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## Main Question or Discussion Point

Next semester is my last. I have one Math elective left to pick, how would you rank the following Math courses in terms of difficulty of the material:

Math 341 - Introduction to Statistics (3-0-3)

Prerequisite: Math 244 or Math 333. Covers the theory and applications of classical statistical inference. Topics include sampling distributions, point and interval estimation, criteria of good estimators, maximum likelihood estimators and their large sample properties, statistical hypotheses and tests, including most powerful and uniformly most powerful tests and likelihood ratio tests, classical tests of parametric hypotheses about means and variances of normal populations, tests for proportion, chi-square tests of homogeneity, independence, goodness-of-fit, sign test and Wilcoxon test.

Math 344 - Regression Analysis (3-0-3)

Prerequisite: Math 333 or Math 341. An introduction to statistical data analysis using regression techniques. Topics include least squares estimation, hypothesis testing, prediction, regression diagnostics, residual analysis, variance stabilizing transformations, regression using indicator variables, variable selection, and model building.

Math 341 - Introduction to Statistics (3-0-3)

Prerequisite: Math 244 or Math 333. Covers the theory and applications of classical statistical inference. Topics include sampling distributions, point and interval estimation, criteria of good estimators, maximum likelihood estimators and their large sample properties, statistical hypotheses and tests, including most powerful and uniformly most powerful tests and likelihood ratio tests, classical tests of parametric hypotheses about means and variances of normal populations, tests for proportion, chi-square tests of homogeneity, independence, goodness-of-fit, sign test and Wilcoxon test.

Math 344 - Regression Analysis (3-0-3)

Prerequisite: Math 333 or Math 341. An introduction to statistical data analysis using regression techniques. Topics include least squares estimation, hypothesis testing, prediction, regression diagnostics, residual analysis, variance stabilizing transformations, regression using indicator variables, variable selection, and model building.