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May 03, 2026
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Undergraduate Catalog 2026-2027
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MAT 152 Statistics II Credits: (3)
A continuation of Statistics I to include the topics two-sample analysis, linear and multiple regression, correlation, analysis of variance, non-parametric statistics and Chi-square goodness of fit. Time series analysis and/or statistical process control as time permits. Computer software and graphing calculator applications will be an integral component of this course. A graphing calculator with specific statistical capabilities will be required. Credit will not be given for both MAT 152 and BUS 220. Prerequisite(s): MAT 151 or BUS 219 or equivalent.
Designation(s): Liberal Arts
Learning Outcomes
- Use the graphing calculator and statistical software to complete and interpret statistical analysis calculations.
- Construct and interpret confidence intervals for comparing two means and two proportions.
- Perform significant tests for comparing two means and two proportions and interpret p-values in context.
- Perform one and two factor ANOVA tests and interpret results using multiple comparisons or interactions with statistical software.
- Calculate the linear correlation coefficient, the coefficient of determination and the least squares regression line, interpret their values in context and assess the fit of the linear model with analysis of scatterplots and residuals, re-expressing the data where appropriate.
- Test the usefulness of the regression equation with inference for regression by calculating confidence internals and performing hypothesis tests for the slope and intercept of the regression line.
- Compare and select the best model using multiple regression techniques including analysis of correlation matrices, scatterplot matrices, collinearity, and best subsets.
- Read and interpret multiple regression statistical software outputs in reference to the application problem.
- Distinguish between chi-square tests for independence, homogeneity and goodness of fit in application problems, calculate the test statistic for claims about categorical data and interpret results in context.
- Perform significance tests using nonparametric statistics: sign, Tukey’s, signed ranks, rank sum, rank correlation, Kruskal Wallis and runs for randomness tests.
- Evaluate and interpret components of classical time series models.
- Construct and interpret control charts to look for changes and patterns over time for both quantitative and qualitative data.
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