Resource Library

This article addresses a dataset on public school expenditures and SAT performance. Key Words: Multiple regression; Omitted variable bias; Partial correlation; Scatterplot.

This article describes a dataset on body temperature, gender, and heart rate. It addresses concepts like true means, confidence intervals, t-statistics, t-tests, the normal distribution, and regression.

The datasets described in this article contain information for all National Football League (NFL)regular season and playoff games played from 1993 to 1996. In addition to game scores, the data give oddsmakers' pointspreads and over/under values for each game. Key Words: Predictions; Wagering.

The dataset described in this article contains data on retired Major League Baseball players, eligible for the MLB Hall of Fame. The data can be used to illustrate descriptive statistical methods (numerical, graphical, and tabular) or inferential statistics (hypothesis testing, confidence intervals, etc.). The data is in .dat format.

This site is part of an online textbook and discusses non-parametric tests. It explains the calculations, assumptions, and uses of the Wilcoxon ranked sum test. How to treat unpaired and paired samples is covered. Related exercises and answers are included.

This section of an online textbook discusses calculating the exact probability using observed sets of frequencies, constructing frequency tables, and computing p-values. Exercises and answers are provided.

This topic from an online textbook discusses standard error, confidence interval, and significance testing for a difference in percentages or proportions. It also covers paired alternatives and standard error of a total. Exercises and answers are also provided.

This applet performs a hypothesis test for the mean of a single normal population, variance known. Users set the hypothesized mean, true mean, variance, and appropriate alternative hypothesis. The applet plots a representative distribution under the given values with power shaded in blue and significance level shaded in red.

This text document lists detailed learning objectives for introductory statistics courses. Learning objectives are brief, clear statements of what learners will be able to perform at the end of a course. These objectives were developed for a one semester general education introductory statistics course. The objectives cover the broad categories of Graphics, Summary Statistics, The Normal Distribution, Correlation and Scatterplots, Introduction to Regression, Two way Tables, Data Collection and Surveys, Basic Probability, Sampling Distributions, Confidence Intervals, Tests of Hypothesis, and T-distributions.

This website provides data files, examples, guides that are referenced in David Howell's textbook published in 2013. There is also a student manual and links to other useful websites.