# Topic - Bentley University MA214

MA214 is an undergraduate statistics course at Bentley University that builds on the basic managerial statistics course taken by all students. The description from the course catalog can be found here.

It covers hypothesis tests, analysis of variance, multiple regression, and contingency tables.

## Review of statistical inference

- How to compute a confidence interval for a population mean
- How to compute a confidence interval for a population mean using z-scores
- How to compute a confidence interval for the population proportion
- How to do a hypothesis test for a mean difference (matched pairs)
- How to do a hypothesis test for a population proportion
- How to do a hypothesis test for population variance
- How to do a hypothesis test for the mean with known standard deviation

## Two populations

- How to compute a confidence interval for a mean difference (matched pairs)
- How to choose the sample size in a study with two population means
- How to compute a confidence interval for the difference between two means when both population variances are known
- How to compute a confidence interval for the difference between two means when population variances are unknown
- How to compute a confidence interval for the difference between two proportions
- How to compute a confidence interval for the ratio of two population variances
- How to do a hypothesis test for the difference between means when both population variances are known
- How to do a hypothesis test for the difference between two proportions
- How to do a hypothesis test for the ratio of two population variances
- How to do a Kruskal-Wallis test
- How to do a one-sided hypothesis test for two sample means
- How to do a Wilcoxon rank-sum test
- How to do a Wilcoxon signed-rank test
- How to do a Wilcoxon signed-rank test for matched pairs

## Variance inference

## Chi-squares tests

- How to perform a chi-squared test on a contingency table
- How to do a goodness of fit test for a multinomial experiment

## ANOVA

- How to do a one-way analysis of variance (ANOVA)
- How to do a two-way ANOVA test with interaction
- How to do a two-way ANOVA test without interaction
- How to compute Fisher’s confidence intervals
- How to perform an analysis of covariance (ANCOVA)
- How to perform post-hoc analysis with Tukey’s HSD test
- How to use Bonferroni’s Correction method

## Regression

- How to fit a linear model to two columns of data
- How to compute a confidence interval for the expected value of a response variable
- How to compute R-squared for a simple linear model
- How to predict the response variable in a linear model

## Nonparametric tests

- How to create a QQ-plot
- How to test data for normality with Pearson’s chi-squared test
- How to test data for normality with the D’Agostino-Pearson test
- How to test data for normality with the Jarque-Bera test

Content last modified on 03 August 2023.

Contributed by Nathan Carter (ncarter@bentley.edu)