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 |
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1,001 | R vs SAS, why is SAS preferred by private companies? | The reason I understood to be the most convincing was that SAS has an extensive library of vertical business specific modules that people in these verticals all use, so it is somewhat of a lock-in.
But also that SAS has addressed the needs of these vertical segments in business and optimized around their needs - optimi... | R vs SAS, why is SAS preferred by private companies? | The reason I understood to be the most convincing was that SAS has an extensive library of vertical business specific modules that people in these verticals all use, so it is somewhat of a lock-in.
Bu | R vs SAS, why is SAS preferred by private companies?
The reason I understood to be the most convincing was that SAS has an extensive library of vertical business specific modules that people in these verticals all use, so it is somewhat of a lock-in.
But also that SAS has addressed the needs of these vertical segments ... | R vs SAS, why is SAS preferred by private companies?
The reason I understood to be the most convincing was that SAS has an extensive library of vertical business specific modules that people in these verticals all use, so it is somewhat of a lock-in.
Bu |
1,002 | R vs SAS, why is SAS preferred by private companies? | Being the big commercial product that SAS is, there's a strong and coordinated effort by payed salespersons to promote it. I don't think that efforts to promote the usage of R can match these. | R vs SAS, why is SAS preferred by private companies? | Being the big commercial product that SAS is, there's a strong and coordinated effort by payed salespersons to promote it. I don't think that efforts to promote the usage of R can match these. | R vs SAS, why is SAS preferred by private companies?
Being the big commercial product that SAS is, there's a strong and coordinated effort by payed salespersons to promote it. I don't think that efforts to promote the usage of R can match these. | R vs SAS, why is SAS preferred by private companies?
Being the big commercial product that SAS is, there's a strong and coordinated effort by payed salespersons to promote it. I don't think that efforts to promote the usage of R can match these. |
1,003 | R vs SAS, why is SAS preferred by private companies? | I look at Open Source or licenced software like this, be it SAS or anything else. My IT department is there to provide a service to our business. The company earns no money from IT, only from the business IT supports. The business has annual revenues of \$16 Billion. IT costs around \$200 million a year. If money was t... | R vs SAS, why is SAS preferred by private companies? | I look at Open Source or licenced software like this, be it SAS or anything else. My IT department is there to provide a service to our business. The company earns no money from IT, only from the busi | R vs SAS, why is SAS preferred by private companies?
I look at Open Source or licenced software like this, be it SAS or anything else. My IT department is there to provide a service to our business. The company earns no money from IT, only from the business IT supports. The business has annual revenues of \$16 Billion.... | R vs SAS, why is SAS preferred by private companies?
I look at Open Source or licenced software like this, be it SAS or anything else. My IT department is there to provide a service to our business. The company earns no money from IT, only from the busi |
1,004 | R vs SAS, why is SAS preferred by private companies? | Some reasons that I haven't seen mentioned:
Better documentation. SAS documentation is verbose, R documentation is terse. Many companies may prefer verbose documentation.
Better error messages. R's error messages often seem designed to prove that the person writing the message is smarter than the person reading it.
T... | R vs SAS, why is SAS preferred by private companies? | Some reasons that I haven't seen mentioned:
Better documentation. SAS documentation is verbose, R documentation is terse. Many companies may prefer verbose documentation.
Better error messages. R's | R vs SAS, why is SAS preferred by private companies?
Some reasons that I haven't seen mentioned:
Better documentation. SAS documentation is verbose, R documentation is terse. Many companies may prefer verbose documentation.
Better error messages. R's error messages often seem designed to prove that the person writing... | R vs SAS, why is SAS preferred by private companies?
Some reasons that I haven't seen mentioned:
Better documentation. SAS documentation is verbose, R documentation is terse. Many companies may prefer verbose documentation.
Better error messages. R's |
1,005 | R vs SAS, why is SAS preferred by private companies? | I think the legacy angle can be a big one for the following reason. An organisation hires a person, call them person X. They are a computing guru/wizard/etc. They build awesome SAS programs/tools/etc. They are so good that other people in the organisation don't feel like they need to understand how the programs work. T... | R vs SAS, why is SAS preferred by private companies? | I think the legacy angle can be a big one for the following reason. An organisation hires a person, call them person X. They are a computing guru/wizard/etc. They build awesome SAS programs/tools/etc. | R vs SAS, why is SAS preferred by private companies?
I think the legacy angle can be a big one for the following reason. An organisation hires a person, call them person X. They are a computing guru/wizard/etc. They build awesome SAS programs/tools/etc. They are so good that other people in the organisation don't feel ... | R vs SAS, why is SAS preferred by private companies?
I think the legacy angle can be a big one for the following reason. An organisation hires a person, call them person X. They are a computing guru/wizard/etc. They build awesome SAS programs/tools/etc. |
1,006 | R vs SAS, why is SAS preferred by private companies? | For industrial statistics, there are quality assurance people who (usually) have no programming, statistics, or science background and who audit statisticians, programmers, and scientists. They want to know, "How do you know that what you're doing is right?" and "If it's wrong, how can we blame somebody and how will th... | R vs SAS, why is SAS preferred by private companies? | For industrial statistics, there are quality assurance people who (usually) have no programming, statistics, or science background and who audit statisticians, programmers, and scientists. They want t | R vs SAS, why is SAS preferred by private companies?
For industrial statistics, there are quality assurance people who (usually) have no programming, statistics, or science background and who audit statisticians, programmers, and scientists. They want to know, "How do you know that what you're doing is right?" and "If ... | R vs SAS, why is SAS preferred by private companies?
For industrial statistics, there are quality assurance people who (usually) have no programming, statistics, or science background and who audit statisticians, programmers, and scientists. They want t |
1,007 | Does causation imply correlation? | As many of the answers above have stated, causation does not imply linear correlation. Since a lot of the correlation concepts come from fields that rely heavily on linear statistics, usually correlation is seen as equal to linear correlation. The wikipedia article is an alright source for this, I really like this imag... | Does causation imply correlation? | As many of the answers above have stated, causation does not imply linear correlation. Since a lot of the correlation concepts come from fields that rely heavily on linear statistics, usually correlat | Does causation imply correlation?
As many of the answers above have stated, causation does not imply linear correlation. Since a lot of the correlation concepts come from fields that rely heavily on linear statistics, usually correlation is seen as equal to linear correlation. The wikipedia article is an alright source... | Does causation imply correlation?
As many of the answers above have stated, causation does not imply linear correlation. Since a lot of the correlation concepts come from fields that rely heavily on linear statistics, usually correlat |
1,008 | Does causation imply correlation? | The strict answer is "no, causation does not necessarily imply correlation".
Consider $X\sim N(0,1)$ and $Y=X^2\sim\chi^2_1$. Causation does not get any stronger: $X$ determines $Y$. Yet, correlation between $X$ and $Y$ is 0. Proof: The (joint) moments of these variables are: $E[X]=0$; $E[Y]=E[X^2]=1$; $${\rm Cov}[X,Y]... | Does causation imply correlation? | The strict answer is "no, causation does not necessarily imply correlation".
Consider $X\sim N(0,1)$ and $Y=X^2\sim\chi^2_1$. Causation does not get any stronger: $X$ determines $Y$. Yet, correlation | Does causation imply correlation?
The strict answer is "no, causation does not necessarily imply correlation".
Consider $X\sim N(0,1)$ and $Y=X^2\sim\chi^2_1$. Causation does not get any stronger: $X$ determines $Y$. Yet, correlation between $X$ and $Y$ is 0. Proof: The (joint) moments of these variables are: $E[X]=0$;... | Does causation imply correlation?
The strict answer is "no, causation does not necessarily imply correlation".
Consider $X\sim N(0,1)$ and $Y=X^2\sim\chi^2_1$. Causation does not get any stronger: $X$ determines $Y$. Yet, correlation |
1,009 | Does causation imply correlation? | Essentially, yes.
Correlation does not imply causation because there could be other explanations for a correlation beyond cause. But in order for A to be a cause of B they must be associated in some way. Meaning there is a correlation between them - though that correlation does not necessarily need to be linear.
As som... | Does causation imply correlation? | Essentially, yes.
Correlation does not imply causation because there could be other explanations for a correlation beyond cause. But in order for A to be a cause of B they must be associated in some w | Does causation imply correlation?
Essentially, yes.
Correlation does not imply causation because there could be other explanations for a correlation beyond cause. But in order for A to be a cause of B they must be associated in some way. Meaning there is a correlation between them - though that correlation does not nec... | Does causation imply correlation?
Essentially, yes.
Correlation does not imply causation because there could be other explanations for a correlation beyond cause. But in order for A to be a cause of B they must be associated in some w |
1,010 | Does causation imply correlation? | Things are definitely nuanced here. Causation does not imply correlation nor even statistical dependence, at least not in the simple way we usually think about them, or in the way some answers are suggesting (just transforming $X$ or $Y$ etc).
Consider the following causal model:
$$
X \rightarrow Y \leftarrow U
$$
That... | Does causation imply correlation? | Things are definitely nuanced here. Causation does not imply correlation nor even statistical dependence, at least not in the simple way we usually think about them, or in the way some answers are sug | Does causation imply correlation?
Things are definitely nuanced here. Causation does not imply correlation nor even statistical dependence, at least not in the simple way we usually think about them, or in the way some answers are suggesting (just transforming $X$ or $Y$ etc).
Consider the following causal model:
$$
X ... | Does causation imply correlation?
Things are definitely nuanced here. Causation does not imply correlation nor even statistical dependence, at least not in the simple way we usually think about them, or in the way some answers are sug |
1,011 | Does causation imply correlation? | Adding to @EpiGrad 's answer. I think, for a lot of people, "correlation" will imply "linear correlation". And the concept of nonlinear correlation might not be intuitive.
So, I would say "no they don't have to be correlated but they do have to be related". We are agreeing on the substance, but disagreeing on the best... | Does causation imply correlation? | Adding to @EpiGrad 's answer. I think, for a lot of people, "correlation" will imply "linear correlation". And the concept of nonlinear correlation might not be intuitive.
So, I would say "no they do | Does causation imply correlation?
Adding to @EpiGrad 's answer. I think, for a lot of people, "correlation" will imply "linear correlation". And the concept of nonlinear correlation might not be intuitive.
So, I would say "no they don't have to be correlated but they do have to be related". We are agreeing on the subs... | Does causation imply correlation?
Adding to @EpiGrad 's answer. I think, for a lot of people, "correlation" will imply "linear correlation". And the concept of nonlinear correlation might not be intuitive.
So, I would say "no they do |
1,012 | Does causation imply correlation? | The cause and the effect will be correlated unless there is no variation at all in the incidence and magnitude of the cause and no variation at all in its causal force. The only other possibility would be if the cause is perfectly correlated with another causal variable with exactly the opposite effect. Basically, th... | Does causation imply correlation? | The cause and the effect will be correlated unless there is no variation at all in the incidence and magnitude of the cause and no variation at all in its causal force. The only other possibility wou | Does causation imply correlation?
The cause and the effect will be correlated unless there is no variation at all in the incidence and magnitude of the cause and no variation at all in its causal force. The only other possibility would be if the cause is perfectly correlated with another causal variable with exactly t... | Does causation imply correlation?
The cause and the effect will be correlated unless there is no variation at all in the incidence and magnitude of the cause and no variation at all in its causal force. The only other possibility wou |
1,013 | Does causation imply correlation? | There are great answers here. Artem Kaznatcheev, Fomite and Peter Flom point out that causation would usually imply dependence rather than linear correlation. Carlos Cinelli gives an example where there's no dependence, because of how the generating function is set up.
I want to add a point about how this dependence c... | Does causation imply correlation? | There are great answers here. Artem Kaznatcheev, Fomite and Peter Flom point out that causation would usually imply dependence rather than linear correlation. Carlos Cinelli gives an example where the | Does causation imply correlation?
There are great answers here. Artem Kaznatcheev, Fomite and Peter Flom point out that causation would usually imply dependence rather than linear correlation. Carlos Cinelli gives an example where there's no dependence, because of how the generating function is set up.
I want to add a... | Does causation imply correlation?
There are great answers here. Artem Kaznatcheev, Fomite and Peter Flom point out that causation would usually imply dependence rather than linear correlation. Carlos Cinelli gives an example where the |
1,014 | Does causation imply correlation? | The answer is: Causation does not imply (linear) correlation.
Assume we have the causal graph: $X \rightarrow Y$, where $X$ is a cause of $Y$, such that, if $X < 0$ we have $Y=X$ and else (if $X \geq 0$) we have $Y=-X$.
Clearly, $X$ is a cause of $Y$. However, when you compute the correlation between instances of $X$ a... | Does causation imply correlation? | The answer is: Causation does not imply (linear) correlation.
Assume we have the causal graph: $X \rightarrow Y$, where $X$ is a cause of $Y$, such that, if $X < 0$ we have $Y=X$ and else (if $X \geq | Does causation imply correlation?
The answer is: Causation does not imply (linear) correlation.
Assume we have the causal graph: $X \rightarrow Y$, where $X$ is a cause of $Y$, such that, if $X < 0$ we have $Y=X$ and else (if $X \geq 0$) we have $Y=-X$.
Clearly, $X$ is a cause of $Y$. However, when you compute the corr... | Does causation imply correlation?
The answer is: Causation does not imply (linear) correlation.
Assume we have the causal graph: $X \rightarrow Y$, where $X$ is a cause of $Y$, such that, if $X < 0$ we have $Y=X$ and else (if $X \geq |
1,015 | Does causation imply correlation? | I add a less statistically technical answer here for the less statistically inclined audience:
One variable (let's say, X) can positively influence another variable (let's say, $Y$), while not being associated with $Y$, or even being negatively associated with $Y$, if there are confounding factors that distort the asso... | Does causation imply correlation? | I add a less statistically technical answer here for the less statistically inclined audience:
One variable (let's say, X) can positively influence another variable (let's say, $Y$), while not being a | Does causation imply correlation?
I add a less statistically technical answer here for the less statistically inclined audience:
One variable (let's say, X) can positively influence another variable (let's say, $Y$), while not being associated with $Y$, or even being negatively associated with $Y$, if there are confoun... | Does causation imply correlation?
I add a less statistically technical answer here for the less statistically inclined audience:
One variable (let's say, X) can positively influence another variable (let's say, $Y$), while not being a |
1,016 | Help me understand Bayesian prior and posterior distributions | Let me first explain what a conjugate prior is. I will then explain the Bayesian analyses using your specific example.
Bayesian statistics involve the following steps:
Define the prior distribution that incorporates your subjective beliefs about a parameter (in your example the parameter of interest is the proportion ... | Help me understand Bayesian prior and posterior distributions | Let me first explain what a conjugate prior is. I will then explain the Bayesian analyses using your specific example.
Bayesian statistics involve the following steps:
Define the prior distribution t | Help me understand Bayesian prior and posterior distributions
Let me first explain what a conjugate prior is. I will then explain the Bayesian analyses using your specific example.
Bayesian statistics involve the following steps:
Define the prior distribution that incorporates your subjective beliefs about a parameter... | Help me understand Bayesian prior and posterior distributions
Let me first explain what a conjugate prior is. I will then explain the Bayesian analyses using your specific example.
Bayesian statistics involve the following steps:
Define the prior distribution t |
1,017 | Help me understand Bayesian prior and posterior distributions | A beta distribution with $\alpha$ = 1 and $\beta$ = 1 is the same as a uniform distribution. So it is in fact, uniformative. You're trying to find information about a parameter of a distribution (in this case, percentage of left handed people in a group of people). Bayes formula states:
$P(r|Y_{1,...,n})$ = $\frac{P... | Help me understand Bayesian prior and posterior distributions | A beta distribution with $\alpha$ = 1 and $\beta$ = 1 is the same as a uniform distribution. So it is in fact, uniformative. You're trying to find information about a parameter of a distribution (in | Help me understand Bayesian prior and posterior distributions
A beta distribution with $\alpha$ = 1 and $\beta$ = 1 is the same as a uniform distribution. So it is in fact, uniformative. You're trying to find information about a parameter of a distribution (in this case, percentage of left handed people in a group of... | Help me understand Bayesian prior and posterior distributions
A beta distribution with $\alpha$ = 1 and $\beta$ = 1 is the same as a uniform distribution. So it is in fact, uniformative. You're trying to find information about a parameter of a distribution (in |
1,018 | Help me understand Bayesian prior and posterior distributions | In the first part of your question it asks you to define a suitable prior for "r". With the binomial data in hand it would be wise to choose a beta distribution. Because then the posterior will be a beta. The Uniform ditribution being a special case of beta, you can choose prior for "r" the Uniform disribution allowing... | Help me understand Bayesian prior and posterior distributions | In the first part of your question it asks you to define a suitable prior for "r". With the binomial data in hand it would be wise to choose a beta distribution. Because then the posterior will be a b | Help me understand Bayesian prior and posterior distributions
In the first part of your question it asks you to define a suitable prior for "r". With the binomial data in hand it would be wise to choose a beta distribution. Because then the posterior will be a beta. The Uniform ditribution being a special case of beta,... | Help me understand Bayesian prior and posterior distributions
In the first part of your question it asks you to define a suitable prior for "r". With the binomial data in hand it would be wise to choose a beta distribution. Because then the posterior will be a b |
1,019 | Amazon interview question—probability of 2nd interview | Say 200 people took the interview, so that 100 received a 2nd interview and 100 did not. Out of the first lot, 95 felt they had a great first interview. Out of the 2nd lot, 75 felt they had a great first interview. So in total 95 + 75 people felt they had a great first interview. Of those 95 + 75 = 170 people, only 95 ... | Amazon interview question—probability of 2nd interview | Say 200 people took the interview, so that 100 received a 2nd interview and 100 did not. Out of the first lot, 95 felt they had a great first interview. Out of the 2nd lot, 75 felt they had a great fi | Amazon interview question—probability of 2nd interview
Say 200 people took the interview, so that 100 received a 2nd interview and 100 did not. Out of the first lot, 95 felt they had a great first interview. Out of the 2nd lot, 75 felt they had a great first interview. So in total 95 + 75 people felt they had a great f... | Amazon interview question—probability of 2nd interview
Say 200 people took the interview, so that 100 received a 2nd interview and 100 did not. Out of the first lot, 95 felt they had a great first interview. Out of the 2nd lot, 75 felt they had a great fi |
1,020 | Amazon interview question—probability of 2nd interview | Let
$\text{pass}=$ being invited to a second interview,
$\text{fail}=$ not being so invited,
$\text{good}=$ feel good about first interview, and
$\text{bad}=$ don't feel good about first interview.
$$
\begin{align}
p(\text{pass}) &= 0.5 \\
p(\text{good}\mid\text{pass}) &= 0.95 \\
p(\text{good}\mid\text{fail}) &= 0.75... | Amazon interview question—probability of 2nd interview | Let
$\text{pass}=$ being invited to a second interview,
$\text{fail}=$ not being so invited,
$\text{good}=$ feel good about first interview, and
$\text{bad}=$ don't feel good about first interview.
| Amazon interview question—probability of 2nd interview
Let
$\text{pass}=$ being invited to a second interview,
$\text{fail}=$ not being so invited,
$\text{good}=$ feel good about first interview, and
$\text{bad}=$ don't feel good about first interview.
$$
\begin{align}
p(\text{pass}) &= 0.5 \\
p(\text{good}\mid\text{... | Amazon interview question—probability of 2nd interview
Let
$\text{pass}=$ being invited to a second interview,
$\text{fail}=$ not being so invited,
$\text{good}=$ feel good about first interview, and
$\text{bad}=$ don't feel good about first interview.
|
1,021 | Amazon interview question—probability of 2nd interview | The question contains insufficient information to answer the question:
$x$% of all people do A
$y$% of your friends do B
Unless we know the population size of all people and your friends, it is not possible to answer this question accurately, unless we make either of two assumptions:
The group your friends is represen... | Amazon interview question—probability of 2nd interview | The question contains insufficient information to answer the question:
$x$% of all people do A
$y$% of your friends do B
Unless we know the population size of all people and your friends, it is not po | Amazon interview question—probability of 2nd interview
The question contains insufficient information to answer the question:
$x$% of all people do A
$y$% of your friends do B
Unless we know the population size of all people and your friends, it is not possible to answer this question accurately, unless we make either ... | Amazon interview question—probability of 2nd interview
The question contains insufficient information to answer the question:
$x$% of all people do A
$y$% of your friends do B
Unless we know the population size of all people and your friends, it is not po |
1,022 | Amazon interview question—probability of 2nd interview | The answer is 50%. Particularly since it was an interview question I think Amazon wanted to test the candidate to see if they could spot the obvious and not be distracted by the unimportant.
When you hear hoofbeats, think horses, not zebras - reference
My explanation: The first statement is all the information you n... | Amazon interview question—probability of 2nd interview | The answer is 50%. Particularly since it was an interview question I think Amazon wanted to test the candidate to see if they could spot the obvious and not be distracted by the unimportant.
When you | Amazon interview question—probability of 2nd interview
The answer is 50%. Particularly since it was an interview question I think Amazon wanted to test the candidate to see if they could spot the obvious and not be distracted by the unimportant.
When you hear hoofbeats, think horses, not zebras - reference
My explana... | Amazon interview question—probability of 2nd interview
The answer is 50%. Particularly since it was an interview question I think Amazon wanted to test the candidate to see if they could spot the obvious and not be distracted by the unimportant.
When you |
1,023 | Amazon interview question—probability of 2nd interview | The answer that I would give is:
Based on this information, 50%. 'Your friends' is not a representative sample so it should not be considered in the probability calculation.
If you assume that the data is valid then Bayes' theorem is the way to go. | Amazon interview question—probability of 2nd interview | The answer that I would give is:
Based on this information, 50%. 'Your friends' is not a representative sample so it should not be considered in the probability calculation.
If you assume that the d | Amazon interview question—probability of 2nd interview
The answer that I would give is:
Based on this information, 50%. 'Your friends' is not a representative sample so it should not be considered in the probability calculation.
If you assume that the data is valid then Bayes' theorem is the way to go. | Amazon interview question—probability of 2nd interview
The answer that I would give is:
Based on this information, 50%. 'Your friends' is not a representative sample so it should not be considered in the probability calculation.
If you assume that the d |
1,024 | Amazon interview question—probability of 2nd interview | State that none of your friends are also up for interview.
State that the question is underconstrained.
Before they can scramble for some further constraint to the problem quickly try and get in a more productive pre-prepared question of your own in a manner fully expecting a response. Maybe you can get them to move o... | Amazon interview question—probability of 2nd interview | State that none of your friends are also up for interview.
State that the question is underconstrained.
Before they can scramble for some further constraint to the problem quickly try and get in a mo | Amazon interview question—probability of 2nd interview
State that none of your friends are also up for interview.
State that the question is underconstrained.
Before they can scramble for some further constraint to the problem quickly try and get in a more productive pre-prepared question of your own in a manner fully... | Amazon interview question—probability of 2nd interview
State that none of your friends are also up for interview.
State that the question is underconstrained.
Before they can scramble for some further constraint to the problem quickly try and get in a mo |
1,025 | Amazon interview question—probability of 2nd interview | Joke answers but should work well:
"100% When it comes to demanding superb performance from myself, I don't attribute the outcome to any probability. See you in the 2nd interview."
"50%, until my friends got their own Amazon Prime account I won't consider their feelings valid. Actually, sorry, that was a bit too hars... | Amazon interview question—probability of 2nd interview | Joke answers but should work well:
"100% When it comes to demanding superb performance from myself, I don't attribute the outcome to any probability. See you in the 2nd interview."
"50%, until my fr | Amazon interview question—probability of 2nd interview
Joke answers but should work well:
"100% When it comes to demanding superb performance from myself, I don't attribute the outcome to any probability. See you in the 2nd interview."
"50%, until my friends got their own Amazon Prime account I won't consider their f... | Amazon interview question—probability of 2nd interview
Joke answers but should work well:
"100% When it comes to demanding superb performance from myself, I don't attribute the outcome to any probability. See you in the 2nd interview."
"50%, until my fr |
1,026 | Amazon interview question—probability of 2nd interview | Simple case :
95 / (95 + 75) ≈ 0.559 is a quick way to get to the result Out of people who felt good - 95 succeeded , 75 failed . So thats probability of you passing from that group is above . But
No where it is said you are part of the above group .
If you can think that distributions (your friends circle's) patt... | Amazon interview question—probability of 2nd interview | Simple case :
95 / (95 + 75) ≈ 0.559 is a quick way to get to the result Out of people who felt good - 95 succeeded , 75 failed . So thats probability of you passing from that group is above . But
| Amazon interview question—probability of 2nd interview
Simple case :
95 / (95 + 75) ≈ 0.559 is a quick way to get to the result Out of people who felt good - 95 succeeded , 75 failed . So thats probability of you passing from that group is above . But
No where it is said you are part of the above group .
If you ca... | Amazon interview question—probability of 2nd interview
Simple case :
95 / (95 + 75) ≈ 0.559 is a quick way to get to the result Out of people who felt good - 95 succeeded , 75 failed . So thats probability of you passing from that group is above . But
|
1,027 | Amazon interview question—probability of 2nd interview | It might be helpful to view this chain of events as a binary tree where just two leaf probabilities are relevant. The root node contains all folks who had a 1st interview; we then split this group on being invited to a 2nd interview ("2nd", "no 2nd") and subsequently on whether they felt good about the 1st interview ("... | Amazon interview question—probability of 2nd interview | It might be helpful to view this chain of events as a binary tree where just two leaf probabilities are relevant. The root node contains all folks who had a 1st interview; we then split this group on | Amazon interview question—probability of 2nd interview
It might be helpful to view this chain of events as a binary tree where just two leaf probabilities are relevant. The root node contains all folks who had a 1st interview; we then split this group on being invited to a 2nd interview ("2nd", "no 2nd") and subsequent... | Amazon interview question—probability of 2nd interview
It might be helpful to view this chain of events as a binary tree where just two leaf probabilities are relevant. The root node contains all folks who had a 1st interview; we then split this group on |
1,028 | Amazon interview question—probability of 2nd interview | The answer is 50%. They told you in the first line what the chance of anyone getting a second interview is. It's a test of your ability to see the essential information and not get distracted by irrelevant noise like how your friends felt. How they felt made no difference. | Amazon interview question—probability of 2nd interview | The answer is 50%. They told you in the first line what the chance of anyone getting a second interview is. It's a test of your ability to see the essential information and not get distracted by irrel | Amazon interview question—probability of 2nd interview
The answer is 50%. They told you in the first line what the chance of anyone getting a second interview is. It's a test of your ability to see the essential information and not get distracted by irrelevant noise like how your friends felt. How they felt made no dif... | Amazon interview question—probability of 2nd interview
The answer is 50%. They told you in the first line what the chance of anyone getting a second interview is. It's a test of your ability to see the essential information and not get distracted by irrel |
1,029 | Amazon interview question—probability of 2nd interview | Both statements say:
% of your friends
not
% of your friends who were interviewed
We do know that the group "that got a second interview" can only include those who had a first interview. However, the group "that did not get a second interview" includes all other friends.
Without knowing what percentage of your fri... | Amazon interview question—probability of 2nd interview | Both statements say:
% of your friends
not
% of your friends who were interviewed
We do know that the group "that got a second interview" can only include those who had a first interview. However, | Amazon interview question—probability of 2nd interview
Both statements say:
% of your friends
not
% of your friends who were interviewed
We do know that the group "that got a second interview" can only include those who had a first interview. However, the group "that did not get a second interview" includes all oth... | Amazon interview question—probability of 2nd interview
Both statements say:
% of your friends
not
% of your friends who were interviewed
We do know that the group "that got a second interview" can only include those who had a first interview. However, |
1,030 | Amazon interview question—probability of 2nd interview | This being an interview question, I don't believe there is a correct answer.
I would most likely calculate the ~56% using Bayes and then tell the interviewer:
Without any knowledge about me, it could be between 50% and 56%, but because I know me and my past, the probability is 100% | Amazon interview question—probability of 2nd interview | This being an interview question, I don't believe there is a correct answer.
I would most likely calculate the ~56% using Bayes and then tell the interviewer:
Without any knowledge about me, it could | Amazon interview question—probability of 2nd interview
This being an interview question, I don't believe there is a correct answer.
I would most likely calculate the ~56% using Bayes and then tell the interviewer:
Without any knowledge about me, it could be between 50% and 56%, but because I know me and my past, the pr... | Amazon interview question—probability of 2nd interview
This being an interview question, I don't believe there is a correct answer.
I would most likely calculate the ~56% using Bayes and then tell the interviewer:
Without any knowledge about me, it could |
1,031 | Amazon interview question—probability of 2nd interview | I think the answer is 50% - right at the beginning of the question. It's irrelevant what percentage of your friends feel. | Amazon interview question—probability of 2nd interview | I think the answer is 50% - right at the beginning of the question. It's irrelevant what percentage of your friends feel. | Amazon interview question—probability of 2nd interview
I think the answer is 50% - right at the beginning of the question. It's irrelevant what percentage of your friends feel. | Amazon interview question—probability of 2nd interview
I think the answer is 50% - right at the beginning of the question. It's irrelevant what percentage of your friends feel. |
1,032 | Amazon interview question—probability of 2nd interview | Mathematically
You're chances are 50%. This is because in the Venn diagram of Amazon Interviewees you fall into the Universal Set of ALL Interviewees, but not the set of 'Your friends'.
Had the question stated: 'One of your friends had a great interview. What is the percentage she'll get a second interview?' Then the... | Amazon interview question—probability of 2nd interview | Mathematically
You're chances are 50%. This is because in the Venn diagram of Amazon Interviewees you fall into the Universal Set of ALL Interviewees, but not the set of 'Your friends'.
Had the ques | Amazon interview question—probability of 2nd interview
Mathematically
You're chances are 50%. This is because in the Venn diagram of Amazon Interviewees you fall into the Universal Set of ALL Interviewees, but not the set of 'Your friends'.
Had the question stated: 'One of your friends had a great interview. What is ... | Amazon interview question—probability of 2nd interview
Mathematically
You're chances are 50%. This is because in the Venn diagram of Amazon Interviewees you fall into the Universal Set of ALL Interviewees, but not the set of 'Your friends'.
Had the ques |
1,033 | Amazon interview question—probability of 2nd interview | Answer is: ≈1
The question doesnt provide how many people among those appearing for interview,are our friends.However, we can assume that data & get any answer we want.Also, main thing about this assumption is that only our friends get selected for 2nd interview.
Lets say 104 of your friends appear for the interview,& ... | Amazon interview question—probability of 2nd interview | Answer is: ≈1
The question doesnt provide how many people among those appearing for interview,are our friends.However, we can assume that data & get any answer we want.Also, main thing about this assu | Amazon interview question—probability of 2nd interview
Answer is: ≈1
The question doesnt provide how many people among those appearing for interview,are our friends.However, we can assume that data & get any answer we want.Also, main thing about this assumption is that only our friends get selected for 2nd interview.
L... | Amazon interview question—probability of 2nd interview
Answer is: ≈1
The question doesnt provide how many people among those appearing for interview,are our friends.However, we can assume that data & get any answer we want.Also, main thing about this assu |
1,034 | Why L1 norm for sparse models | Consider the vector $\vec{x}=(1,\varepsilon)\in\mathbb{R}^2$ where $\varepsilon>0$ is small. The $l_1$ and $l_2$ norms of $\vec{x}$, respectively, are given by
$$||\vec{x}||_1 = 1+\varepsilon,\ \ ||\vec{x}||_2^2 = 1+\varepsilon^2$$
Now say that, as part of some regularization procedure, we are going to reduce the magni... | Why L1 norm for sparse models | Consider the vector $\vec{x}=(1,\varepsilon)\in\mathbb{R}^2$ where $\varepsilon>0$ is small. The $l_1$ and $l_2$ norms of $\vec{x}$, respectively, are given by
$$||\vec{x}||_1 = 1+\varepsilon,\ \ ||\v | Why L1 norm for sparse models
Consider the vector $\vec{x}=(1,\varepsilon)\in\mathbb{R}^2$ where $\varepsilon>0$ is small. The $l_1$ and $l_2$ norms of $\vec{x}$, respectively, are given by
$$||\vec{x}||_1 = 1+\varepsilon,\ \ ||\vec{x}||_2^2 = 1+\varepsilon^2$$
Now say that, as part of some regularization procedure, we... | Why L1 norm for sparse models
Consider the vector $\vec{x}=(1,\varepsilon)\in\mathbb{R}^2$ where $\varepsilon>0$ is small. The $l_1$ and $l_2$ norms of $\vec{x}$, respectively, are given by
$$||\vec{x}||_1 = 1+\varepsilon,\ \ ||\v |
1,035 | Why L1 norm for sparse models | With a sparse model, we think of a model where many of the weights are 0. Let us therefore reason about how L1-regularization is more likely to create 0-weights.
Consider a model consisting of the weights $(w_1, w_2, \dots, w_m)$.
With L1 regularization, you penalize the model by a loss function $L_1(w)$ = $\Sigma_i |w... | Why L1 norm for sparse models | With a sparse model, we think of a model where many of the weights are 0. Let us therefore reason about how L1-regularization is more likely to create 0-weights.
Consider a model consisting of the wei | Why L1 norm for sparse models
With a sparse model, we think of a model where many of the weights are 0. Let us therefore reason about how L1-regularization is more likely to create 0-weights.
Consider a model consisting of the weights $(w_1, w_2, \dots, w_m)$.
With L1 regularization, you penalize the model by a loss fu... | Why L1 norm for sparse models
With a sparse model, we think of a model where many of the weights are 0. Let us therefore reason about how L1-regularization is more likely to create 0-weights.
Consider a model consisting of the wei |
1,036 | Why L1 norm for sparse models | The Figure 3.11 from Elements of Statistical Learning by Hastie, Tibshirani, and Friedman is very illustrative:
Explanations: The $\hat{\beta}$ is the unconstrained least squares estimate. The red ellipses are (as explained in the caption of this Figure) the contours of the least squares error function, in terms of par... | Why L1 norm for sparse models | The Figure 3.11 from Elements of Statistical Learning by Hastie, Tibshirani, and Friedman is very illustrative:
Explanations: The $\hat{\beta}$ is the unconstrained least squares estimate. The red ell | Why L1 norm for sparse models
The Figure 3.11 from Elements of Statistical Learning by Hastie, Tibshirani, and Friedman is very illustrative:
Explanations: The $\hat{\beta}$ is the unconstrained least squares estimate. The red ellipses are (as explained in the caption of this Figure) the contours of the least squares e... | Why L1 norm for sparse models
The Figure 3.11 from Elements of Statistical Learning by Hastie, Tibshirani, and Friedman is very illustrative:
Explanations: The $\hat{\beta}$ is the unconstrained least squares estimate. The red ell |
1,037 | Why L1 norm for sparse models | Have a look on figure 3.11 (page 71) of The elements of statistical learning. It shows the position of a unconstrained $\hat \beta$ that minimizes the squared error function, the ellipses showing the levels of the square error function, and where are the $\hat \beta$ subject to constraints $\ell_1 (\hat \beta) < t$ and... | Why L1 norm for sparse models | Have a look on figure 3.11 (page 71) of The elements of statistical learning. It shows the position of a unconstrained $\hat \beta$ that minimizes the squared error function, the ellipses showing the | Why L1 norm for sparse models
Have a look on figure 3.11 (page 71) of The elements of statistical learning. It shows the position of a unconstrained $\hat \beta$ that minimizes the squared error function, the ellipses showing the levels of the square error function, and where are the $\hat \beta$ subject to constraints... | Why L1 norm for sparse models
Have a look on figure 3.11 (page 71) of The elements of statistical learning. It shows the position of a unconstrained $\hat \beta$ that minimizes the squared error function, the ellipses showing the |
1,038 | Why L1 norm for sparse models | The image shows the shapes of area occupied by L1 and L2 Norm. The second image consists of various Gradient Descent contours for various regression problems. In all the contour plots, observe the red circle which intersects the Ridge or L2 Norm. the intersection is not on the axes. The black circle in all the contours... | Why L1 norm for sparse models | The image shows the shapes of area occupied by L1 and L2 Norm. The second image consists of various Gradient Descent contours for various regression problems. In all the contour plots, observe the red | Why L1 norm for sparse models
The image shows the shapes of area occupied by L1 and L2 Norm. The second image consists of various Gradient Descent contours for various regression problems. In all the contour plots, observe the red circle which intersects the Ridge or L2 Norm. the intersection is not on the axes. The bl... | Why L1 norm for sparse models
The image shows the shapes of area occupied by L1 and L2 Norm. The second image consists of various Gradient Descent contours for various regression problems. In all the contour plots, observe the red |
1,039 | Why L1 norm for sparse models | A simple non mathematical answer wold be:
For L2: Penalty term is squared,so squaring a small value will make it smaller.
We don't have to make it zero to achieve our aim to get minimum square error, we will get it before that.
For L1: Penalty term is absolute,we might need to go to zero as there are no catalyst to mak... | Why L1 norm for sparse models | A simple non mathematical answer wold be:
For L2: Penalty term is squared,so squaring a small value will make it smaller.
We don't have to make it zero to achieve our aim to get minimum square error, | Why L1 norm for sparse models
A simple non mathematical answer wold be:
For L2: Penalty term is squared,so squaring a small value will make it smaller.
We don't have to make it zero to achieve our aim to get minimum square error, we will get it before that.
For L1: Penalty term is absolute,we might need to go to zero a... | Why L1 norm for sparse models
A simple non mathematical answer wold be:
For L2: Penalty term is squared,so squaring a small value will make it smaller.
We don't have to make it zero to achieve our aim to get minimum square error, |
1,040 | Why L1 norm for sparse models | I suggest you read some more about the theory of convex optimization. An answer to why the $ \ell_1 $ regularization achieves sparsity can be found if you examine implementations of models employing it, for example LASSO. One such method to solve the convex optimization problem with $ \ell_1 $ norm is by using the prox... | Why L1 norm for sparse models | I suggest you read some more about the theory of convex optimization. An answer to why the $ \ell_1 $ regularization achieves sparsity can be found if you examine implementations of models employing i | Why L1 norm for sparse models
I suggest you read some more about the theory of convex optimization. An answer to why the $ \ell_1 $ regularization achieves sparsity can be found if you examine implementations of models employing it, for example LASSO. One such method to solve the convex optimization problem with $ \ell... | Why L1 norm for sparse models
I suggest you read some more about the theory of convex optimization. An answer to why the $ \ell_1 $ regularization achieves sparsity can be found if you examine implementations of models employing i |
1,041 | Why L1 norm for sparse models | l2 regularizer does not change the value of weight vector from one iteration to another iteration because of the slope of l2 norm is reducing all the time where as l1 regularizer constantly reduce the value of weight vector towards optimal W* which is 0 because of the slopeod L1 norm is constant | Why L1 norm for sparse models | l2 regularizer does not change the value of weight vector from one iteration to another iteration because of the slope of l2 norm is reducing all the time where as l1 regularizer constantly reduce the | Why L1 norm for sparse models
l2 regularizer does not change the value of weight vector from one iteration to another iteration because of the slope of l2 norm is reducing all the time where as l1 regularizer constantly reduce the value of weight vector towards optimal W* which is 0 because of the slopeod L1 norm is co... | Why L1 norm for sparse models
l2 regularizer does not change the value of weight vector from one iteration to another iteration because of the slope of l2 norm is reducing all the time where as l1 regularizer constantly reduce the |
1,042 | Pearson's or Spearman's correlation with non-normal data | Pearson's correlation is a measure of the linear relationship between two continuous random variables. It does not assume normality although it does assume finite variances and finite covariance. When the variables are bivariate normal, Pearson's correlation provides a complete description of the association.
Spearman'... | Pearson's or Spearman's correlation with non-normal data | Pearson's correlation is a measure of the linear relationship between two continuous random variables. It does not assume normality although it does assume finite variances and finite covariance. When | Pearson's or Spearman's correlation with non-normal data
Pearson's correlation is a measure of the linear relationship between two continuous random variables. It does not assume normality although it does assume finite variances and finite covariance. When the variables are bivariate normal, Pearson's correlation prov... | Pearson's or Spearman's correlation with non-normal data
Pearson's correlation is a measure of the linear relationship between two continuous random variables. It does not assume normality although it does assume finite variances and finite covariance. When |
1,043 | Pearson's or Spearman's correlation with non-normal data | Don't forget Kendall's tau! Roger Newson has argued for the superiority of Kendall's τa over Spearman's correlation rS as a rank-based measure of correlation in a paper whose full text is now freely available online:
Newson R. Parameters behind "nonparametric" statistics: Kendall's tau,Somers' D and median differences.... | Pearson's or Spearman's correlation with non-normal data | Don't forget Kendall's tau! Roger Newson has argued for the superiority of Kendall's τa over Spearman's correlation rS as a rank-based measure of correlation in a paper whose full text is now freely a | Pearson's or Spearman's correlation with non-normal data
Don't forget Kendall's tau! Roger Newson has argued for the superiority of Kendall's τa over Spearman's correlation rS as a rank-based measure of correlation in a paper whose full text is now freely available online:
Newson R. Parameters behind "nonparametric" st... | Pearson's or Spearman's correlation with non-normal data
Don't forget Kendall's tau! Roger Newson has argued for the superiority of Kendall's τa over Spearman's correlation rS as a rank-based measure of correlation in a paper whose full text is now freely a |
1,044 | Pearson's or Spearman's correlation with non-normal data | From an applied perspective, I am more concerned with choosing an approach that summarises the relationship between two variables in a way that aligns with my research question. I think that determining a method for getting accurate standard errors and p-values is a question that should come second. Even if you chose n... | Pearson's or Spearman's correlation with non-normal data | From an applied perspective, I am more concerned with choosing an approach that summarises the relationship between two variables in a way that aligns with my research question. I think that determini | Pearson's or Spearman's correlation with non-normal data
From an applied perspective, I am more concerned with choosing an approach that summarises the relationship between two variables in a way that aligns with my research question. I think that determining a method for getting accurate standard errors and p-values i... | Pearson's or Spearman's correlation with non-normal data
From an applied perspective, I am more concerned with choosing an approach that summarises the relationship between two variables in a way that aligns with my research question. I think that determini |
1,045 | Pearson's or Spearman's correlation with non-normal data | Updated
The question asks us to choose between Pearson's and Spearman's method when normality is questioned. Restricted to this concern, I think the following paper should inform anyone's decision:
On the Effects of Non-Normality on the Distribution of the Sample Product-Moment
Correlation Coefficient (Kowalski, 1975... | Pearson's or Spearman's correlation with non-normal data | Updated
The question asks us to choose between Pearson's and Spearman's method when normality is questioned. Restricted to this concern, I think the following paper should inform anyone's decision:
| Pearson's or Spearman's correlation with non-normal data
Updated
The question asks us to choose between Pearson's and Spearman's method when normality is questioned. Restricted to this concern, I think the following paper should inform anyone's decision:
On the Effects of Non-Normality on the Distribution of the Samp... | Pearson's or Spearman's correlation with non-normal data
Updated
The question asks us to choose between Pearson's and Spearman's method when normality is questioned. Restricted to this concern, I think the following paper should inform anyone's decision:
|
1,046 | Pearson's or Spearman's correlation with non-normal data | I think these figures (of Gross-Error Sensitivity and Asymptotic Variance) and quotation from the below paper will make it a bit clear:
"The Kendall correlation measure is more robust and slightly more efficient than Spearman’s rank correlation, making it the preferable estimator from both perspectives."
Source:
Crou... | Pearson's or Spearman's correlation with non-normal data | I think these figures (of Gross-Error Sensitivity and Asymptotic Variance) and quotation from the below paper will make it a bit clear:
"The Kendall correlation measure is more robust and slightly m | Pearson's or Spearman's correlation with non-normal data
I think these figures (of Gross-Error Sensitivity and Asymptotic Variance) and quotation from the below paper will make it a bit clear:
"The Kendall correlation measure is more robust and slightly more efficient than Spearman’s rank correlation, making it the p... | Pearson's or Spearman's correlation with non-normal data
I think these figures (of Gross-Error Sensitivity and Asymptotic Variance) and quotation from the below paper will make it a bit clear:
"The Kendall correlation measure is more robust and slightly m |
1,047 | Pearson's or Spearman's correlation with non-normal data | Even though this is an age old question, I would like to contribute the (cool) observation that Pearson's $\rho$ is nothing but the slope of the trend line between $Y$ and $X$ after means have been removed and the scales are normalized for $\sigma_Y$, i.e. after removing means and normalizing for $\sigma_Y$, Pearson's ... | Pearson's or Spearman's correlation with non-normal data | Even though this is an age old question, I would like to contribute the (cool) observation that Pearson's $\rho$ is nothing but the slope of the trend line between $Y$ and $X$ after means have been re | Pearson's or Spearman's correlation with non-normal data
Even though this is an age old question, I would like to contribute the (cool) observation that Pearson's $\rho$ is nothing but the slope of the trend line between $Y$ and $X$ after means have been removed and the scales are normalized for $\sigma_Y$, i.e. after ... | Pearson's or Spearman's correlation with non-normal data
Even though this is an age old question, I would like to contribute the (cool) observation that Pearson's $\rho$ is nothing but the slope of the trend line between $Y$ and $X$ after means have been re |
1,048 | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? | Subtracting the dataset mean serves to "center" the data. Additionally, you ideally would like to divide by the sttdev of that feature or pixel as well if you want to normalize each feature value to a z-score.
The reason we do both of those things is because in the process of training our network, we're going to be mu... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep | Subtracting the dataset mean serves to "center" the data. Additionally, you ideally would like to divide by the sttdev of that feature or pixel as well if you want to normalize each feature value to a | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning?
Subtracting the dataset mean serves to "center" the data. Additionally, you ideally would like to divide by the sttdev of that feature or pixel as well if you want to normalize each feature value to a z-score. ... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep
Subtracting the dataset mean serves to "center" the data. Additionally, you ideally would like to divide by the sttdev of that feature or pixel as well if you want to normalize each feature value to a |
1,049 | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? | Prior to batch normalization, mean subtraction per channel was used to center the data around zero mean for each channel (R, G, B). This typically helps the network to learn faster since gradients act uniformly for each channel. I suspect if you use batch normalization, the per channel mean subtraction pre-processing s... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep | Prior to batch normalization, mean subtraction per channel was used to center the data around zero mean for each channel (R, G, B). This typically helps the network to learn faster since gradients act | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning?
Prior to batch normalization, mean subtraction per channel was used to center the data around zero mean for each channel (R, G, B). This typically helps the network to learn faster since gradients act uniformly... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep
Prior to batch normalization, mean subtraction per channel was used to center the data around zero mean for each channel (R, G, B). This typically helps the network to learn faster since gradients act |
1,050 | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? | Per-image normalization is common and is even the only in-built function currently in Tensorflow (primarily due to being very easy to implement). It is used for the exact reason you mentioned (day VS night for the same image). However, if you imagine a more ideal scenario where lighting was controlled, then the relat... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep | Per-image normalization is common and is even the only in-built function currently in Tensorflow (primarily due to being very easy to implement). It is used for the exact reason you mentioned (day VS | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning?
Per-image normalization is common and is even the only in-built function currently in Tensorflow (primarily due to being very easy to implement). It is used for the exact reason you mentioned (day VS night for... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep
Per-image normalization is common and is even the only in-built function currently in Tensorflow (primarily due to being very easy to implement). It is used for the exact reason you mentioned (day VS |
1,051 | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? | There are two aspects to this topic:
Normalization to keep all data in the same scale --> the outcome is going to be similar when normalizing both on a per-image basis or across the entire image data set
Preservation of relative information --> this is where doing normalization on a per-image or per-set basis makes a ... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep | There are two aspects to this topic:
Normalization to keep all data in the same scale --> the outcome is going to be similar when normalizing both on a per-image basis or across the entire image data | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning?
There are two aspects to this topic:
Normalization to keep all data in the same scale --> the outcome is going to be similar when normalizing both on a per-image basis or across the entire image data set
Prese... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep
There are two aspects to this topic:
Normalization to keep all data in the same scale --> the outcome is going to be similar when normalizing both on a per-image basis or across the entire image data |
1,052 | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? | This is called preprocessing of data before using it. You can process in many ways but there is one condition that you should process each data with same function X_preproc = f(X) and this f(.) should not depend on data itself, so if you use the current image mean to process this current image then your f(X) will actua... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep | This is called preprocessing of data before using it. You can process in many ways but there is one condition that you should process each data with same function X_preproc = f(X) and this f(.) should | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning?
This is called preprocessing of data before using it. You can process in many ways but there is one condition that you should process each data with same function X_preproc = f(X) and this f(.) should not depen... | Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep
This is called preprocessing of data before using it. You can process in many ways but there is one condition that you should process each data with same function X_preproc = f(X) and this f(.) should |
1,053 | Batch gradient descent versus stochastic gradient descent | The applicability of batch or stochastic gradient descent really depends on the error manifold expected.
Batch gradient descent computes the gradient using the whole dataset. This is great for convex, or relatively smooth error manifolds. In this case, we move somewhat directly towards an optimum solution, either loc... | Batch gradient descent versus stochastic gradient descent | The applicability of batch or stochastic gradient descent really depends on the error manifold expected.
Batch gradient descent computes the gradient using the whole dataset. This is great for convex | Batch gradient descent versus stochastic gradient descent
The applicability of batch or stochastic gradient descent really depends on the error manifold expected.
Batch gradient descent computes the gradient using the whole dataset. This is great for convex, or relatively smooth error manifolds. In this case, we move... | Batch gradient descent versus stochastic gradient descent
The applicability of batch or stochastic gradient descent really depends on the error manifold expected.
Batch gradient descent computes the gradient using the whole dataset. This is great for convex |
1,054 | Batch gradient descent versus stochastic gradient descent | As other answer suggests, the main reason to use SGD is to reduce the computation cost of gradient while still largely maintaining the gradient direction when averaged over many mini-batches or samples - that surely helps bring you to the local minima.
Why minibatch works.
The mathematics behind this is that, the "tr... | Batch gradient descent versus stochastic gradient descent | As other answer suggests, the main reason to use SGD is to reduce the computation cost of gradient while still largely maintaining the gradient direction when averaged over many mini-batches or sample | Batch gradient descent versus stochastic gradient descent
As other answer suggests, the main reason to use SGD is to reduce the computation cost of gradient while still largely maintaining the gradient direction when averaged over many mini-batches or samples - that surely helps bring you to the local minima.
Why mini... | Batch gradient descent versus stochastic gradient descent
As other answer suggests, the main reason to use SGD is to reduce the computation cost of gradient while still largely maintaining the gradient direction when averaged over many mini-batches or sample |
1,055 | Batch gradient descent versus stochastic gradient descent | To me, batch gradient resembles lean gradient. In lean gradient, the batch size is chosen so every parameter that shall be updated, is also varied independently, but not necessarily orthogonally, in the batch. For example, if the batch contains 10 experiments, 10 rows, then it is possible to form $2^{10-1} = 512$ inde... | Batch gradient descent versus stochastic gradient descent | To me, batch gradient resembles lean gradient. In lean gradient, the batch size is chosen so every parameter that shall be updated, is also varied independently, but not necessarily orthogonally, in t | Batch gradient descent versus stochastic gradient descent
To me, batch gradient resembles lean gradient. In lean gradient, the batch size is chosen so every parameter that shall be updated, is also varied independently, but not necessarily orthogonally, in the batch. For example, if the batch contains 10 experiments, ... | Batch gradient descent versus stochastic gradient descent
To me, batch gradient resembles lean gradient. In lean gradient, the batch size is chosen so every parameter that shall be updated, is also varied independently, but not necessarily orthogonally, in t |
1,056 | Batch gradient descent versus stochastic gradient descent | If you want to see the differences in a formula, this might help.
In above equation, m indicates the number of training data points.
In Batch Gradient Descent, As the yellow circle shows, in order to calculate the gradient of the cost function, we add up the cost of each sample. If we have 3 million samples, we have t... | Batch gradient descent versus stochastic gradient descent | If you want to see the differences in a formula, this might help.
In above equation, m indicates the number of training data points.
In Batch Gradient Descent, As the yellow circle shows, in order to | Batch gradient descent versus stochastic gradient descent
If you want to see the differences in a formula, this might help.
In above equation, m indicates the number of training data points.
In Batch Gradient Descent, As the yellow circle shows, in order to calculate the gradient of the cost function, we add up the co... | Batch gradient descent versus stochastic gradient descent
If you want to see the differences in a formula, this might help.
In above equation, m indicates the number of training data points.
In Batch Gradient Descent, As the yellow circle shows, in order to |
1,057 | Batch gradient descent versus stochastic gradient descent | Imagine you are going down the hill to a valley of minimum height. You may use batch gradient descent to calculate the direction to the valley once and just go there. But on that direction you may have an up hill. It's better to avoid it, and this is what stochastic gradient descent idea is about. Sometimes is better t... | Batch gradient descent versus stochastic gradient descent | Imagine you are going down the hill to a valley of minimum height. You may use batch gradient descent to calculate the direction to the valley once and just go there. But on that direction you may hav | Batch gradient descent versus stochastic gradient descent
Imagine you are going down the hill to a valley of minimum height. You may use batch gradient descent to calculate the direction to the valley once and just go there. But on that direction you may have an up hill. It's better to avoid it, and this is what stocha... | Batch gradient descent versus stochastic gradient descent
Imagine you are going down the hill to a valley of minimum height. You may use batch gradient descent to calculate the direction to the valley once and just go there. But on that direction you may hav |
1,058 | Batch gradient descent versus stochastic gradient descent | As mentioned in the above answers, the noise in stochastic gradient descent helps you escape "bad" stationary points. For example, you can see how gradient descent (in pink) gets stuck in a saddle point while stochastic gradient descent (in yellow) escapes it. This picture is taken from here. | Batch gradient descent versus stochastic gradient descent | As mentioned in the above answers, the noise in stochastic gradient descent helps you escape "bad" stationary points. For example, you can see how gradient descent (in pink) gets stuck in a saddle poi | Batch gradient descent versus stochastic gradient descent
As mentioned in the above answers, the noise in stochastic gradient descent helps you escape "bad" stationary points. For example, you can see how gradient descent (in pink) gets stuck in a saddle point while stochastic gradient descent (in yellow) escapes it. T... | Batch gradient descent versus stochastic gradient descent
As mentioned in the above answers, the noise in stochastic gradient descent helps you escape "bad" stationary points. For example, you can see how gradient descent (in pink) gets stuck in a saddle poi |
1,059 | Correlations with unordered categorical variables | It depends on what sense of a correlation you want. When you run the prototypical Pearson's product moment correlation, you get a measure of the strength of association and you get a test of the significance of that association. More typically however, the significance test and the measure of effect size differ.
Si... | Correlations with unordered categorical variables | It depends on what sense of a correlation you want. When you run the prototypical Pearson's product moment correlation, you get a measure of the strength of association and you get a test of the sign | Correlations with unordered categorical variables
It depends on what sense of a correlation you want. When you run the prototypical Pearson's product moment correlation, you get a measure of the strength of association and you get a test of the significance of that association. More typically however, the significanc... | Correlations with unordered categorical variables
It depends on what sense of a correlation you want. When you run the prototypical Pearson's product moment correlation, you get a measure of the strength of association and you get a test of the sign |
1,060 | Correlations with unordered categorical variables | I've seen the following cheatsheet linked before:
https://stats.idre.ucla.edu/other/mult-pkg/whatstat/
It may be useful to you. It even has links to specific R libraries. | Correlations with unordered categorical variables | I've seen the following cheatsheet linked before:
https://stats.idre.ucla.edu/other/mult-pkg/whatstat/
It may be useful to you. It even has links to specific R libraries. | Correlations with unordered categorical variables
I've seen the following cheatsheet linked before:
https://stats.idre.ucla.edu/other/mult-pkg/whatstat/
It may be useful to you. It even has links to specific R libraries. | Correlations with unordered categorical variables
I've seen the following cheatsheet linked before:
https://stats.idre.ucla.edu/other/mult-pkg/whatstat/
It may be useful to you. It even has links to specific R libraries. |
1,061 | Correlations with unordered categorical variables | If you want a correlation matrix of categorical variables, you can use the following wrapper function (requiring the 'vcd' package):
catcorrm <- function(vars, dat) sapply(vars, function(y) sapply(vars, function(x) assocstats(table(dat[,x], dat[,y]))$cramer))
Where:
vars is a string vector of categorical variables you... | Correlations with unordered categorical variables | If you want a correlation matrix of categorical variables, you can use the following wrapper function (requiring the 'vcd' package):
catcorrm <- function(vars, dat) sapply(vars, function(y) sapply(var | Correlations with unordered categorical variables
If you want a correlation matrix of categorical variables, you can use the following wrapper function (requiring the 'vcd' package):
catcorrm <- function(vars, dat) sapply(vars, function(y) sapply(vars, function(x) assocstats(table(dat[,x], dat[,y]))$cramer))
Where:
va... | Correlations with unordered categorical variables
If you want a correlation matrix of categorical variables, you can use the following wrapper function (requiring the 'vcd' package):
catcorrm <- function(vars, dat) sapply(vars, function(y) sapply(var |
1,062 | Correlations with unordered categorical variables | Depends on what you want to achieve. Let $X$ be the continuous, numerical variable and $K$ the (unordered) categorical variable. Then one possible approach is to assign numerical scores $t_i$ to each of the possible values of $K$, $i=1, \dots, p$. One possible criterion is to maximize the correlation between the $X$ ... | Correlations with unordered categorical variables | Depends on what you want to achieve. Let $X$ be the continuous, numerical variable and $K$ the (unordered) categorical variable. Then one possible approach is to assign numerical scores $t_i$ to eac | Correlations with unordered categorical variables
Depends on what you want to achieve. Let $X$ be the continuous, numerical variable and $K$ the (unordered) categorical variable. Then one possible approach is to assign numerical scores $t_i$ to each of the possible values of $K$, $i=1, \dots, p$. One possible criteri... | Correlations with unordered categorical variables
Depends on what you want to achieve. Let $X$ be the continuous, numerical variable and $K$ the (unordered) categorical variable. Then one possible approach is to assign numerical scores $t_i$ to eac |
1,063 | Correlations with unordered categorical variables | I had a similar problem and I tried the Chi-squared-Test as suggested but I got very confused in assessing the P-Values against NULL Hypothesis.
I will explain how I interpreted categorical variables. I am not sure how relevant it is in your case. I had Response Variable Y and two Predictor Variables X1 and X2 where X... | Correlations with unordered categorical variables | I had a similar problem and I tried the Chi-squared-Test as suggested but I got very confused in assessing the P-Values against NULL Hypothesis.
I will explain how I interpreted categorical variables | Correlations with unordered categorical variables
I had a similar problem and I tried the Chi-squared-Test as suggested but I got very confused in assessing the P-Values against NULL Hypothesis.
I will explain how I interpreted categorical variables. I am not sure how relevant it is in your case. I had Response Variab... | Correlations with unordered categorical variables
I had a similar problem and I tried the Chi-squared-Test as suggested but I got very confused in assessing the P-Values against NULL Hypothesis.
I will explain how I interpreted categorical variables |
1,064 | Correlations with unordered categorical variables | To measure the link strength between two categorical variable i would rather suggest the use of a cross tab with the chisquare stat
to measure the link strength between a numerical and a categorical variable you can use a mean comparison to see if it change significally from one category to an others | Correlations with unordered categorical variables | To measure the link strength between two categorical variable i would rather suggest the use of a cross tab with the chisquare stat
to measure the link strength between a numerical and a categorical v | Correlations with unordered categorical variables
To measure the link strength between two categorical variable i would rather suggest the use of a cross tab with the chisquare stat
to measure the link strength between a numerical and a categorical variable you can use a mean comparison to see if it change significally... | Correlations with unordered categorical variables
To measure the link strength between two categorical variable i would rather suggest the use of a cross tab with the chisquare stat
to measure the link strength between a numerical and a categorical v |
1,065 | What's the difference between probability and statistics? | The short answer to this I've heard from Persi Diaconis is the following:
The problems considered by probability and statistics are inverse to each other. In probability theory we consider some underlying process which has some randomness or uncertainty modeled by random variables, and we figure out what happens. In ... | What's the difference between probability and statistics? | The short answer to this I've heard from Persi Diaconis is the following:
The problems considered by probability and statistics are inverse to each other. In probability theory we consider some under | What's the difference between probability and statistics?
The short answer to this I've heard from Persi Diaconis is the following:
The problems considered by probability and statistics are inverse to each other. In probability theory we consider some underlying process which has some randomness or uncertainty modeled... | What's the difference between probability and statistics?
The short answer to this I've heard from Persi Diaconis is the following:
The problems considered by probability and statistics are inverse to each other. In probability theory we consider some under |
1,066 | What's the difference between probability and statistics? | I like the example of a jar of red and green jelly beans.
A probabilist starts by knowing the proportion of each and asks the probability of drawing a red jelly bean. A statistician infers the proportion of red jelly beans by sampling from the jar. | What's the difference between probability and statistics? | I like the example of a jar of red and green jelly beans.
A probabilist starts by knowing the proportion of each and asks the probability of drawing a red jelly bean. A statistician infers the prop | What's the difference between probability and statistics?
I like the example of a jar of red and green jelly beans.
A probabilist starts by knowing the proportion of each and asks the probability of drawing a red jelly bean. A statistician infers the proportion of red jelly beans by sampling from the jar. | What's the difference between probability and statistics?
I like the example of a jar of red and green jelly beans.
A probabilist starts by knowing the proportion of each and asks the probability of drawing a red jelly bean. A statistician infers the prop |
1,067 | What's the difference between probability and statistics? | It's misleading to simply say that statistics is simply the inverse of probability. Yes, statistical questions are questions of inverse probability, but they are ill-posed inverse problems, and this makes a big difference in terms of how they are addressed.
Probability is a branch of pure mathematics--probability ques... | What's the difference between probability and statistics? | It's misleading to simply say that statistics is simply the inverse of probability. Yes, statistical questions are questions of inverse probability, but they are ill-posed inverse problems, and this | What's the difference between probability and statistics?
It's misleading to simply say that statistics is simply the inverse of probability. Yes, statistical questions are questions of inverse probability, but they are ill-posed inverse problems, and this makes a big difference in terms of how they are addressed.
Pro... | What's the difference between probability and statistics?
It's misleading to simply say that statistics is simply the inverse of probability. Yes, statistical questions are questions of inverse probability, but they are ill-posed inverse problems, and this |
1,068 | What's the difference between probability and statistics? | I like this from Steve Skienna's Calculated Bets (see the link for complete discussion):
In summary, probability theory enables us to find the consequences of a given ideal world, while statistical theory enables us to to measure the extent to which our world is ideal. | What's the difference between probability and statistics? | I like this from Steve Skienna's Calculated Bets (see the link for complete discussion):
In summary, probability theory enables us to find the consequences of a given ideal world, while statistical t | What's the difference between probability and statistics?
I like this from Steve Skienna's Calculated Bets (see the link for complete discussion):
In summary, probability theory enables us to find the consequences of a given ideal world, while statistical theory enables us to to measure the extent to which our world i... | What's the difference between probability and statistics?
I like this from Steve Skienna's Calculated Bets (see the link for complete discussion):
In summary, probability theory enables us to find the consequences of a given ideal world, while statistical t |
1,069 | What's the difference between probability and statistics? | Table 3.1 of Intuitive Biostatistics answers this question with the diagram shown below. Note that all the arrows point to the right for probability, and point to the left for statistics.
PROBABILITY
General ---> Specific
Population ---> Sample
Model ---> Data
STATISTICS
General <--- Specific
Population <--- Sample
... | What's the difference between probability and statistics? | Table 3.1 of Intuitive Biostatistics answers this question with the diagram shown below. Note that all the arrows point to the right for probability, and point to the left for statistics.
PROBABILITY
| What's the difference between probability and statistics?
Table 3.1 of Intuitive Biostatistics answers this question with the diagram shown below. Note that all the arrows point to the right for probability, and point to the left for statistics.
PROBABILITY
General ---> Specific
Population ---> Sample
Model ---> Data
... | What's the difference between probability and statistics?
Table 3.1 of Intuitive Biostatistics answers this question with the diagram shown below. Note that all the arrows point to the right for probability, and point to the left for statistics.
PROBABILITY
|
1,070 | What's the difference between probability and statistics? | Probability is a pure science (math), statistics is about data. They are connected since probability forms some kind of fundament for statistics, providing basic ideas. | What's the difference between probability and statistics? | Probability is a pure science (math), statistics is about data. They are connected since probability forms some kind of fundament for statistics, providing basic ideas. | What's the difference between probability and statistics?
Probability is a pure science (math), statistics is about data. They are connected since probability forms some kind of fundament for statistics, providing basic ideas. | What's the difference between probability and statistics?
Probability is a pure science (math), statistics is about data. They are connected since probability forms some kind of fundament for statistics, providing basic ideas. |
1,071 | What's the difference between probability and statistics? | Probability answers questions about what will happen, statistics answers questions about what did happen. | What's the difference between probability and statistics? | Probability answers questions about what will happen, statistics answers questions about what did happen. | What's the difference between probability and statistics?
Probability answers questions about what will happen, statistics answers questions about what did happen. | What's the difference between probability and statistics?
Probability answers questions about what will happen, statistics answers questions about what did happen. |
1,072 | What's the difference between probability and statistics? | Probability is about quantifying uncertainty whereas statistics is explaining the variation in some measure of interest (e.g., why do income levels vary?) that we observe in the real world.
We explain the variation by using some observable factors (e.g., gender, education level, age etc for the income example). Howeve... | What's the difference between probability and statistics? | Probability is about quantifying uncertainty whereas statistics is explaining the variation in some measure of interest (e.g., why do income levels vary?) that we observe in the real world.
We explai | What's the difference between probability and statistics?
Probability is about quantifying uncertainty whereas statistics is explaining the variation in some measure of interest (e.g., why do income levels vary?) that we observe in the real world.
We explain the variation by using some observable factors (e.g., gender... | What's the difference between probability and statistics?
Probability is about quantifying uncertainty whereas statistics is explaining the variation in some measure of interest (e.g., why do income levels vary?) that we observe in the real world.
We explai |
1,073 | What's the difference between probability and statistics? | Probability is the embrace of uncertainty, while statistics is an empirical, ravenous pursuit of the truth (damned liars excluded, of course). | What's the difference between probability and statistics? | Probability is the embrace of uncertainty, while statistics is an empirical, ravenous pursuit of the truth (damned liars excluded, of course). | What's the difference between probability and statistics?
Probability is the embrace of uncertainty, while statistics is an empirical, ravenous pursuit of the truth (damned liars excluded, of course). | What's the difference between probability and statistics?
Probability is the embrace of uncertainty, while statistics is an empirical, ravenous pursuit of the truth (damned liars excluded, of course). |
1,074 | What's the difference between probability and statistics? | Similar to what Mark said, Statistics was historically called Inverse Probability, since statistics tries to infer the causes of an event given the observations, while probability tends to be the other way around. | What's the difference between probability and statistics? | Similar to what Mark said, Statistics was historically called Inverse Probability, since statistics tries to infer the causes of an event given the observations, while probability tends to be the othe | What's the difference between probability and statistics?
Similar to what Mark said, Statistics was historically called Inverse Probability, since statistics tries to infer the causes of an event given the observations, while probability tends to be the other way around. | What's the difference between probability and statistics?
Similar to what Mark said, Statistics was historically called Inverse Probability, since statistics tries to infer the causes of an event given the observations, while probability tends to be the othe |
1,075 | What's the difference between probability and statistics? | The probability of an event is its long-run relative frequency. So it's basically telling you the chance of, for example, getting a 'head' on the next flip of a coin, or getting a '3' on the next roll of a die.
A statistic is any numerical measure computed from a sample of the population. For example, the sample mean. ... | What's the difference between probability and statistics? | The probability of an event is its long-run relative frequency. So it's basically telling you the chance of, for example, getting a 'head' on the next flip of a coin, or getting a '3' on the next roll | What's the difference between probability and statistics?
The probability of an event is its long-run relative frequency. So it's basically telling you the chance of, for example, getting a 'head' on the next flip of a coin, or getting a '3' on the next roll of a die.
A statistic is any numerical measure computed from ... | What's the difference between probability and statistics?
The probability of an event is its long-run relative frequency. So it's basically telling you the chance of, for example, getting a 'head' on the next flip of a coin, or getting a '3' on the next roll |
1,076 | What's the difference between probability and statistics? | Probability studies, well, how probable events are. You intuitively know what probability is.
Statistics is the study of data: showing it (using tools such as charts), summarizing it (using means and standard deviations etc.), reaching conclusions about the world from which that data was drawn (fitting lines to data et... | What's the difference between probability and statistics? | Probability studies, well, how probable events are. You intuitively know what probability is.
Statistics is the study of data: showing it (using tools such as charts), summarizing it (using means and | What's the difference between probability and statistics?
Probability studies, well, how probable events are. You intuitively know what probability is.
Statistics is the study of data: showing it (using tools such as charts), summarizing it (using means and standard deviations etc.), reaching conclusions about the worl... | What's the difference between probability and statistics?
Probability studies, well, how probable events are. You intuitively know what probability is.
Statistics is the study of data: showing it (using tools such as charts), summarizing it (using means and |
1,077 | What's the difference between probability and statistics? | In probability theory, we are given random variables X1, X2, ... in some way, and then we study their properties, i.e. calculate probability P{ X1 \in B1 }, study the convergence of X1, X2, ... etc.
In mathematical statistics, we are given n realizations of some random variable X, and set of distributions D; the proble... | What's the difference between probability and statistics? | In probability theory, we are given random variables X1, X2, ... in some way, and then we study their properties, i.e. calculate probability P{ X1 \in B1 }, study the convergence of X1, X2, ... etc.
I | What's the difference between probability and statistics?
In probability theory, we are given random variables X1, X2, ... in some way, and then we study their properties, i.e. calculate probability P{ X1 \in B1 }, study the convergence of X1, X2, ... etc.
In mathematical statistics, we are given n realizations of some... | What's the difference between probability and statistics?
In probability theory, we are given random variables X1, X2, ... in some way, and then we study their properties, i.e. calculate probability P{ X1 \in B1 }, study the convergence of X1, X2, ... etc.
I |
1,078 | What's the difference between probability and statistics? | In probability, the distribution is known and knowable in advance - you start with a known probability distribution function (or similar), and sample from it.
In statistics, the distribution is unknown in advance. It may even be unknowable. Assumptions are hypothesised about the probability distribution behind observed... | What's the difference between probability and statistics? | In probability, the distribution is known and knowable in advance - you start with a known probability distribution function (or similar), and sample from it.
In statistics, the distribution is unknow | What's the difference between probability and statistics?
In probability, the distribution is known and knowable in advance - you start with a known probability distribution function (or similar), and sample from it.
In statistics, the distribution is unknown in advance. It may even be unknowable. Assumptions are hypot... | What's the difference between probability and statistics?
In probability, the distribution is known and knowable in advance - you start with a known probability distribution function (or similar), and sample from it.
In statistics, the distribution is unknow |
1,079 | What's the difference between probability and statistics? | Probability: Given known parameters, find the probability of observing a particular set of data.
Statistics: Given a particular set of observed data, make an inference about what the parameters might be.
Statistics is "more subjective" and "more art than science" (relative to probability).
$$$$
$$\underline{\text{... | What's the difference between probability and statistics? | Probability: Given known parameters, find the probability of observing a particular set of data.
Statistics: Given a particular set of observed data, make an inference about what the parameters might | What's the difference between probability and statistics?
Probability: Given known parameters, find the probability of observing a particular set of data.
Statistics: Given a particular set of observed data, make an inference about what the parameters might be.
Statistics is "more subjective" and "more art than scien... | What's the difference between probability and statistics?
Probability: Given known parameters, find the probability of observing a particular set of data.
Statistics: Given a particular set of observed data, make an inference about what the parameters might |
1,080 | What's the difference between probability and statistics? | Statistics is the pursuit of truth in the face of uncertainty. Probability is the tool that allows us to quantify uncertainty.
(I have provided another, longer, answer that assumed that what was being asked was something along the lines of "how would you explain it to your grandmother?") | What's the difference between probability and statistics? | Statistics is the pursuit of truth in the face of uncertainty. Probability is the tool that allows us to quantify uncertainty.
(I have provided another, longer, answer that assumed that what was being | What's the difference between probability and statistics?
Statistics is the pursuit of truth in the face of uncertainty. Probability is the tool that allows us to quantify uncertainty.
(I have provided another, longer, answer that assumed that what was being asked was something along the lines of "how would you explain... | What's the difference between probability and statistics?
Statistics is the pursuit of truth in the face of uncertainty. Probability is the tool that allows us to quantify uncertainty.
(I have provided another, longer, answer that assumed that what was being |
1,081 | What's the difference between probability and statistics? | Answer #1: Statistics is parametrized Probability.
Any book on measure-theoretic Probability will tell you about the Probability triplet: $(\Omega, \mathcal F, P)$. But if you're doing Statistics, you have to add $\theta$ to the above: $(\Omega, \mathcal F, P_\theta)$, i.e. for different values of $\theta$, you get di... | What's the difference between probability and statistics? | Answer #1: Statistics is parametrized Probability.
Any book on measure-theoretic Probability will tell you about the Probability triplet: $(\Omega, \mathcal F, P)$. But if you're doing Statistics, yo | What's the difference between probability and statistics?
Answer #1: Statistics is parametrized Probability.
Any book on measure-theoretic Probability will tell you about the Probability triplet: $(\Omega, \mathcal F, P)$. But if you're doing Statistics, you have to add $\theta$ to the above: $(\Omega, \mathcal F, P_\... | What's the difference between probability and statistics?
Answer #1: Statistics is parametrized Probability.
Any book on measure-theoretic Probability will tell you about the Probability triplet: $(\Omega, \mathcal F, P)$. But if you're doing Statistics, yo |
1,082 | What's the difference between probability and statistics? | The difference between probabilities and statistics is that in probabilities there is no mistake. We are sure for the probability because we know exactly how many sides has a coin, or how many blue caramels are in the vase. But in statistics we examine a piece of a population of whatever we examine, and from this, we t... | What's the difference between probability and statistics? | The difference between probabilities and statistics is that in probabilities there is no mistake. We are sure for the probability because we know exactly how many sides has a coin, or how many blue ca | What's the difference between probability and statistics?
The difference between probabilities and statistics is that in probabilities there is no mistake. We are sure for the probability because we know exactly how many sides has a coin, or how many blue caramels are in the vase. But in statistics we examine a piece o... | What's the difference between probability and statistics?
The difference between probabilities and statistics is that in probabilities there is no mistake. We are sure for the probability because we know exactly how many sides has a coin, or how many blue ca |
1,083 | What's the difference between probability and statistics? | Savage's text Foundations of Statistics has been cited over 12000 times on Google Scholar.[3] It tells the following.
It is unanimously agreed that statistics depends somehow on probability. But, as to what probability is and how it is connected with statistics, there has seldom been such complete disagreement and bre... | What's the difference between probability and statistics? | Savage's text Foundations of Statistics has been cited over 12000 times on Google Scholar.[3] It tells the following.
It is unanimously agreed that statistics depends somehow on probability. But, as | What's the difference between probability and statistics?
Savage's text Foundations of Statistics has been cited over 12000 times on Google Scholar.[3] It tells the following.
It is unanimously agreed that statistics depends somehow on probability. But, as to what probability is and how it is connected with statistics... | What's the difference between probability and statistics?
Savage's text Foundations of Statistics has been cited over 12000 times on Google Scholar.[3] It tells the following.
It is unanimously agreed that statistics depends somehow on probability. But, as |
1,084 | What's the difference between probability and statistics? | The term "statistics" is beautifully explained by J. C. Maxwell in the article Molecules (in Nature 8, 1873, pp. 437–441). Let me quote the relevant passage:
When the working members of Section F get hold of a Report of the Census, or any other document containing the numerical data of Economic and Social Science, the... | What's the difference between probability and statistics? | The term "statistics" is beautifully explained by J. C. Maxwell in the article Molecules (in Nature 8, 1873, pp. 437–441). Let me quote the relevant passage:
When the working members of Section F get | What's the difference between probability and statistics?
The term "statistics" is beautifully explained by J. C. Maxwell in the article Molecules (in Nature 8, 1873, pp. 437–441). Let me quote the relevant passage:
When the working members of Section F get hold of a Report of the Census, or any other document contain... | What's the difference between probability and statistics?
The term "statistics" is beautifully explained by J. C. Maxwell in the article Molecules (in Nature 8, 1873, pp. 437–441). Let me quote the relevant passage:
When the working members of Section F get |
1,085 | What's the difference between probability and statistics? | Many people and mathematicians say that 'STATISTICS is the inverse of PROBABILITY',but its not particularly right. The way of approaching or method of solving these 2 are completely different but they are INTERCONNECTED.
i will like to refer to my friend John D Cook.....
"I like the example of a jar of red and green j... | What's the difference between probability and statistics? | Many people and mathematicians say that 'STATISTICS is the inverse of PROBABILITY',but its not particularly right. The way of approaching or method of solving these 2 are completely different but they | What's the difference between probability and statistics?
Many people and mathematicians say that 'STATISTICS is the inverse of PROBABILITY',but its not particularly right. The way of approaching or method of solving these 2 are completely different but they are INTERCONNECTED.
i will like to refer to my friend John D... | What's the difference between probability and statistics?
Many people and mathematicians say that 'STATISTICS is the inverse of PROBABILITY',but its not particularly right. The way of approaching or method of solving these 2 are completely different but they |
1,086 | What is the difference between linear regression on y with x and x with y? | The best way to think about this is to imagine a scatterplot of points with $y$ on the vertical axis and $x$ represented by the horizontal axis. Given this framework, you see a cloud of points, which may be vaguely circular, or may be elongated into an ellipse. What you are trying to do in regression is find what mig... | What is the difference between linear regression on y with x and x with y? | The best way to think about this is to imagine a scatterplot of points with $y$ on the vertical axis and $x$ represented by the horizontal axis. Given this framework, you see a cloud of points, which | What is the difference between linear regression on y with x and x with y?
The best way to think about this is to imagine a scatterplot of points with $y$ on the vertical axis and $x$ represented by the horizontal axis. Given this framework, you see a cloud of points, which may be vaguely circular, or may be elongated... | What is the difference between linear regression on y with x and x with y?
The best way to think about this is to imagine a scatterplot of points with $y$ on the vertical axis and $x$ represented by the horizontal axis. Given this framework, you see a cloud of points, which |
1,087 | What is the difference between linear regression on y with x and x with y? | I'm going to illustrate the answer with some R code and output.
First, we construct a random normal distribution, y, with a mean of 5 and a SD of 1:
y <- rnorm(1000, mean=5, sd=1)
Next, I purposely create a second random normal distribution, x, which is simply 5x the value of y for each y:
x <- y*5
By design, we have... | What is the difference between linear regression on y with x and x with y? | I'm going to illustrate the answer with some R code and output.
First, we construct a random normal distribution, y, with a mean of 5 and a SD of 1:
y <- rnorm(1000, mean=5, sd=1)
Next, I purposely c | What is the difference between linear regression on y with x and x with y?
I'm going to illustrate the answer with some R code and output.
First, we construct a random normal distribution, y, with a mean of 5 and a SD of 1:
y <- rnorm(1000, mean=5, sd=1)
Next, I purposely create a second random normal distribution, x,... | What is the difference between linear regression on y with x and x with y?
I'm going to illustrate the answer with some R code and output.
First, we construct a random normal distribution, y, with a mean of 5 and a SD of 1:
y <- rnorm(1000, mean=5, sd=1)
Next, I purposely c |
1,088 | What is the difference between linear regression on y with x and x with y? | The insight that since Pearson's correlation is the same whether we do a regression of x against y, or y against x is a good one, we should get the same linear regression is a good one. It is only slightly incorrect, and we can use it to understand what is actually occurring.
This is the equation for a line, which is ... | What is the difference between linear regression on y with x and x with y? | The insight that since Pearson's correlation is the same whether we do a regression of x against y, or y against x is a good one, we should get the same linear regression is a good one. It is only sl | What is the difference between linear regression on y with x and x with y?
The insight that since Pearson's correlation is the same whether we do a regression of x against y, or y against x is a good one, we should get the same linear regression is a good one. It is only slightly incorrect, and we can use it to unders... | What is the difference between linear regression on y with x and x with y?
The insight that since Pearson's correlation is the same whether we do a regression of x against y, or y against x is a good one, we should get the same linear regression is a good one. It is only sl |
1,089 | What is the difference between linear regression on y with x and x with y? | On questions like this it's easy to get caught up on the technical issues, so I'd like to focus specifically on the question in the title of the thread which asks: What is the difference between linear regression on y with x and x with y?
Consider for a moment a (simplified) econometric model from human capital theory ... | What is the difference between linear regression on y with x and x with y? | On questions like this it's easy to get caught up on the technical issues, so I'd like to focus specifically on the question in the title of the thread which asks: What is the difference between linea | What is the difference between linear regression on y with x and x with y?
On questions like this it's easy to get caught up on the technical issues, so I'd like to focus specifically on the question in the title of the thread which asks: What is the difference between linear regression on y with x and x with y?
Consid... | What is the difference between linear regression on y with x and x with y?
On questions like this it's easy to get caught up on the technical issues, so I'd like to focus specifically on the question in the title of the thread which asks: What is the difference between linea |
1,090 | What is the difference between linear regression on y with x and x with y? | Expanding on @gung's excellent answer:
In a simple linear regression the absolute value of Pearson's $r$ can be seen as the geometric mean of the two slopes we obtain if we regress $y$ on $x$ and $x$ on $y$, respectively:
$$\sqrt{{\hat{\beta}_1}_{y\,on\,x} \cdot {\hat{\beta}_1}_{x\,on\,y}} = \sqrt{\frac{\text{Cov}(x... | What is the difference between linear regression on y with x and x with y? | Expanding on @gung's excellent answer:
In a simple linear regression the absolute value of Pearson's $r$ can be seen as the geometric mean of the two slopes we obtain if we regress $y$ on $x$ and $ | What is the difference between linear regression on y with x and x with y?
Expanding on @gung's excellent answer:
In a simple linear regression the absolute value of Pearson's $r$ can be seen as the geometric mean of the two slopes we obtain if we regress $y$ on $x$ and $x$ on $y$, respectively:
$$\sqrt{{\hat{\beta}... | What is the difference between linear regression on y with x and x with y?
Expanding on @gung's excellent answer:
In a simple linear regression the absolute value of Pearson's $r$ can be seen as the geometric mean of the two slopes we obtain if we regress $y$ on $x$ and $ |
1,091 | What is the difference between linear regression on y with x and x with y? | There is a very interesting phenomenon about this topic. After exchanging x and y, although the regression coefficient changes, but the t-statistic/F-statistic and significance level for the coefficient don't change. This is also true even in multiple regression, where we exchange y with one of the independent variabl... | What is the difference between linear regression on y with x and x with y? | There is a very interesting phenomenon about this topic. After exchanging x and y, although the regression coefficient changes, but the t-statistic/F-statistic and significance level for the coefficie | What is the difference between linear regression on y with x and x with y?
There is a very interesting phenomenon about this topic. After exchanging x and y, although the regression coefficient changes, but the t-statistic/F-statistic and significance level for the coefficient don't change. This is also true even in m... | What is the difference between linear regression on y with x and x with y?
There is a very interesting phenomenon about this topic. After exchanging x and y, although the regression coefficient changes, but the t-statistic/F-statistic and significance level for the coefficie |
1,092 | What is the difference between linear regression on y with x and x with y? | The relation is not symmetric because we are solving two different optimisation problems. $\textbf{ Doing regression of $y$ given $x$}$ can be written as solving the following problem:
$$\min_b \mathbb E(Y - bX)^2$$
whereas for $\textbf{doing regression of $x$ given $y$}$:
$$\min_b \mathbb E(X - bY)^2$$, which can be r... | What is the difference between linear regression on y with x and x with y? | The relation is not symmetric because we are solving two different optimisation problems. $\textbf{ Doing regression of $y$ given $x$}$ can be written as solving the following problem:
$$\min_b \mathb | What is the difference between linear regression on y with x and x with y?
The relation is not symmetric because we are solving two different optimisation problems. $\textbf{ Doing regression of $y$ given $x$}$ can be written as solving the following problem:
$$\min_b \mathbb E(Y - bX)^2$$
whereas for $\textbf{doing re... | What is the difference between linear regression on y with x and x with y?
The relation is not symmetric because we are solving two different optimisation problems. $\textbf{ Doing regression of $y$ given $x$}$ can be written as solving the following problem:
$$\min_b \mathb |
1,093 | What is the difference between linear regression on y with x and x with y? | This question can also be answered from a linear algebra perspective. Say you have a bunch of data points $(x,y)$. We want to find the line $y=mx+b$ that's closest to all our points (the regression line).
As an example, say we have the points $(1,2),(2,4.5),(3,6),(4,7)$. We can look at this as a simultaneous equation ... | What is the difference between linear regression on y with x and x with y? | This question can also be answered from a linear algebra perspective. Say you have a bunch of data points $(x,y)$. We want to find the line $y=mx+b$ that's closest to all our points (the regression li | What is the difference between linear regression on y with x and x with y?
This question can also be answered from a linear algebra perspective. Say you have a bunch of data points $(x,y)$. We want to find the line $y=mx+b$ that's closest to all our points (the regression line).
As an example, say we have the points $... | What is the difference between linear regression on y with x and x with y?
This question can also be answered from a linear algebra perspective. Say you have a bunch of data points $(x,y)$. We want to find the line $y=mx+b$ that's closest to all our points (the regression li |
1,094 | What is the difference between linear regression on y with x and x with y? | Well, it's true that for a simple bivariate regression, the linear correlation coefficient and R-square will be the same for both equations. But the slopes will be $rS_y/S_x$ or $rS_x/S_y$ , which are not reciprocals of each other, unless $r = 1$. | What is the difference between linear regression on y with x and x with y? | Well, it's true that for a simple bivariate regression, the linear correlation coefficient and R-square will be the same for both equations. But the slopes will be $rS_y/S_x$ or $rS_x/S_y$ , which ar | What is the difference between linear regression on y with x and x with y?
Well, it's true that for a simple bivariate regression, the linear correlation coefficient and R-square will be the same for both equations. But the slopes will be $rS_y/S_x$ or $rS_x/S_y$ , which are not reciprocals of each other, unless $r = ... | What is the difference between linear regression on y with x and x with y?
Well, it's true that for a simple bivariate regression, the linear correlation coefficient and R-square will be the same for both equations. But the slopes will be $rS_y/S_x$ or $rS_x/S_y$ , which ar |
1,095 | Why does a time series have to be stationary? | Stationarity is a one type of dependence structure.
Suppose we have a data $X_1,...,X_n$. The most basic assumption is that $X_i$ are independent, i.e. we have a sample. The independence is a nice property, since using it we can derive a lot of useful results. The problem is that sometimes (or frequently, depending on ... | Why does a time series have to be stationary? | Stationarity is a one type of dependence structure.
Suppose we have a data $X_1,...,X_n$. The most basic assumption is that $X_i$ are independent, i.e. we have a sample. The independence is a nice pro | Why does a time series have to be stationary?
Stationarity is a one type of dependence structure.
Suppose we have a data $X_1,...,X_n$. The most basic assumption is that $X_i$ are independent, i.e. we have a sample. The independence is a nice property, since using it we can derive a lot of useful results. The problem i... | Why does a time series have to be stationary?
Stationarity is a one type of dependence structure.
Suppose we have a data $X_1,...,X_n$. The most basic assumption is that $X_i$ are independent, i.e. we have a sample. The independence is a nice pro |
1,096 | Why does a time series have to be stationary? | What quantities are we typically interested in when we perform statistical analysis on a time series? We want to know
Its expected value,
Its variance, and
The correlation between values $s$ periods apart for a set of $s$ values.
How do we calculate these things? Using a mean across many time periods.
The mean across... | Why does a time series have to be stationary? | What quantities are we typically interested in when we perform statistical analysis on a time series? We want to know
Its expected value,
Its variance, and
The correlation between values $s$ periods | Why does a time series have to be stationary?
What quantities are we typically interested in when we perform statistical analysis on a time series? We want to know
Its expected value,
Its variance, and
The correlation between values $s$ periods apart for a set of $s$ values.
How do we calculate these things? Using a ... | Why does a time series have to be stationary?
What quantities are we typically interested in when we perform statistical analysis on a time series? We want to know
Its expected value,
Its variance, and
The correlation between values $s$ periods |
1,097 | Why does a time series have to be stationary? | To add a high-level answer to some of the other answers that are good but more detailed, stationarity is important because, in its absence, a model describing the data will vary in accuracy at different time points. As such, stationarity is required for sample statistics such as means, variances, and correlations to ac... | Why does a time series have to be stationary? | To add a high-level answer to some of the other answers that are good but more detailed, stationarity is important because, in its absence, a model describing the data will vary in accuracy at differe | Why does a time series have to be stationary?
To add a high-level answer to some of the other answers that are good but more detailed, stationarity is important because, in its absence, a model describing the data will vary in accuracy at different time points. As such, stationarity is required for sample statistics su... | Why does a time series have to be stationary?
To add a high-level answer to some of the other answers that are good but more detailed, stationarity is important because, in its absence, a model describing the data will vary in accuracy at differe |
1,098 | Why does a time series have to be stationary? | An underlying idea in statistical learning is that you can learn by repeating an experiment. For example, we can keep flipping a thumbtack to learn the probability that a thumbtack lands on its head.
In the time-series context, we observe a single run of a stochastic process rather than repeated runs of the stochastic ... | Why does a time series have to be stationary? | An underlying idea in statistical learning is that you can learn by repeating an experiment. For example, we can keep flipping a thumbtack to learn the probability that a thumbtack lands on its head.
| Why does a time series have to be stationary?
An underlying idea in statistical learning is that you can learn by repeating an experiment. For example, we can keep flipping a thumbtack to learn the probability that a thumbtack lands on its head.
In the time-series context, we observe a single run of a stochastic proces... | Why does a time series have to be stationary?
An underlying idea in statistical learning is that you can learn by repeating an experiment. For example, we can keep flipping a thumbtack to learn the probability that a thumbtack lands on its head.
|
1,099 | Why does a time series have to be stationary? | First of all, ARIMA(p,1,q) processes are not stationary. These are so called integrated series, e.g. $x_t=x_{t-1}+e_t$ is ARIMA(0,1,0) or I(1) process, also random walk or unit root. So, no, you don't need them all stationary.
However, we often do look for stationarity. Why?
Consider the forecasting problem. How do you... | Why does a time series have to be stationary? | First of all, ARIMA(p,1,q) processes are not stationary. These are so called integrated series, e.g. $x_t=x_{t-1}+e_t$ is ARIMA(0,1,0) or I(1) process, also random walk or unit root. So, no, you don't | Why does a time series have to be stationary?
First of all, ARIMA(p,1,q) processes are not stationary. These are so called integrated series, e.g. $x_t=x_{t-1}+e_t$ is ARIMA(0,1,0) or I(1) process, also random walk or unit root. So, no, you don't need them all stationary.
However, we often do look for stationarity. Why... | Why does a time series have to be stationary?
First of all, ARIMA(p,1,q) processes are not stationary. These are so called integrated series, e.g. $x_t=x_{t-1}+e_t$ is ARIMA(0,1,0) or I(1) process, also random walk or unit root. So, no, you don't |
1,100 | Why does a time series have to be stationary? | Since ARIMA is regressing on itself for the most part, it uses a type of self-induced multiple regression that would be unnecessarily influenced by either a strong trend or seasonality. This multiple regression technique is based on previous time series values, especially those within the latest periods, and allows us... | Why does a time series have to be stationary? | Since ARIMA is regressing on itself for the most part, it uses a type of self-induced multiple regression that would be unnecessarily influenced by either a strong trend or seasonality. This multiple | Why does a time series have to be stationary?
Since ARIMA is regressing on itself for the most part, it uses a type of self-induced multiple regression that would be unnecessarily influenced by either a strong trend or seasonality. This multiple regression technique is based on previous time series values, especially ... | Why does a time series have to be stationary?
Since ARIMA is regressing on itself for the most part, it uses a type of self-induced multiple regression that would be unnecessarily influenced by either a strong trend or seasonality. This multiple |
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