Resource Library

This lesson introduces the Central Limit Theorem and discusses it in terms of the normal distribution, binomial distribution, and Poisson distribution.

This lesson introduces confidence intervals and how to calculate them. A multiple choice test is given at the end.

This lesson introduces two sample hypothesis testing for means and discusses the one-tailed and two-tailed t-tests.

This applet relates the pdf of the Normal distribution to the cdf of the Normal distribution. The graph of the cdf is shown above with the pdf shown below. Click "Move" and the scroll bar will advance across the graph highlighting the area under the pdf in red. The z-score is shown as well as the probability less than z (F(z)) and the probability greater than z (1-F(z)).

This online resource is intended to help students understand concepts from probability and statistics and covers many topics from introductory to advanced. You can follow the progression of the text, or you can click on a topic on the left. Key Words: Alpha Reliability; Analysis of Covariance (ANCOVA); Analysis of Variance (ANOVA); Bayesian Analysis; Bias; Binomial regression; Bonferroni adjustment; Bootstrapping; Categorical modeling; Central limit theorem; Chi-squared test; Clinical significance; Cluster analysis; Coefficient of variation; Confidence Intervals; Contingency Table; Controlled trial; Confounders; Correlation; Dimension reduction; Discriminant function analysis; Frequency; Normal; Poisson; Probability Distribution; Effect; Error; Factor Analysis; Goodness of Fit; Heteroscedasticity; Hypothesis Testing; Independence, Interactions; Kappa Coefficient; Latin Squares; Least Squares Means; Likert scales; Linear Regression; Logistic Regression; Multivariate ANOVA (MANOVA); Mixed Modeling; Multiple Linear Regression; Nonparametric models; Odds ratio; P Values; Path Analysis; Percentiles; Polynomial Regression; Power; PRESS; Probability; Relative Frequency; Repeated Measures; Sample Size; Sampling; Sensitivity; Stepwise regression; Structural equation modeling; T Test; Transformation; Validity.

This tutorial explains the theory and use of the Sign Test and demonstrates it with an example on intervention methods. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of two-way ANOVA and demonstrates it with an example on final exam scores. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of the Wilcoxon Matched-Pairs Ranks test and demonstrates it with an example on project quality. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of Pearson's Product Moment Coefficient of Correlation and demonstrates it with an example on GPA and test scores. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of One-Way ANOVA and demonstrates it with an example on final exam scores. Data is given as well as SPSS and Minitab code.