How to draw a probable outcome from a distribution?

To visualize the data, I'd like to draw a 'typical' outcome of an experiment.A plot of the 'typical' outcome would then have the average (or possibly mode) number of objects, say, 5.

Modeling response times

not an expert, but maybe the ex-gaussian (gaussian plus exponential distribution)?pdf

In the framework of cognitive
processes, this convolution can be
seen as representing the overall
distribution of RT [Response Time] resulting from two

Using mixed effects modelling to estimate and compare variability

Can I use mixed effects analysis to assess whether this within-person variability is, on average, different between the two groups?seed(1)

group_A_base_sd = 1
group_B_base_sd = 2
within_group_sd_of_sds =.

Intraclass correlation and aggregation

Thus, my questions are:

What descriptive labels would you attach to different values of the intra-class correlation?, the aim is to actually relate the values of the intra-class correlation to qualitative language such as: "When the intraclass correlation is greater than x, it suggests that the attitudes are modestly/moderately/strongly shared across team members.

How to fit a negative binomial distribution in R while incorporating censoring

I need to fit $Y_{ij} \sim NegBin(m_{ij},k)$, i.a negative binomial distribution to count data.

If the t-test and the ANOVA for two groups are equivalent, why aren't their assumptions equivalent?

I'm sure I've got this completely wrapped round my head, but I just can't figure it out.So why is it that the t-test is equivalent to ANOVA with two groups?

Appropriate normality tests for small samples

So far, I've been using the Shapiro-Wilk statistic in order to test normality assumptions in small samples.The fBasics package in R (part of Rmetrics) includes several normality tests, covering many of the popular frequentist tests -- Kolmogorov-Smirnov, Shapiro-Wilk, Jarque–Bera, and D'Agostino -- along with a wrapper for the normality tests in the nortest package -- Anderson–Darling, Cramer–von Mises, Lilliefors (Kolmogorov-Smirnov), Pearson chi–square, and Shapiro–Francia.

Based on z-score, is it possible to compute confidence without looking at a z-table?

Is it possible to compute confidence without looking up a z-table?A z-table gives you values of the cumulative distribution function for the standard (i.

Comparing 2 independent non-central t statistics

The sample Sharpe ratio is the sample mean divided by the sample standard deviation.Up to a constant factor ($\sqrt{n}$, where $n$ is the number of observations), this is distributed as a (possibly non-central) $t$-statistic.

Constructing smoothing splines with cross-validation

Can someone provide me with a book or online reference on how to construct smoothing splines with cross-validation?I would also appreciate an overview of whether this is smoothing technique is a good one for smoothing data and whether there are any disadvantages of which a non-statistician needs to be aware.

Using information geometry to define distances and volumes…useful?

I came across a large body of literature which advocates using Fisher's Information metric as a natural local metric in the space of probability distributions and then integrating over it to define distances and volumes.But are these "integrated" quantities actually useful for anything?

Do working statisticians care about the difference between frequentist and Bayesian inference?

It is a trade-off between whether the subjective element of the Bayesian approach (which is itself debatable, see e.I think Bayesian statistics come into play in two different contexts.

Is there a way to remember the definitions of Type I and Type II Errors?

)

Since type two means "False negative" or sort of "false false", I remember it as the number of falses.If you believe such an argument:

Type I errors are of primary concern
Type II errors are of secondary concern

Note: I'm not endorsing this value judgement, but it does help me remember Type I from Type II.

Survival Analysis tools in Python

I am wondering if there are any packages for python that is capable of performing survival analysis.I have been using the survival package in R but would like to port my work to python.

Express answers in terms of original units, in Box-Cox transformed data

So I only can make inferences about the difference (or the ratio) of the medians on the original scale of measurement.However, if we apply t-tools to Box-Cox transformed data , we will get inferences about the difference in means of the transformed data.

What are alternatives to broken axes?

]

(3) You can show the broken plot side-by-side with the same plot on unbroken axes.(4) In the case of your bar chart example, choose a suitable (perhaps hugely stretched) vertical axis and provide a panning utility.

Shall I trust AIC (non-full model) or slope (full model)?

The purpose to run regressions for butterfly richness again 5 environmental variables is to show the importance rank of the independent variables mainly by AIC.In non-full models, they reveal that variable A tends to be more influential than the others by delta AIC.

Video/Audio online material for getting into Bayesian analysis and logistic-regressions

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.

Variation in PCA weights

I have weights of SNP variation (output through Eigenstrat program) for each SNP for the three main PCs.g:

SNPNam PC1-wt PC 2-wt PC3-wt
SNP_1 -1.

I just installed the latest version of R. What packages should I obtain?

Duplicate thread: 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?