Introduction

This course is an introduction to applied data analysis. We will explore data sets, examine various regression models for the data, assess the validity of their assumptions, and determine which conclusions we can make (if any). Data analysis is a bit of an art; there may be several valid approaches. We will strongly emphasize the importance of critical thinking about the data and the question of interest. Our overall goal is to use a basic set of modeling tools to explore and analyze data and to present the results in a scientific report. We then consider simple linear regression, a model that uses only one predictor. After briefly reviewing some linear algebra, we turn to multiple linear regression, a model that uses multiple variables to predict the response of interest. For all models, we will examine the underlying assumptions. More specifically, do the data support the assumptions? Do they contradict them? What are the consequences for inference? Also, we will explore some nonlinear models and data transformations. Finally, we discuss Linear regression based on the categorical with some applications. The whole lecture note for this module can be found by clicking here. However, this textbook will be used for the exercise classes for the module (STAT332). The notes have not completed yet, I will still add new exercises and fix typos. If you find any typos or mistakes or have any question, I will appreciate if you email me at Aalharbi10@ksu.edu.sa

Note: My colleague Abdulrahman Alfaifi is posting very helpful youtube videos discussing some exercises and past-exam questions you can reach that easily by clicking here.