STAT6021-Linear_Models_for_Data_Science

Language: R

This repository contains assignments and projects for a course on basic statistics for data science. Course topics included single linear regression, multiple linear regression, logistic regression, transformation, ANOVA F-tests, stepwise/backwards/forwards model creation, and linearity assumptions.

homework1 parts 1-4 (Simple Linear Regressions, Transformations, and Regression Assumptions)

• Summary: conducted t-tests, confidence intervals, and linear regressions on several different data sets. Assessed assumptions of regressions and conducted some transformations. R.
• File type: .Rmd

hw2 Q1-4 (Categorical Variables, Interaction Effects, ANOVA F-tests and Partial F-tests, Correlation Matrices)

• Summary: conducted analysis of multiple linear regressions. Played with model modficiation based on ANOVA (global) and partial F-tests. Utilized categorical variables and interaction effects as well. R.
• File type: .Rmd

hw3 and hw4 all questions (Outlier Analysis using Bonferroni/Cook/DFFITS Methods, Logistic Regression, ROC, Confusion Matrix)

• Summary: conducted outlier analysis using a variety of methods, interpreted a logistic regression's coefficients and performance via ROC/confusion matrices, and evaluated multinomial logistic regression using Wald's test statistic. R.
• File type: .Rmd

full_model_code.R and Project_1_Final.pdf (BlueNile Diamonds)

• Summary: first course group project. Conducted an experiment to see which variables would best predict diamond price. Compared multiple model approach on subsets of data vs a singular model. R.
• File type: .R

Stats_proj2_scratch_joe.R (Wine Analysis)

• Summary: second course group project in which we analyzed which variables best predicted wine quality.
• File type: .R