Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using R.
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This work came from the Stats525 course in Regression Analysis in R during the Spring semester of 2021 with Professor Maryclare Griffin
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