. That is, extracting the most salient words and/or collocations for each of your clusters. A number of methods are available to do that, most of which are centered around either using graph centrality measures (think: Google's PageRank) or using l... | Assigning meaningful cluster name automatically | One technique for this is unsupervised multi-document keyword extraction. That is, extracting the most salient words and/or collocations for each of your clusters. A number of methods are available to | Assigning meaningful cluster name automatically
One technique for this is unsupervised multi-document keyword extraction. That is, extracting the most salient words and/or collocations for each of your clusters. A number of methods are available to do that, most of which are centered around either using graph centralit... | Assigning meaningful cluster name automatically
One technique for this is unsupervised multi-document keyword extraction. That is, extracting the most salient words and/or collocations for each of your clusters. A number of methods are available to |
55,227 | Assigning meaningful cluster name automatically | Yes it makes sense, however frequencies of single words may lead to trivial topics. You need to do some kind of normalisation by using TF-IDF and finding the most informative word(s) in the cluster (IDFs should be computed on the whole corpus). In other words you want the most frequent word(s) in the cluster that are i... | Assigning meaningful cluster name automatically | Yes it makes sense, however frequencies of single words may lead to trivial topics. You need to do some kind of normalisation by using TF-IDF and finding the most informative word(s) in the cluster (I | Assigning meaningful cluster name automatically
Yes it makes sense, however frequencies of single words may lead to trivial topics. You need to do some kind of normalisation by using TF-IDF and finding the most informative word(s) in the cluster (IDFs should be computed on the whole corpus). In other words you want the... | Assigning meaningful cluster name automatically
Yes it makes sense, however frequencies of single words may lead to trivial topics. You need to do some kind of normalisation by using TF-IDF and finding the most informative word(s) in the cluster (I |
55,228 | Compute pdf of a k-th order statistic | Preamble
As per the above comments, order statistics from non-identical distributions typically require complicated calculations, and generally yield complicated solutions, which makes them well-suited for solving with computer algebra systems. I am not aware that one can generally derive closed-form solutions as a fun... | Compute pdf of a k-th order statistic | Preamble
As per the above comments, order statistics from non-identical distributions typically require complicated calculations, and generally yield complicated solutions, which makes them well-suite | Compute pdf of a k-th order statistic
Preamble
As per the above comments, order statistics from non-identical distributions typically require complicated calculations, and generally yield complicated solutions, which makes them well-suited for solving with computer algebra systems. I am not aware that one can generally... | Compute pdf of a k-th order statistic
Preamble
As per the above comments, order statistics from non-identical distributions typically require complicated calculations, and generally yield complicated solutions, which makes them well-suite |
55,229 | How the hypergeometric distribution sums to 1? | The reference mentions that this identity is from combinatorics: that is, it counts things.
What does it count? Consider $N$ objects. Once and for all, divide those $n$ things into a group of $K$ of them, which I will call "red," and the remainder, which I will call "blue." Each subset of $n$ such objects determine... | How the hypergeometric distribution sums to 1? | The reference mentions that this identity is from combinatorics: that is, it counts things.
What does it count? Consider $N$ objects. Once and for all, divide those $n$ things into a group of $K$ o | How the hypergeometric distribution sums to 1?
The reference mentions that this identity is from combinatorics: that is, it counts things.
What does it count? Consider $N$ objects. Once and for all, divide those $n$ things into a group of $K$ of them, which I will call "red," and the remainder, which I will call "bl... | How the hypergeometric distribution sums to 1?
The reference mentions that this identity is from combinatorics: that is, it counts things.
What does it count? Consider $N$ objects. Once and for all, divide those $n$ things into a group of $K$ o |
55,230 | How the hypergeometric distribution sums to 1? | I don't think there is a problem here.
Note that $min(n,K)=n$ when $n\leq K$ and $min(n,K)=K$ when $K<n$. Whenever $n\leq K$, we will not have any problem as the upper bound in Vandermonde's identity is $n$. Also when $K<n$, we have: $k\leq K<n$. This is because $k$ is the number of successes and $K$ is the number ... | How the hypergeometric distribution sums to 1? | I don't think there is a problem here.
Note that $min(n,K)=n$ when $n\leq K$ and $min(n,K)=K$ when $K<n$. Whenever $n\leq K$, we will not have any problem as the upper bound in Vandermonde's ident | How the hypergeometric distribution sums to 1?
I don't think there is a problem here.
Note that $min(n,K)=n$ when $n\leq K$ and $min(n,K)=K$ when $K<n$. Whenever $n\leq K$, we will not have any problem as the upper bound in Vandermonde's identity is $n$. Also when $K<n$, we have: $k\leq K<n$. This is because $k$ is... | How the hypergeometric distribution sums to 1?
I don't think there is a problem here.
Note that $min(n,K)=n$ when $n\leq K$ and $min(n,K)=K$ when $K<n$. Whenever $n\leq K$, we will not have any problem as the upper bound in Vandermonde's ident |
55,231 | When interpreting interactions in a factorial ANOVA, is it necessary to look at the residual cell means? | While it looks generally wise to require closer investigation of the data, the authors overlooked important facts that weaken their argumentation. I'll show these fact on the illustrational examples they provide.
1) The first is about the possible interaction of child's sex and health status to "grief" (about the child... | When interpreting interactions in a factorial ANOVA, is it necessary to look at the residual cell me | While it looks generally wise to require closer investigation of the data, the authors overlooked important facts that weaken their argumentation. I'll show these fact on the illustrational examples t | When interpreting interactions in a factorial ANOVA, is it necessary to look at the residual cell means?
While it looks generally wise to require closer investigation of the data, the authors overlooked important facts that weaken their argumentation. I'll show these fact on the illustrational examples they provide.
1)... | When interpreting interactions in a factorial ANOVA, is it necessary to look at the residual cell me
While it looks generally wise to require closer investigation of the data, the authors overlooked important facts that weaken their argumentation. I'll show these fact on the illustrational examples t |
55,232 | When interpreting interactions in a factorial ANOVA, is it necessary to look at the residual cell means? | This will not be a full answer, but I'll say a few things.
I am suspicious of any advice that says that such and such is "absolutely necessary." While looking at interaction effects is one way to help understand an interaction, it is not the only possible way, nor may it be the best way, in some contexts.
I also tend t... | When interpreting interactions in a factorial ANOVA, is it necessary to look at the residual cell me | This will not be a full answer, but I'll say a few things.
I am suspicious of any advice that says that such and such is "absolutely necessary." While looking at interaction effects is one way to help | When interpreting interactions in a factorial ANOVA, is it necessary to look at the residual cell means?
This will not be a full answer, but I'll say a few things.
I am suspicious of any advice that says that such and such is "absolutely necessary." While looking at interaction effects is one way to help understand an ... | When interpreting interactions in a factorial ANOVA, is it necessary to look at the residual cell me
This will not be a full answer, but I'll say a few things.
I am suspicious of any advice that says that such and such is "absolutely necessary." While looking at interaction effects is one way to help |
55,233 | Sample covariance matrix | I believe there is a confusion of notation here. The paper uses $\mathbf I$ to denote the identity matrix, while you seem to use this symbol to denote a matrix where all its elements are equal to one, which the paper expresses using the notation $\mathbf1\mathbf1'$, $\mathbf1$ denoting a column vector of ones. (Moreove... | Sample covariance matrix | I believe there is a confusion of notation here. The paper uses $\mathbf I$ to denote the identity matrix, while you seem to use this symbol to denote a matrix where all its elements are equal to one, | Sample covariance matrix
I believe there is a confusion of notation here. The paper uses $\mathbf I$ to denote the identity matrix, while you seem to use this symbol to denote a matrix where all its elements are equal to one, which the paper expresses using the notation $\mathbf1\mathbf1'$, $\mathbf1$ denoting a column... | Sample covariance matrix
I believe there is a confusion of notation here. The paper uses $\mathbf I$ to denote the identity matrix, while you seem to use this symbol to denote a matrix where all its elements are equal to one, |
55,234 | Sample covariance matrix | The simplest way to understand the equation for S is by rearranging it as follows, using the expression for means vector $m=\frac{1}{T}X1$ given in the same paper:
$S=\frac{1}{T}\left(XX'-T\left(\frac{1}{T}X1\right)\left(1'X'\frac{1}{T}\right)\right)=\frac{1}{T}XX'-mm'$.
It should be obvious now how the equation for S... | Sample covariance matrix | The simplest way to understand the equation for S is by rearranging it as follows, using the expression for means vector $m=\frac{1}{T}X1$ given in the same paper:
$S=\frac{1}{T}\left(XX'-T\left(\frac | Sample covariance matrix
The simplest way to understand the equation for S is by rearranging it as follows, using the expression for means vector $m=\frac{1}{T}X1$ given in the same paper:
$S=\frac{1}{T}\left(XX'-T\left(\frac{1}{T}X1\right)\left(1'X'\frac{1}{T}\right)\right)=\frac{1}{T}XX'-mm'$.
It should be obvious n... | Sample covariance matrix
The simplest way to understand the equation for S is by rearranging it as follows, using the expression for means vector $m=\frac{1}{T}X1$ given in the same paper:
$S=\frac{1}{T}\left(XX'-T\left(\frac |
55,235 | Meaning of Bagged Random Forests? | As a colleague of the authors, I can address this question.
To directly answer the OP, @rapaio is correct: the top quotation means that the authors created 10 separate bags each with a random forest of 10 trees -- there will be 100 total trees.
As @rapaio mentioned, there's no clear cut reason why this performed bette... | Meaning of Bagged Random Forests? | As a colleague of the authors, I can address this question.
To directly answer the OP, @rapaio is correct: the top quotation means that the authors created 10 separate bags each with a random forest | Meaning of Bagged Random Forests?
As a colleague of the authors, I can address this question.
To directly answer the OP, @rapaio is correct: the top quotation means that the authors created 10 separate bags each with a random forest of 10 trees -- there will be 100 total trees.
As @rapaio mentioned, there's no clear c... | Meaning of Bagged Random Forests?
As a colleague of the authors, I can address this question.
To directly answer the OP, @rapaio is correct: the top quotation means that the authors created 10 separate bags each with a random forest |
55,236 | Meaning of Bagged Random Forests? | I would venture that it refers to regular random forests, but the author wants to bring out the distinction between a) the bagging / bootstrapping of the observations used for each tree and b) the random selection of a subset of the input parameters. not sure though. | Meaning of Bagged Random Forests? | I would venture that it refers to regular random forests, but the author wants to bring out the distinction between a) the bagging / bootstrapping of the observations used for each tree and b) the ran | Meaning of Bagged Random Forests?
I would venture that it refers to regular random forests, but the author wants to bring out the distinction between a) the bagging / bootstrapping of the observations used for each tree and b) the random selection of a subset of the input parameters. not sure though. | Meaning of Bagged Random Forests?
I would venture that it refers to regular random forests, but the author wants to bring out the distinction between a) the bagging / bootstrapping of the observations used for each tree and b) the ran |
55,237 | Meaning of Bagged Random Forests? | I tried to understand why bagging 10 random forests would work better than a random forest with 100 tress and I see no rational reason. I do not exclude that there could be some Weka implementation details.
However to answer your question I believe it is talking about a bagging ensemble with 10 bags and in each bag a r... | Meaning of Bagged Random Forests? | I tried to understand why bagging 10 random forests would work better than a random forest with 100 tress and I see no rational reason. I do not exclude that there could be some Weka implementation de | Meaning of Bagged Random Forests?
I tried to understand why bagging 10 random forests would work better than a random forest with 100 tress and I see no rational reason. I do not exclude that there could be some Weka implementation details.
However to answer your question I believe it is talking about a bagging ensembl... | Meaning of Bagged Random Forests?
I tried to understand why bagging 10 random forests would work better than a random forest with 100 tress and I see no rational reason. I do not exclude that there could be some Weka implementation de |
55,238 | Comparing different deep learning models? | Bengio's review of representation learining is probably as close as it gets. | Comparing different deep learning models? | Bengio's review of representation learining is probably as close as it gets. | Comparing different deep learning models?
Bengio's review of representation learining is probably as close as it gets. | Comparing different deep learning models?
Bengio's review of representation learining is probably as close as it gets. |
55,239 | Comparing different deep learning models? | Here is a good resource I use for comparison - their performance on different benchmark datasets.
This is an excellent site that does that has an ordered results of most of the noteworthy papers.
Take note of the "details" button on the right column, It gives a short description on their testing methodology, for exam... | Comparing different deep learning models? | Here is a good resource I use for comparison - their performance on different benchmark datasets.
This is an excellent site that does that has an ordered results of most of the noteworthy papers.
Ta | Comparing different deep learning models?
Here is a good resource I use for comparison - their performance on different benchmark datasets.
This is an excellent site that does that has an ordered results of most of the noteworthy papers.
Take note of the "details" button on the right column, It gives a short descript... | Comparing different deep learning models?
Here is a good resource I use for comparison - their performance on different benchmark datasets.
This is an excellent site that does that has an ordered results of most of the noteworthy papers.
Ta |
55,240 | Converting log odds coefficients to probabilities | The relation between odds & probabilities is non-linear, so a model with a constant odds ratio between males & females doesn't translate into one with a constant probability ratio (a.k.a. relative risk) between males & females—the latter depends on the intercept & values of other predictors. And you apply the inverse l... | Converting log odds coefficients to probabilities | The relation between odds & probabilities is non-linear, so a model with a constant odds ratio between males & females doesn't translate into one with a constant probability ratio (a.k.a. relative ris | Converting log odds coefficients to probabilities
The relation between odds & probabilities is non-linear, so a model with a constant odds ratio between males & females doesn't translate into one with a constant probability ratio (a.k.a. relative risk) between males & females—the latter depends on the intercept & value... | Converting log odds coefficients to probabilities
The relation between odds & probabilities is non-linear, so a model with a constant odds ratio between males & females doesn't translate into one with a constant probability ratio (a.k.a. relative ris |
55,241 | Interpreting regression coefficients of log(y+1) transformed responses | An alternative to thinking in terms of a GM is semi-elasticity.
Your model for the expected value is something like $$E[\ln(y+1) \vert x]= \alpha + \beta \cdot x + \gamma \cdot z$$
Taking the derivative of that with respect to $x$, you get $$\frac{\partial E[\ln(y+1) \vert x]}{\partial x}= \frac{1}{y+1}\cdot \frac{\par... | Interpreting regression coefficients of log(y+1) transformed responses | An alternative to thinking in terms of a GM is semi-elasticity.
Your model for the expected value is something like $$E[\ln(y+1) \vert x]= \alpha + \beta \cdot x + \gamma \cdot z$$
Taking the derivati | Interpreting regression coefficients of log(y+1) transformed responses
An alternative to thinking in terms of a GM is semi-elasticity.
Your model for the expected value is something like $$E[\ln(y+1) \vert x]= \alpha + \beta \cdot x + \gamma \cdot z$$
Taking the derivative of that with respect to $x$, you get $$\frac{\... | Interpreting regression coefficients of log(y+1) transformed responses
An alternative to thinking in terms of a GM is semi-elasticity.
Your model for the expected value is something like $$E[\ln(y+1) \vert x]= \alpha + \beta \cdot x + \gamma \cdot z$$
Taking the derivati |
55,242 | Interpreting regression coefficients of log(y+1) transformed responses | Disclaimer: I would highly advise against fitting log(y+1) in order to prevent the logarithm of negative values. This might be commonly used, but that alone does not yet make it a good practice. In many cases there are better techniques. In the regression case one can use GLM or non-linear regression.
We can estimate i... | Interpreting regression coefficients of log(y+1) transformed responses | Disclaimer: I would highly advise against fitting log(y+1) in order to prevent the logarithm of negative values. This might be commonly used, but that alone does not yet make it a good practice. In ma | Interpreting regression coefficients of log(y+1) transformed responses
Disclaimer: I would highly advise against fitting log(y+1) in order to prevent the logarithm of negative values. This might be commonly used, but that alone does not yet make it a good practice. In many cases there are better techniques. In the regr... | Interpreting regression coefficients of log(y+1) transformed responses
Disclaimer: I would highly advise against fitting log(y+1) in order to prevent the logarithm of negative values. This might be commonly used, but that alone does not yet make it a good practice. In ma |
55,243 | GEE iteration process | You assume:
a link function $g(\mu_{ij})=\mathbf{x}_{ij}'\boldsymbol{\beta}$;
the conditional variance of each $y_{ij}$, $\text{Var}(y_{ij}\mid\mathbf{x}_{ij})=\phi v(\mu_{ij})$;
the pairwise within-subject association, $\mathbf{V}_i=\mathbf{A}^{1/2}_i\mathbf{C}_i\mathbf{A}^{1/2}_i$, where $\mathbf{A}_i=\text{diag}[\t... | GEE iteration process | You assume:
a link function $g(\mu_{ij})=\mathbf{x}_{ij}'\boldsymbol{\beta}$;
the conditional variance of each $y_{ij}$, $\text{Var}(y_{ij}\mid\mathbf{x}_{ij})=\phi v(\mu_{ij})$;
the pairwise within- | GEE iteration process
You assume:
a link function $g(\mu_{ij})=\mathbf{x}_{ij}'\boldsymbol{\beta}$;
the conditional variance of each $y_{ij}$, $\text{Var}(y_{ij}\mid\mathbf{x}_{ij})=\phi v(\mu_{ij})$;
the pairwise within-subject association, $\mathbf{V}_i=\mathbf{A}^{1/2}_i\mathbf{C}_i\mathbf{A}^{1/2}_i$, where $\math... | GEE iteration process
You assume:
a link function $g(\mu_{ij})=\mathbf{x}_{ij}'\boldsymbol{\beta}$;
the conditional variance of each $y_{ij}$, $\text{Var}(y_{ij}\mid\mathbf{x}_{ij})=\phi v(\mu_{ij})$;
the pairwise within- |
55,244 | Remove effect of a factor on continuous proportion data using regression in R | The big issue, I think, is that you need a model of the data to work with. These could be regression models or simply your data fit to a distribution. Your data could theoretically be consistent with a number of models / distributions (such as the normal distribution, proportions of successes arising from a binomial ... | Remove effect of a factor on continuous proportion data using regression in R | The big issue, I think, is that you need a model of the data to work with. These could be regression models or simply your data fit to a distribution. Your data could theoretically be consistent wit | Remove effect of a factor on continuous proportion data using regression in R
The big issue, I think, is that you need a model of the data to work with. These could be regression models or simply your data fit to a distribution. Your data could theoretically be consistent with a number of models / distributions (such... | Remove effect of a factor on continuous proportion data using regression in R
The big issue, I think, is that you need a model of the data to work with. These could be regression models or simply your data fit to a distribution. Your data could theoretically be consistent wit |
55,245 | Why we shouldn't be obsessed with unbiasedness | Some people are annoyingly obsessed with unbiasedness. Bias and dispersion are measures of uncertainty which roughly correspond to accuracy and precision. You usually have a trade-off between accuracy and precision, some estimators may be more precise but less accurate and vice versa.
MSE is a sum of bias and the varia... | Why we shouldn't be obsessed with unbiasedness | Some people are annoyingly obsessed with unbiasedness. Bias and dispersion are measures of uncertainty which roughly correspond to accuracy and precision. You usually have a trade-off between accuracy | Why we shouldn't be obsessed with unbiasedness
Some people are annoyingly obsessed with unbiasedness. Bias and dispersion are measures of uncertainty which roughly correspond to accuracy and precision. You usually have a trade-off between accuracy and precision, some estimators may be more precise but less accurate and... | Why we shouldn't be obsessed with unbiasedness
Some people are annoyingly obsessed with unbiasedness. Bias and dispersion are measures of uncertainty which roughly correspond to accuracy and precision. You usually have a trade-off between accuracy |
55,246 | Why we shouldn't be obsessed with unbiasedness | The motivation of the statement is a rejection of some very important work from Rao-Blackwell about the provision of so called UMVUEs (Uniform minimum variance unbiased estimators). This showed that there is a lower bound on variance of unbiased estimators, and if you achieve that, you usually get a very good estimator... | Why we shouldn't be obsessed with unbiasedness | The motivation of the statement is a rejection of some very important work from Rao-Blackwell about the provision of so called UMVUEs (Uniform minimum variance unbiased estimators). This showed that t | Why we shouldn't be obsessed with unbiasedness
The motivation of the statement is a rejection of some very important work from Rao-Blackwell about the provision of so called UMVUEs (Uniform minimum variance unbiased estimators). This showed that there is a lower bound on variance of unbiased estimators, and if you achi... | Why we shouldn't be obsessed with unbiasedness
The motivation of the statement is a rejection of some very important work from Rao-Blackwell about the provision of so called UMVUEs (Uniform minimum variance unbiased estimators). This showed that t |
55,247 | Data mining techniques in R for advertising and sales data | Given that you have a time series, with possible influences of trend and seasonality on sales, I recommend that you look for time series techniques that can handle causal effects such as advertising. This thread should be a good starting point, although your focus appears not to be forecasting.
Try something like this:... | Data mining techniques in R for advertising and sales data | Given that you have a time series, with possible influences of trend and seasonality on sales, I recommend that you look for time series techniques that can handle causal effects such as advertising. | Data mining techniques in R for advertising and sales data
Given that you have a time series, with possible influences of trend and seasonality on sales, I recommend that you look for time series techniques that can handle causal effects such as advertising. This thread should be a good starting point, although your fo... | Data mining techniques in R for advertising and sales data
Given that you have a time series, with possible influences of trend and seasonality on sales, I recommend that you look for time series techniques that can handle causal effects such as advertising. |
55,248 | Data mining techniques in R for advertising and sales data | Keeping models as simple as possible(but not too simple) is very important. There is absolutely no proof that one should incorporate seasonal differencing into a reasonable model for your data. Some analysts believe that complicated models will yield higher consulting fees. Differencing is a form of a transformation(co... | Data mining techniques in R for advertising and sales data | Keeping models as simple as possible(but not too simple) is very important. There is absolutely no proof that one should incorporate seasonal differencing into a reasonable model for your data. Some a | Data mining techniques in R for advertising and sales data
Keeping models as simple as possible(but not too simple) is very important. There is absolutely no proof that one should incorporate seasonal differencing into a reasonable model for your data. Some analysts believe that complicated models will yield higher con... | Data mining techniques in R for advertising and sales data
Keeping models as simple as possible(but not too simple) is very important. There is absolutely no proof that one should incorporate seasonal differencing into a reasonable model for your data. Some a |
55,249 | Weighted Linear Regression R | Ordinary least squares minimizes the sum of squared residuals (residual = measured value - fitted value). Weighted least squares weights the sqared residuals. From help("lm"):
weighted least squares is used with weights weights (that is,
minimizing sum(w*e^2)) | Weighted Linear Regression R | Ordinary least squares minimizes the sum of squared residuals (residual = measured value - fitted value). Weighted least squares weights the sqared residuals. From help("lm"):
weighted least squares | Weighted Linear Regression R
Ordinary least squares minimizes the sum of squared residuals (residual = measured value - fitted value). Weighted least squares weights the sqared residuals. From help("lm"):
weighted least squares is used with weights weights (that is,
minimizing sum(w*e^2)) | Weighted Linear Regression R
Ordinary least squares minimizes the sum of squared residuals (residual = measured value - fitted value). Weighted least squares weights the sqared residuals. From help("lm"):
weighted least squares |
55,250 | Weighted Linear Regression R | In simple terms, it means that each data point is assigned a weight which either increases or decreases the influence of that data point on the final model. Thus the slope and intercept change as the raw data is not used, but rather the weighted data.
If you want more detailed information, I'd suggest stats.stackexcha... | Weighted Linear Regression R | In simple terms, it means that each data point is assigned a weight which either increases or decreases the influence of that data point on the final model. Thus the slope and intercept change as the | Weighted Linear Regression R
In simple terms, it means that each data point is assigned a weight which either increases or decreases the influence of that data point on the final model. Thus the slope and intercept change as the raw data is not used, but rather the weighted data.
If you want more detailed information,... | Weighted Linear Regression R
In simple terms, it means that each data point is assigned a weight which either increases or decreases the influence of that data point on the final model. Thus the slope and intercept change as the |
55,251 | Analysis of the Residuals vs Fitted | Note that within each diagonal band, the residual decreases by one unit for every one unit the fitted value increases. This looks to me like the response for a given subject remains relatively constant and the predictors change a little bit between observations. Thus when the change in predictors predict a unit increas... | Analysis of the Residuals vs Fitted | Note that within each diagonal band, the residual decreases by one unit for every one unit the fitted value increases. This looks to me like the response for a given subject remains relatively constan | Analysis of the Residuals vs Fitted
Note that within each diagonal band, the residual decreases by one unit for every one unit the fitted value increases. This looks to me like the response for a given subject remains relatively constant and the predictors change a little bit between observations. Thus when the change ... | Analysis of the Residuals vs Fitted
Note that within each diagonal band, the residual decreases by one unit for every one unit the fitted value increases. This looks to me like the response for a given subject remains relatively constan |
55,252 | Sample from a custom continuous distribution in R | If you want to sample from a certain pdf, you can use
rejection sampling which requires nothing more than the density function and the specification of a value as upper bound which is at least as large as the largest value of the density function. The disadvantage is that it can eventually be a very inefficient way to ... | Sample from a custom continuous distribution in R | If you want to sample from a certain pdf, you can use
rejection sampling which requires nothing more than the density function and the specification of a value as upper bound which is at least as larg | Sample from a custom continuous distribution in R
If you want to sample from a certain pdf, you can use
rejection sampling which requires nothing more than the density function and the specification of a value as upper bound which is at least as large as the largest value of the density function. The disadvantage is th... | Sample from a custom continuous distribution in R
If you want to sample from a certain pdf, you can use
rejection sampling which requires nothing more than the density function and the specification of a value as upper bound which is at least as larg |
55,253 | Sample from a custom continuous distribution in R | There are some posts on the internet about rejection sampling, but I found this one to be the most helpful. My example is from there, with minor modifications. If you need to speed things up a bit more, you could use data.table package which can make you feel dizzy from speed gains. I didn't bother because this particu... | Sample from a custom continuous distribution in R | There are some posts on the internet about rejection sampling, but I found this one to be the most helpful. My example is from there, with minor modifications. If you need to speed things up a bit mor | Sample from a custom continuous distribution in R
There are some posts on the internet about rejection sampling, but I found this one to be the most helpful. My example is from there, with minor modifications. If you need to speed things up a bit more, you could use data.table package which can make you feel dizzy from... | Sample from a custom continuous distribution in R
There are some posts on the internet about rejection sampling, but I found this one to be the most helpful. My example is from there, with minor modifications. If you need to speed things up a bit mor |
55,254 | Regression model for proportion or count when counts of outcome and total events are often zero | Probably the most common way to look at this kind of thing, if you're only interested in the proportions, is to assume that at the $i$th location $A_i$ & $B_i$ are independent Poisson variables with rates $\lambda_i$ & $\mu_i$ respectively. (That doesn't seem unreasonable for two types of car crashes at the same locati... | Regression model for proportion or count when counts of outcome and total events are often zero | Probably the most common way to look at this kind of thing, if you're only interested in the proportions, is to assume that at the $i$th location $A_i$ & $B_i$ are independent Poisson variables with r | Regression model for proportion or count when counts of outcome and total events are often zero
Probably the most common way to look at this kind of thing, if you're only interested in the proportions, is to assume that at the $i$th location $A_i$ & $B_i$ are independent Poisson variables with rates $\lambda_i$ & $\mu_... | Regression model for proportion or count when counts of outcome and total events are often zero
Probably the most common way to look at this kind of thing, if you're only interested in the proportions, is to assume that at the $i$th location $A_i$ & $B_i$ are independent Poisson variables with r |
55,255 | Regression model for proportion or count when counts of outcome and total events are often zero | I may be missing something about your motivation to 'control' for the total number of events, but how about the following:
Concentrate on modelling the rate (not the count) of A per area. To do this you would need to control for different numbers of total events in each area. You'd do this by adding an offset of $\lo... | Regression model for proportion or count when counts of outcome and total events are often zero | I may be missing something about your motivation to 'control' for the total number of events, but how about the following:
Concentrate on modelling the rate (not the count) of A per area. To do this | Regression model for proportion or count when counts of outcome and total events are often zero
I may be missing something about your motivation to 'control' for the total number of events, but how about the following:
Concentrate on modelling the rate (not the count) of A per area. To do this you would need to contr... | Regression model for proportion or count when counts of outcome and total events are often zero
I may be missing something about your motivation to 'control' for the total number of events, but how about the following:
Concentrate on modelling the rate (not the count) of A per area. To do this |
55,256 | Regression model for proportion or count when counts of outcome and total events are often zero | Have you thought about using Tukey's folded logs, as in $$\frac{1}{2} \cdot \ln \left( \frac{A + 1/6}{A+B + 1/3}\right) - \frac{1}{2} \cdot \ln \left(1 - \frac{A + 1/6}{A+B + 1/3}\right)$$
You can justify this type of transformation with some Bayesian arguments. For example, here's some date illustrating this transform... | Regression model for proportion or count when counts of outcome and total events are often zero | Have you thought about using Tukey's folded logs, as in $$\frac{1}{2} \cdot \ln \left( \frac{A + 1/6}{A+B + 1/3}\right) - \frac{1}{2} \cdot \ln \left(1 - \frac{A + 1/6}{A+B + 1/3}\right)$$
You can jus | Regression model for proportion or count when counts of outcome and total events are often zero
Have you thought about using Tukey's folded logs, as in $$\frac{1}{2} \cdot \ln \left( \frac{A + 1/6}{A+B + 1/3}\right) - \frac{1}{2} \cdot \ln \left(1 - \frac{A + 1/6}{A+B + 1/3}\right)$$
You can justify this type of transf... | Regression model for proportion or count when counts of outcome and total events are often zero
Have you thought about using Tukey's folded logs, as in $$\frac{1}{2} \cdot \ln \left( \frac{A + 1/6}{A+B + 1/3}\right) - \frac{1}{2} \cdot \ln \left(1 - \frac{A + 1/6}{A+B + 1/3}\right)$$
You can jus |
55,257 | Why is a symmetric distribution sufficient for the sample mean and variance to be uncorrelated? | Too long for a comment:
It is not true that sample mean and variance are always independent if the distribution is symmetric. For example, take a sample from a distribution which takes values $\pm1$ with equal probability: if the sample mean is $\pm1$ then the sample variance will be $0$, while if the sample mean is ... | Why is a symmetric distribution sufficient for the sample mean and variance to be uncorrelated? | Too long for a comment:
It is not true that sample mean and variance are always independent if the distribution is symmetric. For example, take a sample from a distribution which takes values $\pm1$ | Why is a symmetric distribution sufficient for the sample mean and variance to be uncorrelated?
Too long for a comment:
It is not true that sample mean and variance are always independent if the distribution is symmetric. For example, take a sample from a distribution which takes values $\pm1$ with equal probability:... | Why is a symmetric distribution sufficient for the sample mean and variance to be uncorrelated?
Too long for a comment:
It is not true that sample mean and variance are always independent if the distribution is symmetric. For example, take a sample from a distribution which takes values $\pm1$ |
55,258 | A seeming paradox with rational agents not coming to the same conclusion given the same data | I am afraid that you fell victim to the usual misinterpretation of the (essentially vacuous) dictum "rational agents given the same information must come to the same conclusion". "Information" in this context is not just "data". It includes also the information-processing procedures a rational agent will use. Also, it ... | A seeming paradox with rational agents not coming to the same conclusion given the same data | I am afraid that you fell victim to the usual misinterpretation of the (essentially vacuous) dictum "rational agents given the same information must come to the same conclusion". "Information" in this | A seeming paradox with rational agents not coming to the same conclusion given the same data
I am afraid that you fell victim to the usual misinterpretation of the (essentially vacuous) dictum "rational agents given the same information must come to the same conclusion". "Information" in this context is not just "data"... | A seeming paradox with rational agents not coming to the same conclusion given the same data
I am afraid that you fell victim to the usual misinterpretation of the (essentially vacuous) dictum "rational agents given the same information must come to the same conclusion". "Information" in this |
55,259 | A seeming paradox with rational agents not coming to the same conclusion given the same data | Let me first point out that you appear to have a common misunderstanding about the meaning of p-values. In conventional (frequentist) statistical analysis, the p-value is the probability of getting a sample statistic (say a sample mean) as far or further from the proposed null value as yours, if the null value is the ... | A seeming paradox with rational agents not coming to the same conclusion given the same data | Let me first point out that you appear to have a common misunderstanding about the meaning of p-values. In conventional (frequentist) statistical analysis, the p-value is the probability of getting a | A seeming paradox with rational agents not coming to the same conclusion given the same data
Let me first point out that you appear to have a common misunderstanding about the meaning of p-values. In conventional (frequentist) statistical analysis, the p-value is the probability of getting a sample statistic (say a sa... | A seeming paradox with rational agents not coming to the same conclusion given the same data
Let me first point out that you appear to have a common misunderstanding about the meaning of p-values. In conventional (frequentist) statistical analysis, the p-value is the probability of getting a |
55,260 | A seeming paradox with rational agents not coming to the same conclusion given the same data | The reason that your question appears to be both difficult and strange is that conventional accounts of statistics do not include the philosophy of statistics. In particular for your question we need to consider two normative principles. It is the likelihood principle that suggests that identical evidence should lead t... | A seeming paradox with rational agents not coming to the same conclusion given the same data | The reason that your question appears to be both difficult and strange is that conventional accounts of statistics do not include the philosophy of statistics. In particular for your question we need | A seeming paradox with rational agents not coming to the same conclusion given the same data
The reason that your question appears to be both difficult and strange is that conventional accounts of statistics do not include the philosophy of statistics. In particular for your question we need to consider two normative p... | A seeming paradox with rational agents not coming to the same conclusion given the same data
The reason that your question appears to be both difficult and strange is that conventional accounts of statistics do not include the philosophy of statistics. In particular for your question we need |
55,261 | To know clearly about the population | They can be confusing. However attempts were made to define them:
Two Types of Population in Research
Target Population
Target population refers to the ENTIRE group of individuals or objects
to which researchers are interested in generalizing the conclusions.
The target population usually has varying characteristi... | To know clearly about the population | They can be confusing. However attempts were made to define them:
Two Types of Population in Research
Target Population
Target population refers to the ENTIRE group of individuals or objects
to whi | To know clearly about the population
They can be confusing. However attempts were made to define them:
Two Types of Population in Research
Target Population
Target population refers to the ENTIRE group of individuals or objects
to which researchers are interested in generalizing the conclusions.
The target populat... | To know clearly about the population
They can be confusing. However attempts were made to define them:
Two Types of Population in Research
Target Population
Target population refers to the ENTIRE group of individuals or objects
to whi |
55,262 | To know clearly about the population | Easily speaking:
Target population is the population you are interested in your study;
Study population is a sub population that you are taking from the target population for doing your study
Theoretical population is the same as target population, which is the population you want your study to be generalized to.
For... | To know clearly about the population | Easily speaking:
Target population is the population you are interested in your study;
Study population is a sub population that you are taking from the target population for doing your study
Theoret | To know clearly about the population
Easily speaking:
Target population is the population you are interested in your study;
Study population is a sub population that you are taking from the target population for doing your study
Theoretical population is the same as target population, which is the population you want ... | To know clearly about the population
Easily speaking:
Target population is the population you are interested in your study;
Study population is a sub population that you are taking from the target population for doing your study
Theoret |
55,263 | To know clearly about the population | Target population, as the name suggests, is the population that is of interest to the researcher. Also known as the theoretical population, it serves as the main environment for the researcher's hypothesis in a general term.
For instance, one may be thinking about doing a research on the criteria for recruiting high s... | To know clearly about the population | Target population, as the name suggests, is the population that is of interest to the researcher. Also known as the theoretical population, it serves as the main environment for the researcher's hypot | To know clearly about the population
Target population, as the name suggests, is the population that is of interest to the researcher. Also known as the theoretical population, it serves as the main environment for the researcher's hypothesis in a general term.
For instance, one may be thinking about doing a research ... | To know clearly about the population
Target population, as the name suggests, is the population that is of interest to the researcher. Also known as the theoretical population, it serves as the main environment for the researcher's hypot |
55,264 | Variance within each cluster | According to the Hastie equation 14.31 (see also Halkidi et al. 2001), the within-cluster variance $W(C_{k})$ of a cluster $C_{k}$ is defined (for the Euclidean distance) as $\sum_{x_{i}\in{C_{k}}}\|x_{i}-\bar{x}_{k}\|^2$ , where $\bar{x}_{k}$ is the mean of cluster $C_{k}$ (also called the cluster centroid, its values... | Variance within each cluster | According to the Hastie equation 14.31 (see also Halkidi et al. 2001), the within-cluster variance $W(C_{k})$ of a cluster $C_{k}$ is defined (for the Euclidean distance) as $\sum_{x_{i}\in{C_{k}}}\|x | Variance within each cluster
According to the Hastie equation 14.31 (see also Halkidi et al. 2001), the within-cluster variance $W(C_{k})$ of a cluster $C_{k}$ is defined (for the Euclidean distance) as $\sum_{x_{i}\in{C_{k}}}\|x_{i}-\bar{x}_{k}\|^2$ , where $\bar{x}_{k}$ is the mean of cluster $C_{k}$ (also called the... | Variance within each cluster
According to the Hastie equation 14.31 (see also Halkidi et al. 2001), the within-cluster variance $W(C_{k})$ of a cluster $C_{k}$ is defined (for the Euclidean distance) as $\sum_{x_{i}\in{C_{k}}}\|x |
55,265 | Compare means of two datasets of binary data | You can express your data in the form of a contingency table.
For a small N you can use Fisher's exact test to test whether your measurements a and b are dependent on each other.
For a larger N you can use the chi-squared test | Compare means of two datasets of binary data | You can express your data in the form of a contingency table.
For a small N you can use Fisher's exact test to test whether your measurements a and b are dependent on each other.
For a larger N you c | Compare means of two datasets of binary data
You can express your data in the form of a contingency table.
For a small N you can use Fisher's exact test to test whether your measurements a and b are dependent on each other.
For a larger N you can use the chi-squared test | Compare means of two datasets of binary data
You can express your data in the form of a contingency table.
For a small N you can use Fisher's exact test to test whether your measurements a and b are dependent on each other.
For a larger N you c |
55,266 | Compare means of two datasets of binary data | Since in your case means and proportions are the same
you can use proportions test for testing the null that
the proportions (probabilities of success) in groups
are the same.
As for reference I can suggest the
Hollander and Wolfe's book Nonparametric Statistical Methods | Compare means of two datasets of binary data | Since in your case means and proportions are the same
you can use proportions test for testing the null that
the proportions (probabilities of success) in groups
are the same.
As for reference I ca | Compare means of two datasets of binary data
Since in your case means and proportions are the same
you can use proportions test for testing the null that
the proportions (probabilities of success) in groups
are the same.
As for reference I can suggest the
Hollander and Wolfe's book Nonparametric Statistical Methods | Compare means of two datasets of binary data
Since in your case means and proportions are the same
you can use proportions test for testing the null that
the proportions (probabilities of success) in groups
are the same.
As for reference I ca |
55,267 | Conditional Expectations and Variances | The conditional version of the law of total variance works just the same.
Expectation follows trivially from the tower property, and for variance:
\begin{align}
\mathbb{V} [X | Y] &= \mathbb{E}[(X - E[X | Y])^2 | Y] \\
&= \mathbb{E}[X^2 | Y] - (\mathbb{E}[X | Y])^2 \\
&= \mathbb{E}[\mathbb{E}[X^2 | Y,Z] | Y] - (\mathbb... | Conditional Expectations and Variances | The conditional version of the law of total variance works just the same.
Expectation follows trivially from the tower property, and for variance:
\begin{align}
\mathbb{V} [X | Y] &= \mathbb{E}[(X - E | Conditional Expectations and Variances
The conditional version of the law of total variance works just the same.
Expectation follows trivially from the tower property, and for variance:
\begin{align}
\mathbb{V} [X | Y] &= \mathbb{E}[(X - E[X | Y])^2 | Y] \\
&= \mathbb{E}[X^2 | Y] - (\mathbb{E}[X | Y])^2 \\
&= \mathbb{E... | Conditional Expectations and Variances
The conditional version of the law of total variance works just the same.
Expectation follows trivially from the tower property, and for variance:
\begin{align}
\mathbb{V} [X | Y] &= \mathbb{E}[(X - E |
55,268 | If C wins B 80% of the time, and B wins A 80% of the time, how often would C beat A? | this information is not enough. Let's look more precisely what I mean by the lack of information. An event $CA$ means that $C$ wins against $A$ and event $\overline{CA}$ for when $C$ looses against $A$. Then we have:
\begin{equation}
p(CB) = p(CB|CA)p(CA) + p(CB|\overline{CA})p(\overline{CA})
\end{equation}
and followi... | If C wins B 80% of the time, and B wins A 80% of the time, how often would C beat A? | this information is not enough. Let's look more precisely what I mean by the lack of information. An event $CA$ means that $C$ wins against $A$ and event $\overline{CA}$ for when $C$ looses against $A | If C wins B 80% of the time, and B wins A 80% of the time, how often would C beat A?
this information is not enough. Let's look more precisely what I mean by the lack of information. An event $CA$ means that $C$ wins against $A$ and event $\overline{CA}$ for when $C$ looses against $A$. Then we have:
\begin{equation}
p... | If C wins B 80% of the time, and B wins A 80% of the time, how often would C beat A?
this information is not enough. Let's look more precisely what I mean by the lack of information. An event $CA$ means that $C$ wins against $A$ and event $\overline{CA}$ for when $C$ looses against $A |
55,269 | If C wins B 80% of the time, and B wins A 80% of the time, how often would C beat A? | In the Elo model, C would be over one class interval (+240) stronger. B the average player (=0) and A over one class interval weaker -(240). The Elo model predicts a win for C against A with 95% probability (rating diff = 480).
See here for the rating probabilities: https://www.fide.com/docs/regulations/FIDE%20Rating%2... | If C wins B 80% of the time, and B wins A 80% of the time, how often would C beat A? | In the Elo model, C would be over one class interval (+240) stronger. B the average player (=0) and A over one class interval weaker -(240). The Elo model predicts a win for C against A with 95% proba | If C wins B 80% of the time, and B wins A 80% of the time, how often would C beat A?
In the Elo model, C would be over one class interval (+240) stronger. B the average player (=0) and A over one class interval weaker -(240). The Elo model predicts a win for C against A with 95% probability (rating diff = 480).
See her... | If C wins B 80% of the time, and B wins A 80% of the time, how often would C beat A?
In the Elo model, C would be over one class interval (+240) stronger. B the average player (=0) and A over one class interval weaker -(240). The Elo model predicts a win for C against A with 95% proba |
55,270 | Cluster Analysis in R | You're trying to measure the Euclidean distance of categories. Euclidean distance is the "normal" distance on numbers: the Euclidean distance of 7 and 10 is 3, the euclidean distance of -1 and 1 is 2.
If you give your categories numbers, then you'll calculate the distances between these numbers - but will they make se... | Cluster Analysis in R | You're trying to measure the Euclidean distance of categories. Euclidean distance is the "normal" distance on numbers: the Euclidean distance of 7 and 10 is 3, the euclidean distance of -1 and 1 is 2 | Cluster Analysis in R
You're trying to measure the Euclidean distance of categories. Euclidean distance is the "normal" distance on numbers: the Euclidean distance of 7 and 10 is 3, the euclidean distance of -1 and 1 is 2.
If you give your categories numbers, then you'll calculate the distances between these numbers -... | Cluster Analysis in R
You're trying to measure the Euclidean distance of categories. Euclidean distance is the "normal" distance on numbers: the Euclidean distance of 7 and 10 is 3, the euclidean distance of -1 and 1 is 2 |
55,271 | Cluster Analysis in R | If you want to see the similarities within your data across different dimensions, you might want to use some descriptive plots such as boxplots for numeric data, barplots for counts of your categoric data, crosstabs and if required, stacked boxplots (boxplots where you compare some numeric variable, split according one... | Cluster Analysis in R | If you want to see the similarities within your data across different dimensions, you might want to use some descriptive plots such as boxplots for numeric data, barplots for counts of your categoric | Cluster Analysis in R
If you want to see the similarities within your data across different dimensions, you might want to use some descriptive plots such as boxplots for numeric data, barplots for counts of your categoric data, crosstabs and if required, stacked boxplots (boxplots where you compare some numeric variabl... | Cluster Analysis in R
If you want to see the similarities within your data across different dimensions, you might want to use some descriptive plots such as boxplots for numeric data, barplots for counts of your categoric |
55,272 | Not reporting significant results from a model I am reporting other results from | I would like to see the entire model included somewhere in the paper, probably in the methods section or in a supplement/appendix. If it the model is huge, a table is a nice way to present this. You could even organize the table to distinguish between "interesting" variables (e.g., drug dose) and boring covariates that... | Not reporting significant results from a model I am reporting other results from | I would like to see the entire model included somewhere in the paper, probably in the methods section or in a supplement/appendix. If it the model is huge, a table is a nice way to present this. You c | Not reporting significant results from a model I am reporting other results from
I would like to see the entire model included somewhere in the paper, probably in the methods section or in a supplement/appendix. If it the model is huge, a table is a nice way to present this. You could even organize the table to disting... | Not reporting significant results from a model I am reporting other results from
I would like to see the entire model included somewhere in the paper, probably in the methods section or in a supplement/appendix. If it the model is huge, a table is a nice way to present this. You c |
55,273 | Not reporting significant results from a model I am reporting other results from | If the bulk of your paper is taken up with discussion of the ANOVA results it would seem odd to omit to mention controlling for observer bias; & odd to want to, as your having taken it into account forestalls potential criticism for not having done so. If you're giving effect estimates and standard errors you need to s... | Not reporting significant results from a model I am reporting other results from | If the bulk of your paper is taken up with discussion of the ANOVA results it would seem odd to omit to mention controlling for observer bias; & odd to want to, as your having taken it into account fo | Not reporting significant results from a model I am reporting other results from
If the bulk of your paper is taken up with discussion of the ANOVA results it would seem odd to omit to mention controlling for observer bias; & odd to want to, as your having taken it into account forestalls potential criticism for not ha... | Not reporting significant results from a model I am reporting other results from
If the bulk of your paper is taken up with discussion of the ANOVA results it would seem odd to omit to mention controlling for observer bias; & odd to want to, as your having taken it into account fo |
55,274 | What are the differences between the linear regression and multiple linear regression? | By linear regression I assume that you mean simple linear regression. The difference is in the number of independent explanatory variables you use to model your dependent variable.
Simple linear regression
$Y=\beta X+\beta_0$
Multiple linear regression
$Y=\beta_1 X_1+\beta_2 X_2+...+ \beta_m X_m + \beta_0$
Where the $\... | What are the differences between the linear regression and multiple linear regression? | By linear regression I assume that you mean simple linear regression. The difference is in the number of independent explanatory variables you use to model your dependent variable.
Simple linear regre | What are the differences between the linear regression and multiple linear regression?
By linear regression I assume that you mean simple linear regression. The difference is in the number of independent explanatory variables you use to model your dependent variable.
Simple linear regression
$Y=\beta X+\beta_0$
Multipl... | What are the differences between the linear regression and multiple linear regression?
By linear regression I assume that you mean simple linear regression. The difference is in the number of independent explanatory variables you use to model your dependent variable.
Simple linear regre |
55,275 | Rejection region or p-value | In situations like these - it's best to look at things from the reader's perspective. Would the reader care about the actual value of the test statistic? Do you want the reader to know that the $T$-statistic is $2.79$ or $F = 8.91$? In most cases, the reader would not be interested in these values, so just give the p-v... | Rejection region or p-value | In situations like these - it's best to look at things from the reader's perspective. Would the reader care about the actual value of the test statistic? Do you want the reader to know that the $T$-st | Rejection region or p-value
In situations like these - it's best to look at things from the reader's perspective. Would the reader care about the actual value of the test statistic? Do you want the reader to know that the $T$-statistic is $2.79$ or $F = 8.91$? In most cases, the reader would not be interested in these ... | Rejection region or p-value
In situations like these - it's best to look at things from the reader's perspective. Would the reader care about the actual value of the test statistic? Do you want the reader to know that the $T$-st |
55,276 | Rejection region or p-value | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
I suggest you to put a exact p-value for the testing y... | Rejection region or p-value | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Rejection region or p-value
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
I suggest you to put a exa... | Rejection region or p-value
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
55,277 | Rejection region or p-value | I would recommend to report $p$-values and if you have the space go for the test statistics and rejection region. But there is a reason behind that as in the most research journals and software you can see $p$-values. First of all, let me say that the conclusions based on $p$-values and rejection region approach is bas... | Rejection region or p-value | I would recommend to report $p$-values and if you have the space go for the test statistics and rejection region. But there is a reason behind that as in the most research journals and software you ca | Rejection region or p-value
I would recommend to report $p$-values and if you have the space go for the test statistics and rejection region. But there is a reason behind that as in the most research journals and software you can see $p$-values. First of all, let me say that the conclusions based on $p$-values and reje... | Rejection region or p-value
I would recommend to report $p$-values and if you have the space go for the test statistics and rejection region. But there is a reason behind that as in the most research journals and software you ca |
55,278 | A question on SEM and path analysis | A few thoughts come to mind. I hope they are helpful.
Lets say you have exposure X, outcome Y, and mediator X.
1) Baron and Kenny is, in my opinion, not a very good way to address mediation, at least not without a lot of thougfulness. The main problem is potential "collider bias" REF. If there are confounders of the ... | A question on SEM and path analysis | A few thoughts come to mind. I hope they are helpful.
Lets say you have exposure X, outcome Y, and mediator X.
1) Baron and Kenny is, in my opinion, not a very good way to address mediation, at leas | A question on SEM and path analysis
A few thoughts come to mind. I hope they are helpful.
Lets say you have exposure X, outcome Y, and mediator X.
1) Baron and Kenny is, in my opinion, not a very good way to address mediation, at least not without a lot of thougfulness. The main problem is potential "collider bias" R... | A question on SEM and path analysis
A few thoughts come to mind. I hope they are helpful.
Lets say you have exposure X, outcome Y, and mediator X.
1) Baron and Kenny is, in my opinion, not a very good way to address mediation, at leas |
55,279 | A question on SEM and path analysis | Trying different methods seems wise to me for the sake of understanding any differences in the results you receive from different analyses. Baron & Kenny's approach has received a fair amount of criticism (e.g., Pardo & Román, 2013; Hayes, 2009; Zhao, Lynch, & Chen, 2009; Krause et al., 2010), so alternatives to that w... | A question on SEM and path analysis | Trying different methods seems wise to me for the sake of understanding any differences in the results you receive from different analyses. Baron & Kenny's approach has received a fair amount of criti | A question on SEM and path analysis
Trying different methods seems wise to me for the sake of understanding any differences in the results you receive from different analyses. Baron & Kenny's approach has received a fair amount of criticism (e.g., Pardo & Román, 2013; Hayes, 2009; Zhao, Lynch, & Chen, 2009; Krause et a... | A question on SEM and path analysis
Trying different methods seems wise to me for the sake of understanding any differences in the results you receive from different analyses. Baron & Kenny's approach has received a fair amount of criti |
55,280 | Likelihood for negative binomial distribution | Actually I'd like to disagree slightly with a previous answer. Yes it's true that the estimates themselves that come from a MLE are indeed invariant to transformations of the parameters, so it's correct that you can just take $\hat{\phi} = 1 + \hat{m}/\hat{r}$
However, the question also asked about the standard error ... | Likelihood for negative binomial distribution | Actually I'd like to disagree slightly with a previous answer. Yes it's true that the estimates themselves that come from a MLE are indeed invariant to transformations of the parameters, so it's corr | Likelihood for negative binomial distribution
Actually I'd like to disagree slightly with a previous answer. Yes it's true that the estimates themselves that come from a MLE are indeed invariant to transformations of the parameters, so it's correct that you can just take $\hat{\phi} = 1 + \hat{m}/\hat{r}$
However, the... | Likelihood for negative binomial distribution
Actually I'd like to disagree slightly with a previous answer. Yes it's true that the estimates themselves that come from a MLE are indeed invariant to transformations of the parameters, so it's corr |
55,281 | Likelihood for negative binomial distribution | One of the nice things about maximum-likelihood estimators is that they're invariant to transformations of the parameters (one-to-one transformations at any rate). So you can just take $\hat{\phi}=1+\hat{m}/\hat{r}$. | Likelihood for negative binomial distribution | One of the nice things about maximum-likelihood estimators is that they're invariant to transformations of the parameters (one-to-one transformations at any rate). So you can just take $\hat{\phi}=1+\ | Likelihood for negative binomial distribution
One of the nice things about maximum-likelihood estimators is that they're invariant to transformations of the parameters (one-to-one transformations at any rate). So you can just take $\hat{\phi}=1+\hat{m}/\hat{r}$. | Likelihood for negative binomial distribution
One of the nice things about maximum-likelihood estimators is that they're invariant to transformations of the parameters (one-to-one transformations at any rate). So you can just take $\hat{\phi}=1+\ |
55,282 | Does the central limit theorem imply the law of large numbers | WLLN, yes.
Here is a general claim: Suppose $\{ f_n \}$, $f$, and $g$ are random variables, and
$$
\sqrt{n} (f_n - f) \stackrel{d}{\mapsto} g.
$$
Let's say the CDF of $g$ is continuous everywhere. Then $f_n \rightarrow f$ in probability. This is because $\sqrt{n} (f_n - f)$ is bounded in probability/uniformly tight. | Does the central limit theorem imply the law of large numbers | WLLN, yes.
Here is a general claim: Suppose $\{ f_n \}$, $f$, and $g$ are random variables, and
$$
\sqrt{n} (f_n - f) \stackrel{d}{\mapsto} g.
$$
Let's say the CDF of $g$ is continuous everywhere. The | Does the central limit theorem imply the law of large numbers
WLLN, yes.
Here is a general claim: Suppose $\{ f_n \}$, $f$, and $g$ are random variables, and
$$
\sqrt{n} (f_n - f) \stackrel{d}{\mapsto} g.
$$
Let's say the CDF of $g$ is continuous everywhere. Then $f_n \rightarrow f$ in probability. This is because $\sq... | Does the central limit theorem imply the law of large numbers
WLLN, yes.
Here is a general claim: Suppose $\{ f_n \}$, $f$, and $g$ are random variables, and
$$
\sqrt{n} (f_n - f) \stackrel{d}{\mapsto} g.
$$
Let's say the CDF of $g$ is continuous everywhere. The |
55,283 | Mann-Whitney U test with very large sample size? | This is not a problem of the t-test, but of any test in which the power of the test depends on the sample size. This is called "overpowering". And yes, changing the test to Mann-Whitney will not help.
Therefore, apart from asking whether the results are statistically significant, you need to ask yourself whether the ob... | Mann-Whitney U test with very large sample size? | This is not a problem of the t-test, but of any test in which the power of the test depends on the sample size. This is called "overpowering". And yes, changing the test to Mann-Whitney will not help. | Mann-Whitney U test with very large sample size?
This is not a problem of the t-test, but of any test in which the power of the test depends on the sample size. This is called "overpowering". And yes, changing the test to Mann-Whitney will not help.
Therefore, apart from asking whether the results are statistically sig... | Mann-Whitney U test with very large sample size?
This is not a problem of the t-test, but of any test in which the power of the test depends on the sample size. This is called "overpowering". And yes, changing the test to Mann-Whitney will not help. |
55,284 | Simulating longitudinal lognormal data in R | Without more information, it is difficult to guess exactly what kind of data you would like to simulate, but here is an example. In my experience, growth data are often (approximately) linear when transforming (log, square-root, etc.) the predictor (time) variable, so this would be my suggestion.
library(MASS)
library(... | Simulating longitudinal lognormal data in R | Without more information, it is difficult to guess exactly what kind of data you would like to simulate, but here is an example. In my experience, growth data are often (approximately) linear when tra | Simulating longitudinal lognormal data in R
Without more information, it is difficult to guess exactly what kind of data you would like to simulate, but here is an example. In my experience, growth data are often (approximately) linear when transforming (log, square-root, etc.) the predictor (time) variable, so this wo... | Simulating longitudinal lognormal data in R
Without more information, it is difficult to guess exactly what kind of data you would like to simulate, but here is an example. In my experience, growth data are often (approximately) linear when tra |
55,285 | 2D multivariate normal coverage probability | Here's my understanding of your question: you have a circular covariance matrix, $$C = \left( \begin{array}{cc} \sigma^{2} & 0 \\ 0 & \sigma^{2} \end{array} \right)$$ together with a position vector $ \overrightarrow{X} = [x,y]$ and a 2D Gaussian pdf defined by those two items, i.e., $$p(x,y) = \frac{1}{2\pi\sigma^{2}}... | 2D multivariate normal coverage probability | Here's my understanding of your question: you have a circular covariance matrix, $$C = \left( \begin{array}{cc} \sigma^{2} & 0 \\ 0 & \sigma^{2} \end{array} \right)$$ together with a position vector $ | 2D multivariate normal coverage probability
Here's my understanding of your question: you have a circular covariance matrix, $$C = \left( \begin{array}{cc} \sigma^{2} & 0 \\ 0 & \sigma^{2} \end{array} \right)$$ together with a position vector $ \overrightarrow{X} = [x,y]$ and a 2D Gaussian pdf defined by those two item... | 2D multivariate normal coverage probability
Here's my understanding of your question: you have a circular covariance matrix, $$C = \left( \begin{array}{cc} \sigma^{2} & 0 \\ 0 & \sigma^{2} \end{array} \right)$$ together with a position vector $ |
55,286 | Proof of Convergence in Probability | The meaning of $P$ is monotone relative to set containment is this:
If $A \subseteq B$ then $P(A)\leq P(B)$.
Now here for every $\omega\in\Omega$, we have
$$\begin{align*}
A &= \left\{\omega : \left| (X_n(\omega) + Y_n(\omega)) - ( X(\omega) + Y(\omega) ) \right| \geq \epsilon \right\}, \text{ and}\\
B &= \{ \omega ... | Proof of Convergence in Probability | The meaning of $P$ is monotone relative to set containment is this:
If $A \subseteq B$ then $P(A)\leq P(B)$.
Now here for every $\omega\in\Omega$, we have
$$\begin{align*}
A &= \left\{\omega : \lef | Proof of Convergence in Probability
The meaning of $P$ is monotone relative to set containment is this:
If $A \subseteq B$ then $P(A)\leq P(B)$.
Now here for every $\omega\in\Omega$, we have
$$\begin{align*}
A &= \left\{\omega : \left| (X_n(\omega) + Y_n(\omega)) - ( X(\omega) + Y(\omega) ) \right| \geq \epsilon \ri... | Proof of Convergence in Probability
The meaning of $P$ is monotone relative to set containment is this:
If $A \subseteq B$ then $P(A)\leq P(B)$.
Now here for every $\omega\in\Omega$, we have
$$\begin{align*}
A &= \left\{\omega : \lef |
55,287 | Proof of Convergence in Probability | If you're aware of an equivalent characterization of convergence in probability, the proof is really short. It is known that $X_n \rightarrow X$ in probability if and only if, for any subsequence $\left \{a_1, a_2, \ldots \right\}$, there exists a sub-subsequence $\left\{a'_1,a'_2,\ldots \right \}$ such that $X_{a'_n}... | Proof of Convergence in Probability | If you're aware of an equivalent characterization of convergence in probability, the proof is really short. It is known that $X_n \rightarrow X$ in probability if and only if, for any subsequence $\l | Proof of Convergence in Probability
If you're aware of an equivalent characterization of convergence in probability, the proof is really short. It is known that $X_n \rightarrow X$ in probability if and only if, for any subsequence $\left \{a_1, a_2, \ldots \right\}$, there exists a sub-subsequence $\left\{a'_1,a'_2,\... | Proof of Convergence in Probability
If you're aware of an equivalent characterization of convergence in probability, the proof is really short. It is known that $X_n \rightarrow X$ in probability if and only if, for any subsequence $\l |
55,288 | Vector space model: cosine similarity vs euclidean distance | To complement other answers:
Cosine similarity of $x, y$ : $\frac{\langle x, y\rangle}{\|x\|\|y\|}$
Euclidean distance (squared) between $x, y$: $\|x-y\|^2 = \|x\|^2 +\|y\|^2 - 2\langle x , y\rangle$
Assuming that $x, y$ are normed
Cosine similarity: $\langle x , y\rangle$
Euclidean distance (squared): $2(1 - \langle ... | Vector space model: cosine similarity vs euclidean distance | To complement other answers:
Cosine similarity of $x, y$ : $\frac{\langle x, y\rangle}{\|x\|\|y\|}$
Euclidean distance (squared) between $x, y$: $\|x-y\|^2 = \|x\|^2 +\|y\|^2 - 2\langle x , y\rangle$ | Vector space model: cosine similarity vs euclidean distance
To complement other answers:
Cosine similarity of $x, y$ : $\frac{\langle x, y\rangle}{\|x\|\|y\|}$
Euclidean distance (squared) between $x, y$: $\|x-y\|^2 = \|x\|^2 +\|y\|^2 - 2\langle x , y\rangle$
Assuming that $x, y$ are normed
Cosine similarity: $\langle... | Vector space model: cosine similarity vs euclidean distance
To complement other answers:
Cosine similarity of $x, y$ : $\frac{\langle x, y\rangle}{\|x\|\|y\|}$
Euclidean distance (squared) between $x, y$: $\|x-y\|^2 = \|x\|^2 +\|y\|^2 - 2\langle x , y\rangle$ |
55,289 | Vector space model: cosine similarity vs euclidean distance | You can use the Euclidean distance, as far as you use an appropriate transformation rule, e.g:
$dist = 1 -sim$, $dist = \frac{1-sim}{sim}$, $dist = \sqrt{1-sim}$ or $dist = -\log(sim)$.
However, it is important to remember that in general a distance is not a similarity. The latter one is subjective-driven (are two obj... | Vector space model: cosine similarity vs euclidean distance | You can use the Euclidean distance, as far as you use an appropriate transformation rule, e.g:
$dist = 1 -sim$, $dist = \frac{1-sim}{sim}$, $dist = \sqrt{1-sim}$ or $dist = -\log(sim)$.
However, it i | Vector space model: cosine similarity vs euclidean distance
You can use the Euclidean distance, as far as you use an appropriate transformation rule, e.g:
$dist = 1 -sim$, $dist = \frac{1-sim}{sim}$, $dist = \sqrt{1-sim}$ or $dist = -\log(sim)$.
However, it is important to remember that in general a distance is not a ... | Vector space model: cosine similarity vs euclidean distance
You can use the Euclidean distance, as far as you use an appropriate transformation rule, e.g:
$dist = 1 -sim$, $dist = \frac{1-sim}{sim}$, $dist = \sqrt{1-sim}$ or $dist = -\log(sim)$.
However, it i |
55,290 | Vector space model: cosine similarity vs euclidean distance | If you don't normalize the vectors to be all the same length then their length will depend on the length of the document. Usually, in document classification we don't want to be biased by the document lengths. This is one reason why cosine similarity is preferred. | Vector space model: cosine similarity vs euclidean distance | If you don't normalize the vectors to be all the same length then their length will depend on the length of the document. Usually, in document classification we don't want to be biased by the document | Vector space model: cosine similarity vs euclidean distance
If you don't normalize the vectors to be all the same length then their length will depend on the length of the document. Usually, in document classification we don't want to be biased by the document lengths. This is one reason why cosine similarity is prefer... | Vector space model: cosine similarity vs euclidean distance
If you don't normalize the vectors to be all the same length then their length will depend on the length of the document. Usually, in document classification we don't want to be biased by the document |
55,291 | Normality Testing - Choose the transformation that makes the data "most normal"? | You can say a lot about a lower bound for an upper percentile, even with small amounts of data.
Suppose the 99th percentile of the true (but unknown) distribution from which $n$ values are obtained (independently) is the number $x_{0.99}$. Then the chance that $k$ or more of your data exceed $x_{0.99}$ is given by the... | Normality Testing - Choose the transformation that makes the data "most normal"? | You can say a lot about a lower bound for an upper percentile, even with small amounts of data.
Suppose the 99th percentile of the true (but unknown) distribution from which $n$ values are obtained (i | Normality Testing - Choose the transformation that makes the data "most normal"?
You can say a lot about a lower bound for an upper percentile, even with small amounts of data.
Suppose the 99th percentile of the true (but unknown) distribution from which $n$ values are obtained (independently) is the number $x_{0.99}$.... | Normality Testing - Choose the transformation that makes the data "most normal"?
You can say a lot about a lower bound for an upper percentile, even with small amounts of data.
Suppose the 99th percentile of the true (but unknown) distribution from which $n$ values are obtained (i |
55,292 | Normality Testing - Choose the transformation that makes the data "most normal"? | Any attempt to use the data to fit the distribution, when the choice is among 3 or more distributions, will result in at most tiny improvements over nonparametric methods, due to model uncertainty. For example if you try different distributions to get agreement with the empirical CDF the true variance of the final est... | Normality Testing - Choose the transformation that makes the data "most normal"? | Any attempt to use the data to fit the distribution, when the choice is among 3 or more distributions, will result in at most tiny improvements over nonparametric methods, due to model uncertainty. F | Normality Testing - Choose the transformation that makes the data "most normal"?
Any attempt to use the data to fit the distribution, when the choice is among 3 or more distributions, will result in at most tiny improvements over nonparametric methods, due to model uncertainty. For example if you try different distrib... | Normality Testing - Choose the transformation that makes the data "most normal"?
Any attempt to use the data to fit the distribution, when the choice is among 3 or more distributions, will result in at most tiny improvements over nonparametric methods, due to model uncertainty. F |
55,293 | Autocorrelation and trends | The autocorrelation(acf) function summarizes the correlation of different lags and is a descriptive statistic. If there is a "trend" in the data then the acf will suggest non-stationarity. However a non-stationary acf does not necessarily suggest a "trend". If the series is impacted by one or more level/step shifts the... | Autocorrelation and trends | The autocorrelation(acf) function summarizes the correlation of different lags and is a descriptive statistic. If there is a "trend" in the data then the acf will suggest non-stationarity. However a n | Autocorrelation and trends
The autocorrelation(acf) function summarizes the correlation of different lags and is a descriptive statistic. If there is a "trend" in the data then the acf will suggest non-stationarity. However a non-stationary acf does not necessarily suggest a "trend". If the series is impacted by one or... | Autocorrelation and trends
The autocorrelation(acf) function summarizes the correlation of different lags and is a descriptive statistic. If there is a "trend" in the data then the acf will suggest non-stationarity. However a n |
55,294 | Autocorrelation and trends | Autocorrelation function (ACF) is an theoretical object related to the population moments. What happens when these moments do not exist as finite?
Sample autocorrelation function (SACF) is a descriptive statistic and is a function of sample moments, mainly sample mean. What is a breakpoint value for the sample mean? Is... | Autocorrelation and trends | Autocorrelation function (ACF) is an theoretical object related to the population moments. What happens when these moments do not exist as finite?
Sample autocorrelation function (SACF) is a descripti | Autocorrelation and trends
Autocorrelation function (ACF) is an theoretical object related to the population moments. What happens when these moments do not exist as finite?
Sample autocorrelation function (SACF) is a descriptive statistic and is a function of sample moments, mainly sample mean. What is a breakpoint va... | Autocorrelation and trends
Autocorrelation function (ACF) is an theoretical object related to the population moments. What happens when these moments do not exist as finite?
Sample autocorrelation function (SACF) is a descripti |
55,295 | How to identify outliers and conduct robust PCA? | Robust PCA is a very active research area, and identifying and removing outliers in a sound way is quite delicate. (I've written two papers in this field, so I do know a bit about it.) While I don't know SPSS, you may be able to implement the relatively simple Algorithm (1) here.
This algorithm (not mine) has rigorou... | How to identify outliers and conduct robust PCA? | Robust PCA is a very active research area, and identifying and removing outliers in a sound way is quite delicate. (I've written two papers in this field, so I do know a bit about it.) While I don't k | How to identify outliers and conduct robust PCA?
Robust PCA is a very active research area, and identifying and removing outliers in a sound way is quite delicate. (I've written two papers in this field, so I do know a bit about it.) While I don't know SPSS, you may be able to implement the relatively simple Algorithm ... | How to identify outliers and conduct robust PCA?
Robust PCA is a very active research area, and identifying and removing outliers in a sound way is quite delicate. (I've written two papers in this field, so I do know a bit about it.) While I don't k |
55,296 | Independence of multivariate normal distribution | Indeed, for the normal distribution, uncorrelatedness implies independence.
For your first case, showing formally independence between the random vector $(X_1, X_2)$ and the scalar random variable $X_3$ can be done by showing that the conditional on $X_3$ mean and conditional covariance matrix of the random vector $(X... | Independence of multivariate normal distribution | Indeed, for the normal distribution, uncorrelatedness implies independence.
For your first case, showing formally independence between the random vector $(X_1, X_2)$ and the scalar random variable $X_ | Independence of multivariate normal distribution
Indeed, for the normal distribution, uncorrelatedness implies independence.
For your first case, showing formally independence between the random vector $(X_1, X_2)$ and the scalar random variable $X_3$ can be done by showing that the conditional on $X_3$ mean and condi... | Independence of multivariate normal distribution
Indeed, for the normal distribution, uncorrelatedness implies independence.
For your first case, showing formally independence between the random vector $(X_1, X_2)$ and the scalar random variable $X_ |
55,297 | How can a combination of model parameters have a lower standard error than each individual coefficient? | Denote $\hat b_1$, $\hat b_2$ two estimated coefficients/estimator functions. These are random variables, and have been estimated using the same data (same realizations of random variables). So naturally, they are not independent. Apart from extreme cases of totally non-linear dependence, this means that their co-varia... | How can a combination of model parameters have a lower standard error than each individual coefficie | Denote $\hat b_1$, $\hat b_2$ two estimated coefficients/estimator functions. These are random variables, and have been estimated using the same data (same realizations of random variables). So natura | How can a combination of model parameters have a lower standard error than each individual coefficient?
Denote $\hat b_1$, $\hat b_2$ two estimated coefficients/estimator functions. These are random variables, and have been estimated using the same data (same realizations of random variables). So naturally, they are no... | How can a combination of model parameters have a lower standard error than each individual coefficie
Denote $\hat b_1$, $\hat b_2$ two estimated coefficients/estimator functions. These are random variables, and have been estimated using the same data (same realizations of random variables). So natura |
55,298 | Methods for randomly allocating people between active and placebo in clinical trials | If you have good reasons to think that these three factors influence the outcome, you may want to block them. Otherwise, you could still use blocking (e.g., Imai, King, & Stuart, 2008), but it's probably not that important in that case.
Search for blocking on this site, or Wikipedia, or ...
Imai, K., King, G., & Stuart... | Methods for randomly allocating people between active and placebo in clinical trials | If you have good reasons to think that these three factors influence the outcome, you may want to block them. Otherwise, you could still use blocking (e.g., Imai, King, & Stuart, 2008), but it's proba | Methods for randomly allocating people between active and placebo in clinical trials
If you have good reasons to think that these three factors influence the outcome, you may want to block them. Otherwise, you could still use blocking (e.g., Imai, King, & Stuart, 2008), but it's probably not that important in that case... | Methods for randomly allocating people between active and placebo in clinical trials
If you have good reasons to think that these three factors influence the outcome, you may want to block them. Otherwise, you could still use blocking (e.g., Imai, King, & Stuart, 2008), but it's proba |
55,299 | Methods for randomly allocating people between active and placebo in clinical trials | Assuming all three factors are prognostic (i.e. strongly related to the primary outcome) the most common methods of balancing them within the treatment groups are:
Random permuted blocks within strata
Minimisation
In both cases you must categorise your age variable - it's usual to create 2 categories based on median ... | Methods for randomly allocating people between active and placebo in clinical trials | Assuming all three factors are prognostic (i.e. strongly related to the primary outcome) the most common methods of balancing them within the treatment groups are:
Random permuted blocks within strat | Methods for randomly allocating people between active and placebo in clinical trials
Assuming all three factors are prognostic (i.e. strongly related to the primary outcome) the most common methods of balancing them within the treatment groups are:
Random permuted blocks within strata
Minimisation
In both cases you m... | Methods for randomly allocating people between active and placebo in clinical trials
Assuming all three factors are prognostic (i.e. strongly related to the primary outcome) the most common methods of balancing them within the treatment groups are:
Random permuted blocks within strat |
55,300 | To what extent can statistics improve patient's treatment? | @user32240, first of all, I am sorry for your loss. It is quite difficult and painful to watch people we love fail in treatment or watch a friend's family member fail in treatment. I think we are a pattern seeking species and that trait is part of what helped us evolve. For example, we were able to recognize a patte... | To what extent can statistics improve patient's treatment? | @user32240, first of all, I am sorry for your loss. It is quite difficult and painful to watch people we love fail in treatment or watch a friend's family member fail in treatment. I think we are a | To what extent can statistics improve patient's treatment?
@user32240, first of all, I am sorry for your loss. It is quite difficult and painful to watch people we love fail in treatment or watch a friend's family member fail in treatment. I think we are a pattern seeking species and that trait is part of what helped... | To what extent can statistics improve patient's treatment?
@user32240, first of all, I am sorry for your loss. It is quite difficult and painful to watch people we love fail in treatment or watch a friend's family member fail in treatment. I think we are a |
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