DAT301 HW3

An ioslides presentation explaining simple linear regression using the mtcars dataset, including the regression model, coefficient interpretation, ggplot visualizations, an interactive plotly graph, and a prediction example.

An introduction to hypothesis testing covering null and alternative hypotheses, the Z-test statistic, critical values, and p-values. Includes a full worked example, annotated normal distribution plots, and an interactive 3D surface showing how sample size and standard deviation affect the Z-statistic. Type I and Type II errors and statistical power are also discussed. All visualizations built in R using ggplot2 and plotly.

Introduction to simple linear regression with examples and visualization Statistical analysis of linear relationships using R

Feature selection, data preprocessing, model tuning, validation, variable importance, and actionable insights for optimization.

In this part we finish getting the top genes that the probe IDs matched a gene from 15 groups of 20 top genes in top 10 over expressed and top 10 under expressed using group avg/ group median, plus the available genes from the study's published article. We will use this to build the machine to predict the 3 classes of lymphoma within the 3 major group genes, study genes, and all genes by their subcategory within larger class, and by the new 4 classifications this study is proposing. The machine learning will be next part in Part 4.