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1624 | 1 | 1633 | null | 4 | 760 | How are the values in a z-table computed? Is it possible to compute confidence without looking up a z-table?
| Based on z-score, is it possible to compute confidence without looking at a z-table? | CC BY-SA 2.5 | null | 2010-08-13T00:48:15.973 | 2022-11-26T12:32:37.333 | 2010-08-13T08:35:39.487 | 8 | 850 | [
"confidence-interval",
"z-statistic"
] |
1625 | 2 | null | 1557 | 7 | null | See also [Jennrich, RJ, Oman, SD "How much does Stein estimation help in multiple linear regression?" Technometrics, 28, 113-121, 1986.](http://www.jstor.org/pss/1270447)
| null | CC BY-SA 2.5 | null | 2010-08-13T01:08:39.567 | 2010-08-13T01:26:17.920 | 2010-08-13T01:26:17.920 | 159 | null | null |
1627 | 2 | null | 396 | 6 | null | Take a look at the R graphics library, ggplot2. Details are at the web page [http://had.co.nz/ggplot2/](http://had.co.nz/ggplot2/) This package generates very good default plots, that follow the Tufte principles, Cleveland's guidelines and Ihaka's color package.
| null | CC BY-SA 2.5 | null | 2010-08-13T01:30:32.060 | 2010-08-13T01:30:32.060 | null | null | null | null |
1628 | 2 | null | 1610 | 7 | null | Based on the principle of [Occam's razor](http://en.wikipedia.org/wiki/Occam%27s_razor), Type I errors (rejecting the null hypothesis when it is true) are "arguably" worse than Type II errors (not rejecting the null hypothesis when it is false).
If you believe such an argument:
- Type I errors are of primary concern
... | null | CC BY-SA 2.5 | null | 2010-08-13T01:38:42.727 | 2010-08-13T01:48:32.673 | 2010-08-13T01:48:32.673 | 183 | 183 | null |
1629 | 2 | null | 1601 | 11 | null | The first step should be to ask why your variables are non-normally distributed. This can be illuminating.
Common findings from my experience:
- Ability tests (e.g., exams, intelligence tests, admission tests) tend to be negatively skewed when there are ceiling effects and positively skewed when there are floor effec... | null | CC BY-SA 2.5 | null | 2010-08-13T02:18:09.137 | 2010-08-13T02:18:09.137 | 2017-04-13T12:44:53.777 | -1 | 183 | null |
1630 | 2 | null | 1444 | 105 | null | No-one mentioned the inverse hyperbolic sine transformation. So for completeness I'm adding it here.
This is an alternative to the Box-Cox transformations and is defined by
\begin{equation}
f(y,\theta) = \text{sinh}^{-1}(\theta y)/\theta = \log[\theta y + (\theta^2y^2+1)^{1/2}]/\theta,
\end{equation}
where $\theta>0$. ... | null | CC BY-SA 2.5 | null | 2010-08-13T04:27:15.563 | 2010-08-13T04:27:15.563 | null | null | 159 | null |
1631 | 2 | null | 1624 | 3 | null | A z-table gives you values of the cumulative distribution function for the standard (i.e., mean = 0, standard deviation = 1) normal distribution. The integral needs to be estimated numerically. See [here for a discussion](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution_function).
Of course, to ... | null | CC BY-SA 2.5 | null | 2010-08-13T05:17:37.523 | 2010-08-13T08:22:26.757 | 2010-08-13T08:22:26.757 | 183 | 183 | null |
1632 | 2 | null | 1595 | 322 | null | It's hard to ignore the wealth of statistical packages available in R/CRAN. That said, I spend a lot of time in Python land and would never dissuade anyone from having as much fun as I do. :) Here are some libraries/links you might find useful for statistical work.
- NumPy/Scipy You probably know about these alre... | null | CC BY-SA 3.0 | null | 2010-08-13T05:30:09.033 | 2016-12-08T17:34:43.973 | 2016-12-08T17:34:43.973 | 128762 | 251 | null |
1633 | 2 | null | 1624 | 3 | null | The simplest answer is that one either uses numerical quadrature techniques (Simpson's, for instance) on the PDF, or approximations such as those found in [Abramowitz and Stegun](https://personal.math.ubc.ca/%7Ecbm/aands/page_932.htm). Personally however, since one merely needs a few digits of accuracy for the "error f... | null | CC BY-SA 4.0 | null | 2010-08-13T05:43:29.447 | 2022-11-26T12:32:37.333 | 2022-11-26T12:32:37.333 | 362671 | 830 | null |
1634 | 2 | null | 1562 | 4 | null | There are two definitions of statistical independence:
1) P(A,B)=P(A)*P(B) <=> 2) P(A|B)=P(A) <=> 2a) P(B|A)=P(B).
(<=> means if and only if)
So to answer your question: both are valid.
Pearson Chi-square test of independence is motivated by definition 1), logistic regression and multinomial regression are motivated by... | null | CC BY-SA 2.5 | null | 2010-08-13T05:51:00.667 | 2010-08-13T05:51:00.667 | null | null | 419 | null |
1635 | 2 | null | 1622 | 3 | null | I'm probably missing something important, but why does the fact that your observed variable is a Sharpe ratio change the statistic you would use to test the difference in Sharpe ratios? I understand that they are already distributed like 2 independent non-central t statistics, but what forces you to treat them that way... | null | CC BY-SA 2.5 | null | 2010-08-13T05:51:02.383 | 2010-08-13T05:51:02.383 | null | null | 196 | null |
1636 | 2 | null | 1380 | 3 | null | I may be a little unclear about the question. But here would be my solution computing some "statistic on each of the tables" and comparing those values.
If your contingency tables are like a binomial effect size display (BESD), with clear YES/NO predictions being provided by each of your K methods you'll have a number... | null | CC BY-SA 2.5 | null | 2010-08-13T06:09:24.530 | 2010-08-13T06:24:50.753 | 2010-08-13T06:24:50.753 | 196 | 196 | null |
1637 | 1 | 1639 | null | 55 | 71852 | I'm sure I've got this completely wrapped round my head, but I just can't figure it out.
The t-test compares two normal distributions using the Z distribution. That's why there's an assumption of normality in the DATA.
ANOVA is equivalent to linear regression with dummy variables, and uses sums of squares, just like OL... | If the t-test and the ANOVA for two groups are equivalent, why aren't their assumptions equivalent? | CC BY-SA 2.5 | null | 2010-08-13T09:41:13.160 | 2013-01-08T15:26:45.957 | 2010-08-13T10:00:01.717 | 8 | 199 | [
"distributions",
"regression",
"normality-assumption",
"t-test",
"anova"
] |
1638 | 2 | null | 1610 | 6 | null | Hurrah, a question non-technical enough so as I can answer it!
"Type one is a con" [rhyming]- i.e. fools you into thinking that a difference exists when it doesn't. Always works for me.
| null | CC BY-SA 2.5 | null | 2010-08-13T09:50:51.067 | 2010-08-13T09:50:51.067 | null | null | 199 | null |
1639 | 2 | null | 1637 | 37 | null | The t-test with two groups assumes that each group is normally distributed with the same variance (although the means may differ under the alternative hypothesis). That is equivalent to a regression with a dummy variable as the regression allows the mean of each group to differ but not the variance. Hence the residuals... | null | CC BY-SA 2.5 | null | 2010-08-13T09:52:44.700 | 2010-08-13T09:52:44.700 | null | null | 159 | null |
1641 | 2 | null | 672 | 8 | null | Bayes' theorem is a way to rotate a conditional probability $P(A|B)$ to another conditional probability $P(B|A)$.
A stumbling block for some is the meaning of $P(B|A)$. This is a way to reduce the space of possible events by considering only those events where $A$ definitely happens (or is true). So for instance the ... | null | CC BY-SA 2.5 | null | 2010-08-13T11:42:08.157 | 2010-08-13T11:42:08.157 | null | null | 702 | null |
1642 | 2 | null | 1610 | 10 | null | You could reject the idea entirely.
Some authors (Andrew Gelman is one) are shifting to discussing Type S (sign) and Type M (magnitude) errors. You can infer the wrong effect direction (e.g., you believe the treatment group does better but actually does worse) or the wrong magnitude (e.g., you find a massive effect wh... | null | CC BY-SA 2.5 | null | 2010-08-13T12:22:25.313 | 2010-08-13T12:22:25.313 | null | null | 702 | null |
1643 | 2 | null | 1637 | 19 | null | I totally agree with Rob's answer, but let me put it another way (using wikipedia):
[Assumptions ANOVA](http://en.wikipedia.org/wiki/Analysis_of_variance#Assumptions_of_ANOVA):
- Independence of cases – this is an assumption of the model that simplifies the statistical analysis.
- Normality – the distributions of the... | null | CC BY-SA 2.5 | null | 2010-08-13T12:24:11.597 | 2010-08-13T12:24:11.597 | null | null | 442 | null |
1644 | 2 | null | 1611 | 22 | null | I don't think it matters very much, as long as the interpretation of the results is performed within the same framework as the analysis. The main problem with frequentist statistics is that there is a natural tendency to treat the p-value of a frequentist significance test as if it was a Bayesian a-posteriori probabil... | null | CC BY-SA 2.5 | null | 2010-08-13T12:31:09.510 | 2010-08-13T12:31:09.510 | null | null | 887 | null |
1645 | 1 | 1648 | null | 24 | 18495 | So far, I've been using the Shapiro-Wilk statistic in order to test normality assumptions in small samples.
Could you please recommend another technique?
| Appropriate normality tests for small samples | CC BY-SA 3.0 | null | 2010-08-13T12:42:30.220 | 2021-02-06T14:56:07.777 | 2015-02-22T12:35:34.830 | 22047 | 1356 | [
"hypothesis-testing",
"goodness-of-fit",
"normality-assumption",
"small-sample"
] |
1646 | 1 | 2524 | null | 8 | 2967 | Imagine that:
- You have a sample of 1000 teams each with 10 members.
- You measured team functioning by asking each team member how well they think their team is functioning using a reliable multi-item numeric scale.
- You want to describe the extent to which the measure of team effectiveness is a property of the t... | Intraclass correlation and aggregation | CC BY-SA 2.5 | null | 2010-08-13T12:44:23.913 | 2014-07-09T12:10:01.610 | 2010-08-13T12:50:59.220 | 183 | 183 | [
"correlation",
"intraclass-correlation",
"aggregation",
"interpretation",
"effect-size"
] |
1647 | 2 | null | 1645 | 11 | null | There is a whole [Wikipedia category on normality tests](http://en.wikipedia.org/wiki/Category%3aNormality_tests) including:
- the Anderson-Darling test, popular amongst statisticians; and
- the Jarque-Bera test, popular amongst econometricians.
I think A-D is probably the best of them.
| null | CC BY-SA 2.5 | null | 2010-08-13T12:47:16.497 | 2010-08-13T12:47:16.497 | null | null | 159 | null |
1648 | 2 | null | 1645 | 25 | null | The [fBasics](http://cran.r-project.org/web/packages/fBasics/index.html) package in R (part of [Rmetrics](https://www.rmetrics.org/)) includes [several normality tests](http://hosho.ees.hokudai.ac.jp/~kubo/Rdoc/library/fBasics/html/NormalityTests.html), covering many of the popular [frequentist tests](http://en.wikiped... | null | CC BY-SA 3.0 | null | 2010-08-13T13:32:27.913 | 2015-02-22T12:37:27.600 | 2015-02-22T12:37:27.600 | 22047 | 5 | null |
1649 | 1 | 1650 | null | 5 | 568 | Say I observe two groups of 10 people, measuring some quantity 100 times in each person. There will presumably be some variability across these 100 measures in each person. Can I use mixed effects analysis to assess whether this within-person variability is, on average, different between the two groups? For example, us... | Using mixed effects modelling to estimate and compare variability | CC BY-SA 2.5 | null | 2010-08-13T13:58:38.567 | 2010-08-17T06:26:59.823 | null | null | 364 | [
"variance",
"mixed-model"
] |
1650 | 2 | null | 1649 | 4 | null | You can structure the model along the following lines. Let,
$j = 1, 2$ be the two groups and
$i$ index the individuals in the two groups.
Then your model is:
$y_{ij} \sim N(\mu_j,\sigma_j^2)$ $\forall \ i, j$
$\sigma_j^2 \sim IG(v,1)$ $\forall \ j$
(Note: $IG(.)$ is the inverse gamma distribution.)
Priors
$\mu_j \s... | null | CC BY-SA 2.5 | null | 2010-08-13T14:17:15.793 | 2010-08-13T14:17:15.793 | null | null | null | null |
1651 | 1 | null | null | 6 | 2188 | I need to fit $Y_{ij} \sim NegBin(m_{ij},k)$, i.e. a negative binomial distribution to count data. However, the data I have observed are censored - I know the value of $y_{ij}$, but it could be more than that value. The log-likelihood is
\begin{equation}
ll = \sum_{i=1}^n w_i (c_i \log(P(Y_{ij}=y_{ij}|X_{ij})) + (1- c... | How to fit a negative binomial distribution in R while incorporating censoring | CC BY-SA 2.5 | null | 2010-08-13T14:28:02.520 | 2010-08-17T13:21:27.277 | 2010-08-17T13:21:27.277 | 8 | null | [
"r",
"censoring",
"negative-binomial-distribution"
] |
1652 | 2 | null | 534 | 10 | null | Correlation alone never implies causation. It's that simple.
But it's very rare to have only a correlation between two variables. Often you also know something about what those variables are and a theory, or theories, suggesting why there might be a causal relationship between the variables. If not, then we bother c... | null | CC BY-SA 2.5 | null | 2010-08-13T15:21:55.373 | 2010-08-13T15:21:55.373 | null | null | 702 | null |
1653 | 2 | null | 1637 | 26 | null | The t-test simply a special case of the F-test where only two groups are being compared. The result of either will be exactly the same in terms of the p-value and there is a simple relationship between the F and t statistics as well. F = t^2. The two tests are algebraically equivalent and their assumptions are the s... | null | CC BY-SA 2.5 | null | 2010-08-13T15:24:20.490 | 2010-08-13T15:42:56.300 | 2010-08-13T15:42:56.300 | 485 | 485 | null |
1654 | 2 | null | 173 | 5 | null | You may try to model your data using a Dynamic Generalized Linear Model (DGLM). In R, you can fit this kind of models using packages sspir and KFAS. In a sense, this is similar to the gam approach suggested by Rob, except that instead of assuming that the log mean of the Poisson observations be a smooth function of tim... | null | CC BY-SA 2.5 | null | 2010-08-13T15:31:27.857 | 2010-08-13T15:31:27.857 | null | null | null | null |
1655 | 2 | null | 1601 | 9 | null | John Tukey systematically discusses transformations in his book on EDA. In addition to the Box-Cox family (affinely scaled power transformations) he defines a family of "folded" transformations for proportions (essentially powers of x/(1-x)) and "started" counts (adding a positive offset to counted data before transfo... | null | CC BY-SA 2.5 | null | 2010-08-13T15:48:29.310 | 2010-08-13T15:48:29.310 | null | null | 919 | null |
1656 | 2 | null | 1645 | 3 | null | For completeness, econometricians also like the Kiefer and Salmon test from their 1983 paper in Economics Letters -- it sums 'normalized' expressions of skewness and kurtosis which is then chi-square distributed. I have an old C++ version I wrote during grad school I could translate into R.
Edit: And [here](http://eco... | null | CC BY-SA 2.5 | null | 2010-08-13T15:54:37.857 | 2010-08-13T16:54:43.857 | 2010-08-13T16:54:43.857 | 334 | 334 | null |
1657 | 2 | null | 726 | 5 | null | [CauseWeb](http://www.causeweb.org/) has a collection of statistics quotations. Many have already been repeated here, but it has plenty that haven't yet been quoted, such as
>
"The only statistics you can trust are those you falsified yourself."
(Falsely attributed to Sir Winston Churchill.) For the rest, follow t... | null | CC BY-SA 2.5 | null | 2010-08-13T16:04:08.990 | 2010-08-17T22:11:08.277 | 2010-08-17T22:11:08.277 | 919 | 919 | null |
1658 | 2 | null | 726 | 7 | null | "I cannot conceal the fact here that in the [application of probability theory], I foresee many things happening which can cause one to be badly mistaken if he does not proceed cautiously.",
Bernoulli (1713) (via ET Jaynes)
"A statistician is someone who knows what to assume to be Gaussian"
Dikran Marsupial (2009) (not... | null | CC BY-SA 2.5 | null | 2010-08-13T16:11:20.950 | 2010-08-13T16:11:20.950 | null | null | 887 | null |
1659 | 2 | null | 1462 | 4 | null | A player's yardage is unlikely to be anywhere near normally distributed. If it were, your guy averaging 5.3 give or take 1.7 yards would almost never lose yards or gain more than 11 yards on any play in the entire season. Gone is the excitement of the game, to be replaced by some statistical mediocrity. If football ... | null | CC BY-SA 2.5 | null | 2010-08-13T16:15:41.417 | 2010-08-13T16:15:41.417 | null | null | 919 | null |
1660 | 1 | 1665 | null | 11 | 2369 | I need to define what a test of independence is, without the use of heavily statistic terms.
| What is a test of independence? | CC BY-SA 3.0 | null | 2010-08-13T16:43:45.133 | 2015-12-19T16:29:50.090 | 2015-12-19T16:29:50.090 | 28666 | 559 | [
"hypothesis-testing",
"independence",
"definition"
] |
1661 | 1 | null | null | 2 | 720 | X1 is wing length, X2 is tail length for 45 male and 45 female bugs.
Which 2-sample univariate t-test should I use?
My thought was to use Hotelling's T-square?
But Hotelling's is multi-variate not univariate. Now, I'm not sure...
Any ideas?
| Which 2-sample univariate t-test to use? | CC BY-SA 2.5 | null | 2010-08-13T17:21:37.057 | 2010-08-17T18:31:59.927 | 2010-08-15T08:47:32.430 | null | null | [
"t-test"
] |
1662 | 2 | null | 1661 | 1 | null | While your questions is not clear (which means do you want to compare?) you can consult the wiki: [Comparing Means](http://en.wikipedia.org/wiki/Comparing_means) to decide what to do.
| null | CC BY-SA 2.5 | null | 2010-08-13T17:39:02.347 | 2010-08-13T17:39:02.347 | null | null | null | null |
1663 | 2 | null | 1660 | 2 | null | Why don't you take the definition of wikipedia. It's quite short und doesn't use heavily statistic terms.
>
A test of independence assesses whether paired observations on two variables, expressed in a contingency table, are independent of each other – for example, whether people from different regions differ in the fr... | null | CC BY-SA 2.5 | null | 2010-08-13T18:27:56.683 | 2010-08-13T18:27:56.683 | null | null | 927 | null |
1664 | 2 | null | 1661 | 1 | null | As others have said, you need to clarify your question.
However, I'm guessing that you want to determine if wing length or tail length differ between male and female bugs. In this case I would just do a couple of [two sample t-tests](http://en.wikipedia.org/wiki/Student%27s_t-test#Independent_two-sample_t-test). So fo... | null | CC BY-SA 2.5 | null | 2010-08-13T19:29:26.220 | 2010-08-13T19:29:26.220 | null | null | 8 | null |
1665 | 2 | null | 1660 | 5 | null | I would start by defining what you mean by independence. For example,
>
If two variables are independent this
means that knowing the value of one
variable does not tell you anything
about the value of the other variable.
Then I would describe the test:
>
To test for independence we construct
a table of val... | null | CC BY-SA 2.5 | null | 2010-08-13T19:39:43.713 | 2010-08-13T19:39:43.713 | null | null | 8 | null |
1666 | 2 | null | 414 | 12 | null | I loved the Freedman, Pisani, Purves' [Statistics](https://wwnorton.com/books/Statistics/) text because it is extremely non-mathematical. As a mathematician, you will find it to be such a clear guide to the statistical concepts that you will be able to develop all the mathematical theory as an exercise: that's a rewar... | null | CC BY-SA 4.0 | null | 2010-08-13T21:22:56.557 | 2023-02-11T10:04:41.897 | 2023-02-11T10:04:41.897 | 362671 | 919 | null |
1667 | 1 | null | null | 5 | 4810 | As an engineer, I'm interested in topics such as designing experiments that are statistically valid, quality control, process control, reliability, and cost control. I took a course in engineering statistics, but unfortunately neither the book nor the professor were that good. I did OK in the course, but I'm interested... | What books provide an overview of engineering statistics? | CC BY-SA 2.5 | null | 2010-08-13T22:29:08.050 | 2010-08-14T01:32:29.947 | 2010-08-14T01:21:36.430 | 110 | 110 | [
"references",
"engineering-statistics"
] |
1668 | 1 | null | null | 16 | 5548 | As a software engineer, I'm interested in topics such as statistical algorithms, data mining, machine learning, Bayesian networks, classification algorithms, neural networks, Markov chains, Monte Carlo methods, and random number generation.
I personally haven't had the pleasure of working hands-on with any of these tec... | What books provide an overview of computational statistics as it applies to computer science? | CC BY-SA 2.5 | null | 2010-08-13T22:35:50.853 | 2018-09-07T06:27:28.540 | 2010-08-14T01:21:07.197 | 110 | 110 | [
"references",
"computational-statistics"
] |
1669 | 2 | null | 1667 | 4 | null | NIST/SEMATECH e-Handbook of Statistical Methods is a good start. Free and online:
[http://www.itl.nist.gov/div898/handbook/](http://www.itl.nist.gov/div898/handbook/)
| null | CC BY-SA 2.5 | null | 2010-08-13T23:58:11.623 | 2010-08-13T23:58:11.623 | null | null | 74 | null |
1670 | 2 | null | 1667 | 1 | null | When I took the Engineering Statistics course I mentioned in the question, the assigned textbook wasn't very helpful. Instead, I used [Probability and Statistics for Engineers and Scientists - Anthony Hayter](http://rads.stackoverflow.com/amzn/click/0495107573) to get through the course. It didn't cover everything in t... | null | CC BY-SA 2.5 | null | 2010-08-14T00:58:21.737 | 2010-08-14T00:58:21.737 | null | null | 110 | null |
1671 | 2 | null | 1668 | 1 | null | I picked up a copy of [Probability and Statistics for Computer Scientists - Michael Baron](http://rads.stackoverflow.com/amzn/click/1584886412) on sale with another statistics book (I honestly bought it because of the name - I wanted a book that would take some kind of look at statistics from a computer science perspec... | null | CC BY-SA 2.5 | null | 2010-08-14T01:02:31.637 | 2010-08-14T01:02:31.637 | null | null | 110 | null |
1672 | 2 | null | 1668 | 1 | null | Although it's not specifically computational statistics, [A Handbook of Statistical Analyses Using R - Brian S. Everitt and Torsten Hothorn](http://rads.stackoverflow.com/amzn/click/1584885394) covers a lot of topics that I've seen covered in basic and intermediate statistics books - inference, ANOVA, linear regression... | null | CC BY-SA 2.5 | null | 2010-08-14T01:09:33.820 | 2010-08-14T01:09:33.820 | null | null | 110 | null |
1673 | 2 | null | 1668 | 1 | null | [Statistical Computing with R - Maria L. Rizzo](http://rads.stackoverflow.com/amzn/click/1584885459) covers a lot of the topics in Probability and Statistics for Computer Scientists - basic probability and statistics, random variables, Bayesian statistics, Markov chains, visualization of multivariate data, Monte Carlo ... | null | CC BY-SA 2.5 | null | 2010-08-14T01:16:16.037 | 2010-08-14T01:16:16.037 | null | null | 110 | null |
1674 | 1 | 1693 | null | 8 | 692 | On page 331 of "Elements of Information Theory" (1991), author says that while entropy is related to the volume of the typical set, Fisher information is related to the surface area of the typical set, but I can't find anything more on this...can anyone explain this connection?
| Fisher information and the "surface area of the typical set" | CC BY-SA 2.5 | null | 2010-08-14T01:31:43.443 | 2010-09-17T20:26:19.617 | 2010-09-17T20:26:19.617 | null | 511 | [
"information-theory"
] |
1675 | 2 | null | 1667 | 1 | null | The book [Statistical Methods for Engineers - Geoffrey Vining](http://rads.stackoverflow.com/amzn/click/053873518X) is used in my university's Engineering Statistics course. However, I do not recommend this book. When I took the course, I ended up not being able to learn from the professor, so I was using this book to ... | null | CC BY-SA 2.5 | null | 2010-08-14T01:32:29.947 | 2010-08-14T01:32:29.947 | null | null | 110 | null |
1676 | 1 | null | null | 7 | 2389 | Duplicate thread: [What R packages do you find most useful in your daily work?](https://stats.stackexchange.com/questions/73/what-r-packages-do-you-find-most-useful-in-your-daily-work)
Are there any R packages that are just plain good to have, regardless of the type of work you are doing? If so, what are these packages... | I just installed the latest version of R. What packages should I obtain? | CC BY-SA 3.0 | 0 | 2010-08-14T01:39:23.253 | 2020-10-19T07:15:38.787 | 2017-04-13T12:44:24.667 | -1 | 110 | [
"r"
] |
1677 | 2 | null | 1668 | 3 | null | You might want to read the extremely popular question on Stack Overflow on
[what statistics a programmer or computer scientist should know](https://stackoverflow.com/questions/2039904/what-statistics-should-a-programmer-or-computer-scientist-know).
| null | CC BY-SA 2.5 | null | 2010-08-14T01:41:18.490 | 2010-08-14T01:41:18.490 | 2017-05-23T12:39:26.167 | -1 | 183 | null |
1678 | 2 | null | 73 | 6 | null | I imagine graphics and data manipulation are two things that are useful no matter what you are doing. Thus, I'd recommend:
- ggplot2 (great graphics)
- lattice (great graphics)
- plyr (useful for data manipulation)
- Hmisc (good for descriptive statistics and much more)
| null | CC BY-SA 2.5 | null | 2010-08-14T01:46:57.587 | 2010-08-14T01:46:57.587 | null | null | 183 | null |
1680 | 2 | null | 73 | 3 | null | This is definitely a question that doesn't have "an answer". It is completely dependent on what you want to do. That aside, I'll share the packages that I install as a standard with an R update...
```
install.packages(c("car","gregmisc","xtable","Design","Hmisc","psych",
"CCA", "fda", "zoo", "... | null | CC BY-SA 2.5 | null | 2010-08-14T04:58:27.553 | 2010-08-14T04:58:27.553 | null | null | 485 | null |
1681 | 1 | 2081 | null | 4 | 385 | Chernoff bound (for [absolute error](http://en.wikipedia.org/wiki/Chernoff_bound#Theorem_for_additive_form_.28absolute_error.29)) gives a bound on probability of large deviation in terms of sample size and amount of deviation, but it doesn't seem possible to rewrite it to give an explicit bound on the amount of deviati... | Chernoff-like bound for largest allowed deviation? | CC BY-SA 2.5 | null | 2010-08-14T07:16:00.080 | 2023-02-11T12:54:06.857 | 2011-04-29T00:15:43.423 | 3911 | 511 | [
"probability"
] |
1682 | 2 | null | 31 | 12 | null | What the p-value doesn't tell you is how likely it is that the null hypothesis is true. Under the conventional (Fisher) significance testing framework we first compute the likelihood of observing the data assuming the null hypothesis is true, this is the p-value. It seems intuitively reasonable then to assume the nul... | null | CC BY-SA 2.5 | null | 2010-08-14T07:52:35.467 | 2010-08-14T07:52:35.467 | null | null | 887 | null |
1683 | 2 | null | 596 | 1 | null | I think semi-supervised methods may be what you are looking for, there is quite a lot of litterature on this in Machine learning. There is a good [book](http://www.amazon.co.uk/Semi-Supervised-Learning-Adaptive-Computation-Machine/dp/0262514125) on this topic, which gives a good idea of recent developments in this are... | null | CC BY-SA 3.0 | null | 2010-08-14T08:12:41.753 | 2018-03-27T10:16:59.000 | 2018-03-27T10:16:59.000 | 887 | 887 | null |
1684 | 2 | null | 73 | 3 | null | If you are working with Latex, I recommend TikZ Device for outputting nice, Latex-formatted (like PSTricks) graphics. The output you get is text-based Latex code, which can be embedded with include(filename) into any figure environment.
Pros:
- Same font in graphics as in your text
- Professional look
Cons:
- Ta... | null | CC BY-SA 3.0 | null | 2010-08-14T08:51:32.627 | 2011-05-10T22:15:06.783 | 2011-05-10T22:15:06.783 | 13 | 939 | null |
1685 | 2 | null | 1383 | 0 | null | So, here's the example: [http://nishi.dreamhosters.com/u/book1bwt_de.txt](http://nishi.dreamhosters.com/u/book1bwt_de.txt)
Its a list of choices between 'd' and 'e' in coding of BWT output of a plaintext book.
(This is a practical task, think bzip2).
For the reference, current result is (reasonably good, but not really... | null | CC BY-SA 2.5 | null | 2010-08-14T12:10:58.203 | 2010-08-14T12:10:58.203 | null | null | 799 | null |
1686 | 2 | null | 73 | 12 | null | In a narrow sense, R Core has a recommendation: the "recommended" packages.
Everything else depends on your data analysis tasks at hand, and I'd recommend the [Task Views](http://cran.r-project.org/web/views) at CRAN.
| null | CC BY-SA 2.5 | null | 2010-08-14T13:06:45.767 | 2010-08-14T13:06:45.767 | null | null | 334 | null |
1687 | 1 | 1762 | null | 2 | 446 | The problem I’m trying to solve is “How do I figure out how much gunpowder should I put into a cartridge so that I can give myself a good probability of making the minimum power factor?”
I compete in USPSA/IPSC which requires that a competitors rounds make a minimum power factor. Power Factor is computed to be the FLOO... | Applied statistics to find the minimum load for power factor floor | CC BY-SA 2.5 | null | 2010-08-14T14:00:16.297 | 2011-04-29T00:18:36.073 | 2011-04-29T00:18:36.073 | 3911 | 937 | [
"probability",
"standard-deviation"
] |
1688 | 2 | null | 73 | 4 | null | You can get user reviews of packages on [crantastic](http://crantastic.org/reviews)
| null | CC BY-SA 2.5 | null | 2010-08-14T14:06:52.273 | 2010-08-14T14:06:52.273 | null | null | 573 | null |
1689 | 2 | null | 36 | 4 | null | Sperm count in males in Slovene villages and the number of bears (also in Slovenia) show a negative correlation. Some people find this very worrying. I'll try and get the study that did this.
| null | CC BY-SA 2.5 | null | 2010-08-14T17:42:41.177 | 2010-08-14T17:42:41.177 | null | null | 144 | null |
1690 | 2 | null | 36 | 7 | null | The standard citation pointing out the correlation between the number of newborn babies and breeding-pairs of storks in West Germany is [A new parameter for sex education, Nature 332, 495 (07 April 1988); doi:10.1038/332495a0](http://nature.com/nature/journal/v332/n6164/abs/332495a0.html)
| null | CC BY-SA 3.0 | null | 2010-08-14T17:59:20.630 | 2014-02-03T19:42:28.837 | 2014-02-03T19:42:28.837 | 22047 | 942 | null |
1691 | 2 | null | 1687 | 1 | null | You have a complex statistical problem and a complete analysis would be too long. However, I will suggest one idea that may perhaps help you to some extent.
You have already performed some calibration tests to assess the mean and standard deviation of the velocity of a bullet. However, I suspect that either your testi... | null | CC BY-SA 2.5 | null | 2010-08-14T18:32:47.380 | 2010-08-14T18:32:47.380 | null | null | null | null |
1692 | 2 | null | 36 | 15 | null | Although it's more of an illustration of the problem of multiple comparisons, it is also a good example of misattributed causation:
[Rugby (the religion of Wales) and its influence on the Catholic church: should Pope Benedict XVI be worried?](http://www.bmj.com/cgi/content/abstract/337/dec17_2/a2768)
>
"every time Wal... | null | CC BY-SA 2.5 | null | 2010-08-14T19:50:51.313 | 2010-08-14T19:50:51.313 | null | null | 495 | null |
1693 | 2 | null | 1674 | 5 | null | UPDATE
Tough crowd. :) For a concise account of connecting the trace of the Fisher matrix to surface area, please see section 4 ("Isoperimetric Inequalities") in the paper below. The crucial part is establishing the relation between differential entropy and the trace of the Fisher matrix, which the authors prove in ... | null | CC BY-SA 2.5 | null | 2010-08-14T21:05:33.317 | 2010-08-17T04:25:48.237 | 2010-08-17T04:25:48.237 | 251 | 251 | null |
1694 | 2 | null | 534 | 11 | null | Sir Austin Bradford Hill's President's Address to the Royal Society of Medicine ([The Environment and Disease: Association or Causation?](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1898525/?tool=pubmed)) explains nine criteria which help to judge whether there is a causal relationship between two correlated or associa... | null | CC BY-SA 2.5 | null | 2010-08-14T21:19:58.003 | 2010-08-14T21:19:58.003 | null | null | 942 | null |
1695 | 2 | null | 73 | 4 | null | I would suggest using some of the packages provided by [revolution R](http://www.revolutionanalytics.com/). In particular, I quite like the:
- multicore package for parallel computing using shared memory processors
- there optimized packages for matrices
| null | CC BY-SA 2.5 | null | 2010-08-14T21:52:11.520 | 2010-08-14T21:52:11.520 | null | null | 8 | null |
1696 | 2 | null | 44 | 6 | null | I would show them the raw data of [Anscombe's Quartet](http://en.wikipedia.org/wiki/Anscombe%27s_quartet) ([JSTOR link to the paper](http://jstor.org/stable/pdfplus/2682899.pdf)) in a big table, alongside another table showing the Mean & Variance of x and y, the correlation coefficient, and the equation of the linear r... | null | CC BY-SA 2.5 | null | 2010-08-14T21:55:32.190 | 2010-08-14T21:55:32.190 | null | null | 942 | null |
1697 | 2 | null | 73 | 2 | null | Jeromy mentioned my first pick: Lattice.
I also have found the `doBy` package and its `summaryBy` function to be insanely useful. They extend `aggregate` with a formula syntax that lets you aggregate multiple functions simultaneously in non-trivial ways. Great if you want, say, mean, std. dev., and length.
| null | CC BY-SA 3.0 | null | 2010-08-15T00:31:42.537 | 2014-03-06T00:03:45.983 | 2014-03-06T00:03:45.983 | 22468 | 389 | null |
1698 | 2 | null | 534 | 6 | null | A related question might be -- under what conditions can you reliably extract causal relations from data?
A 2008 NIPS [workshop](http://jmlr.csail.mit.edu/proceedings/papers/v6/) try to address that question empirically. One of the tasks was to infer the direction of causality from observations of pairs of variables wh... | null | CC BY-SA 2.5 | null | 2010-08-15T00:35:01.157 | 2010-08-15T00:40:38.993 | 2010-08-15T00:40:38.993 | 511 | 511 | null |
1699 | 1 | 1706 | null | 6 | 2092 | I am trying to solve for an efficient portfolio in R. How do I translate my constraints for a tangency point for 2 risky asset portfolio, and a given risk free rate to R solve.QP function? So basically I have the following equations:
```
w = weight of the first risky asset
R1 = mean return of the first risky asset
R2 =... | Tangency portfolio in R | CC BY-SA 2.5 | null | 2010-08-15T03:10:05.873 | 2010-09-30T21:19:46.560 | 2010-09-30T21:19:46.560 | 930 | 862 | [
"r",
"finance",
"extreme-value"
] |
1700 | 2 | null | 278 | 4 | null | What you're discovering is a degree of instability in either the algorithm or the data itself. The approach termed 'consensus' or 'ensemble' clustering is a way of dealing with the problem. The problem there is: given a collection of clusterings, find a "consensus" clustering that is in some sense the "average" of the ... | null | CC BY-SA 2.5 | null | 2010-08-15T05:29:00.713 | 2010-08-15T05:29:00.713 | null | null | 139 | null |
1701 | 2 | null | 278 | 1 | null | Which flat-clustering algorithm are you using? It might also be the case that the different results are because maybe it's not your data but your algorithm itself is non-deterministic (e.g., using K-means with random initialization, or using a model-based clustering with EM or MCMC for inference with random initializat... | null | CC BY-SA 2.5 | null | 2010-08-15T06:13:47.870 | 2010-08-15T06:13:47.870 | null | null | 881 | null |
1702 | 2 | null | 1668 | 2 | null | You've mentioned some ML techniques, so two quite nice books (quite because unfortunately my favorite is in Polish):
[http://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1420067184](http://rads.stackoverflow.com/amzn/click/1420067184)
[http://ai.stanford.edu/~nilsson/mlbook.html](http://ai.sta... | null | CC BY-SA 2.5 | null | 2010-08-15T08:59:45.287 | 2010-08-15T08:59:45.287 | null | null | null | null |
1706 | 2 | null | 1699 | 4 | null | I haven't looked at your code yet, but here are two pointers:
- Rmetrics has the tangencyPortfolio function in the fPortfolio package: http://help.rmetrics.org/fPortfolio/html/class-fPORTFOLIO.html
- Here is a solution from David Ruppert's "Statistics and Finance" book: http://www.stat.tamu.edu/~ljin/Finance/chapter5... | null | CC BY-SA 2.5 | null | 2010-08-15T12:30:19.470 | 2010-08-15T12:30:19.470 | null | null | 5 | null |
1708 | 1 | null | null | 1 | 2265 | I have weights of SNP variation (output through Eigenstrat program) for each SNP for the three main PCs. I wish to reduce my list of SNPs to those that show maximum differentiation between the three PCs. Can anyone help me with which statistical method to use to do this.
say, if each PC describes the magnitude of vari... | Variation in PCA weights | CC BY-SA 2.5 | null | 2010-08-15T14:10:53.410 | 2011-03-25T20:32:46.083 | 2011-03-25T20:32:46.083 | 930 | 952 | [
"pca",
"genetics"
] |
1709 | 1 | 1717 | null | 6 | 2780 | I have collected positional data. To visualize the data, I'd like to draw a 'typical' outcome of an experiment.
The data comes from a few hundred experiments, where I identify a variable number of objects at different positions relative to the origin in 2D. Thus, I can calculate the average number of objects, as well ... | How to draw a probable outcome from a distribution? | CC BY-SA 2.5 | null | 2010-08-15T14:30:23.597 | 2010-08-16T10:40:58.773 | 2010-08-15T20:04:03.057 | 198 | 198 | [
"distributions",
"data-visualization"
] |
1710 | 2 | null | 36 | 3 | null | I've recently been to a conference and one of the speakers gave this very interesting example (although the point was to illustrate something else):
- Americans and English eat a lot of fat food. There is a high rate of cardiovascular diseases in US and UK.
- French eat a lot of fat food, but they have a low(er) rate... | null | CC BY-SA 2.5 | null | 2010-08-15T15:33:31.153 | 2010-08-15T15:33:31.153 | null | null | 582 | null |
1711 | 2 | null | 1709 | 0 | null | One thing that you could do is to plot the position of all your experiments in the 2D plane, one point for each object, maybe colored by experiment (if you have a lot of experiments you may just plot a random subset of them).
If there is a pattern in the position of the objects it should emerge when doing this.
Also, d... | null | CC BY-SA 2.5 | null | 2010-08-15T15:47:36.260 | 2010-08-15T15:47:36.260 | null | null | 582 | null |
1712 | 2 | null | 1709 | 0 | null | Maybe you could use a [smoothed scatterplot](http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=139)? It is an analogy to kernel density approximation, but in 2D.
| null | CC BY-SA 2.5 | null | 2010-08-15T16:05:02.070 | 2010-08-15T16:05:02.070 | null | null | null | null |
1713 | 1 | 1738 | null | 13 | 6223 | For some measurements, the results of an analysis are appropriately presented on the transformed scale. In most of the cases, however, it's desirable to present the results on the original scale of measurement (otherwise your work is more or less worthless).
For example, in case of log-transformed data, a problem with... | Express answers in terms of original units, in Box-Cox transformed data | CC BY-SA 3.0 | null | 2010-08-15T17:01:49.620 | 2013-05-15T01:43:43.250 | 2017-04-13T12:44:27.570 | -1 | 339 | [
"data-transformation",
"confidence-interval",
"t-test",
"interpretation"
] |
1714 | 2 | null | 196 | 3 | null | [Viewpoints](https://www.assembla.com/wiki/show/viewpoints) is useful for multi-variate data sets.
| null | CC BY-SA 3.0 | null | 2010-08-15T17:26:03.560 | 2012-11-08T22:14:04.253 | 2012-11-08T22:14:04.253 | 957 | 957 | null |
1715 | 2 | null | 726 | 9 | null | >
To understand God's Thoughts
we must study statistics
for these are the measure
of His purpose.
--Florence Nightingale
| null | CC BY-SA 2.5 | null | 2010-08-15T18:36:58.477 | 2010-12-03T04:05:19.333 | 2010-12-03T04:05:19.333 | 795 | null | null |
1716 | 2 | null | 1709 | 2 | null | To summarise (please correct me if I'm wrong):
- You have a set of points for a number of parameters/states.
- The points provide a joint distribution of the parameters states
- You want to simulate from a model using some typical states.
The problem you have is that you can't write down a nice closed form density... | null | CC BY-SA 2.5 | null | 2010-08-15T18:48:27.970 | 2010-08-16T09:31:44.377 | 2010-08-16T09:31:44.377 | 8 | 8 | null |
1717 | 2 | null | 1709 | 4 | null | I also think that it's not clear what you want. But if you want a set of deterministically chosen points, so that they preserve the moments of the initial distribution, you can use the sigma point selection method that applies to the [unscented Kalman filter](http://en.wikipedia.org/wiki/UKF#Unscented_Kalman_filter). ... | null | CC BY-SA 2.5 | null | 2010-08-15T19:14:50.803 | 2010-08-16T10:40:58.773 | 2010-08-16T10:40:58.773 | 339 | 339 | null |
1718 | 2 | null | 1308 | 18 | null | This is a counting problem: there are $b^n$ possible assignments of $b$ birthdays to $n$ people. Of those, let $q(k; n, b)$ be the number of assignments for which no birthday is shared by more than $k$ people but at least one birthday actually is shared by $k$ people. The probability we seek can be found by summing t... | null | CC BY-SA 3.0 | null | 2010-08-15T22:03:07.387 | 2016-10-14T20:56:33.213 | 2016-10-14T20:56:33.213 | 919 | 919 | null |
1719 | 1 | null | null | 8 | 3168 | Well, I'm an engineer by day. Although most of my work revolves around modeling, we generally do pretty basic stuff. An "Advanced" model would be a monte carlo simulation validated using R2 tests.
Currently, in my field, there is a lot of research using Logistic and bayesian analysis.
My question is, which courses wo... | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | CC BY-SA 2.5 | null | 2010-08-15T22:51:09.993 | 2012-08-03T07:55:05.020 | 2010-08-15T23:22:39.817 | 159 | 59 | [
"bayesian",
"logistic"
] |
1720 | 2 | null | 1713 | 11 | null | If the Box-Cox transformation yields a symmetric distribution, then the mean of the transformed data is back-transformed to the median on the original scale. This is true for any monotonic transformation, including the Box-Cox transformations, the IHS transformations, etc. So inferences about the means on the transform... | null | CC BY-SA 2.5 | null | 2010-08-15T23:35:52.447 | 2010-08-15T23:35:52.447 | null | null | 159 | null |
1721 | 2 | null | 1095 | 0 | null | So it turns out the first assumption was actually correct: U is indeed the first k eigenvectors of C, that we calculate from G by means of the eigendecompposition $(X_tVD^{-\frac12})D^{1/2} = X_tV$.
| null | CC BY-SA 2.5 | null | 2010-08-15T23:39:20.410 | 2010-08-16T21:27:59.097 | 2010-08-16T21:27:59.097 | 282 | 282 | null |
1722 | 2 | null | 596 | 2 | null | [This paper](https://web.archive.org/web/20151223091555/http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.120.3681&rep=rep1&type=pdf) is very interesting, has good results, and is easy to apply to discriminative models.
And the term is semi-supervised learning, not unsupervised learning.
| null | CC BY-SA 4.0 | null | 2010-08-15T23:43:45.330 | 2022-11-24T03:18:28.493 | 2022-11-24T03:18:28.493 | 362671 | 959 | null |
1723 | 2 | null | 1645 | 13 | null | For normality, actual Shapiro-Wilk has good power in fairly small samples.
The main competitor in studies that I have seen is the more general Anderson-Darling, which does fairly well, but I wouldn't say it was better. If you can clarify what alternatives interest you, possibly a better statistic would be more obvious... | null | CC BY-SA 4.0 | null | 2010-08-15T23:59:19.030 | 2019-10-14T01:50:09.690 | 2019-10-14T01:50:09.690 | 805 | 805 | null |
1724 | 2 | null | 1713 | 6 | null | If you want to do inference about means on the original scale, you could consider using inference that doesn't use a normality assumption.
Take care, however. Simply plugging through a straight comparison of means via say resampling (either permutation tests or bootstrapping) when the two samples have different varian... | null | CC BY-SA 3.0 | null | 2010-08-16T00:30:59.557 | 2013-05-15T01:38:09.353 | 2013-05-15T01:38:09.353 | 805 | 805 | null |
1725 | 2 | null | 1719 | 2 | null | I've only had a little look at this lecture series on Machine Learning, but it looks good.
- http://academicearth.org/courses/machine-learning
[Lecture 11](http://freevideolectures.com/Course/2257/Machine-Learning/11) covers Bayesian Statistics and Regularization.
| null | CC BY-SA 2.5 | null | 2010-08-16T02:38:20.447 | 2010-08-16T02:38:20.447 | null | null | 183 | null |
1726 | 2 | null | 726 | 44 | null | >
Do not trust any statistics you did not fake yourself.
-- Winston Churchill
| null | CC BY-SA 2.5 | null | 2010-08-16T05:45:29.743 | 2010-12-03T04:01:19.320 | 2010-12-03T04:01:19.320 | 795 | 128 | null |
1727 | 2 | null | 196 | 3 | null | Python's [matplotlib](http://matplotlib.sourceforge.net/)
| null | CC BY-SA 2.5 | null | 2010-08-16T06:19:27.897 | 2010-08-16T06:19:27.897 | null | null | 961 | null |
1728 | 2 | null | 1719 | 4 | null | I would go straight to [VideoLectures.net](http://videolectures.net/Top/#o=top&t=vl). This is by far the best source--whether free or paid--i have found for very-high quality (both w/r/t the video quality and w/r/t the presentation content) video lectures and tutorials on statistics, forecasting, and machine learning. ... | null | CC BY-SA 2.5 | null | 2010-08-16T07:09:25.387 | 2010-08-16T07:14:37.997 | 2010-08-16T07:14:37.997 | 438 | 438 | null |
1729 | 1 | 1730 | null | 6 | 4088 | I am trying to format the output from pairwise.t.test into LaTeX, but have not found a way of doing this. Has anyone got any suggestions?
EDIT: As this is a one-time only report where I do need to customize the variable names, and row-/column headings, I was hoping to avoid using Sweave.
| Export/format output from pairwise.t.test to LaTeX | CC BY-SA 2.5 | null | 2010-08-16T08:11:26.317 | 2017-05-18T21:16:24.690 | 2017-05-18T21:16:24.690 | 28666 | 913 | [
"r",
"t-test"
] |
1730 | 2 | null | 1729 | 7 | null | Does this help?
```
> library(xtable)
> attach(airquality)
> res <- pairwise.t.test(Ozone, Month)
> xtable(res$p.value, caption=res$method)
% latex table generated in R 2.9.2 by xtable 1.5-6 package
% Mon Aug 16 04:24:21 2010
\begin{table}[ht]
\begin{center}
\begin{tabular}{rrrrr}
\hline
& 5 & 6 & 7 & 8 \\
\hline... | null | CC BY-SA 2.5 | null | 2010-08-16T08:27:13.563 | 2010-08-16T08:27:13.563 | null | null | 251 | null |
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