Bayesian Inference and Data Analysis
... • Despite differences in many simple analyses, results obtained using the two different procedures yield superficially similar results (especially in asymptotic cases) • Bayesian methods can be easily extended to more complex problems • Usually Bayesian models work better with less data • Bayesian m ...
... • Despite differences in many simple analyses, results obtained using the two different procedures yield superficially similar results (especially in asymptotic cases) • Bayesian methods can be easily extended to more complex problems • Usually Bayesian models work better with less data • Bayesian m ...
Lecture 2
... Delete any variable with a low R2 . Look at partial correlations – pairs of variables with large partial correlations share variance with one another but not with the remaining variables – this is problematic. Kaiser’s MSA will tell you, for each variable, how much of this problem exists. The smalle ...
... Delete any variable with a low R2 . Look at partial correlations – pairs of variables with large partial correlations share variance with one another but not with the remaining variables – this is problematic. Kaiser’s MSA will tell you, for each variable, how much of this problem exists. The smalle ...
Describing Data: Numerical
... Arithmetic mean The arithmetic mean of a collection of numerical values is the sum of these values divided by the number of values. The symbol for the population mean is the Greek letter μ (mu), and the symbol for a sample mean is X (X-bar) A population parameter is any measurable characteristic of ...
... Arithmetic mean The arithmetic mean of a collection of numerical values is the sum of these values divided by the number of values. The symbol for the population mean is the Greek letter μ (mu), and the symbol for a sample mean is X (X-bar) A population parameter is any measurable characteristic of ...
7.samplingdist - Illinois State University Department of Psychology
... will approach a normal distribution with a mean of and a standard deviation of as n approaches infinity n ...
... will approach a normal distribution with a mean of and a standard deviation of as n approaches infinity n ...
EDF 6472
... Since we know the population standard error, the Central Limits Theorem tells us that the sampling distribution of the means is indeed normal, so we may ask ourselves to determine the value of the z-scores that cut off the top and bottom 2.5% of the distribution. These will define our area of rejec ...
... Since we know the population standard error, the Central Limits Theorem tells us that the sampling distribution of the means is indeed normal, so we may ask ourselves to determine the value of the z-scores that cut off the top and bottom 2.5% of the distribution. These will define our area of rejec ...
Manipulating Data (Linear Transformations)
... 1) Find the mean and std. deviation and the shape 2) Take the entire set of data and convert to z-scores (subtract the mean and then divide by the std. deviation). Do this in L2. 3) Calculate the new mean and std. dev (of L2). Why is this so? ...
... 1) Find the mean and std. deviation and the shape 2) Take the entire set of data and convert to z-scores (subtract the mean and then divide by the std. deviation). Do this in L2. 3) Calculate the new mean and std. dev (of L2). Why is this so? ...
Introduction to Statistics
... of the entire population. Taking a census is very costly. The population size is usually indicated by a capital N. Statistic: A statistic is a measure that is derived from the sample data. For example, the sample mean, X̄ , and the sample standard deviation, s, are statistics. They are used to estim ...
... of the entire population. Taking a census is very costly. The population size is usually indicated by a capital N. Statistic: A statistic is a measure that is derived from the sample data. For example, the sample mean, X̄ , and the sample standard deviation, s, are statistics. They are used to estim ...
SAS--Proc Means (Descriptive Stats)
... The keywords below are used to specify the statistics that you want Proc Means to compute and the order to display them in the output. Descriptive statistics keywords used in Proc Means: N (for each variable, gives number of rows in data set with non-missing data) NMISS (for each variable, gives num ...
... The keywords below are used to specify the statistics that you want Proc Means to compute and the order to display them in the output. Descriptive statistics keywords used in Proc Means: N (for each variable, gives number of rows in data set with non-missing data) NMISS (for each variable, gives num ...
Slides Day 3
... that the test will detect a true difference of a specified type. • As in talking about p-values and confidence levels, the reference category for "probability" is the sample. • Thus, power is the probability that a randomly chosen sample o satisfying the model assumptions o will give evidence of a d ...
... that the test will detect a true difference of a specified type. • As in talking about p-values and confidence levels, the reference category for "probability" is the sample. • Thus, power is the probability that a randomly chosen sample o satisfying the model assumptions o will give evidence of a d ...