Applied Linear Regression Analysis (using R,SPSS,SAS,Python)

From basics to advanced level training and practices in doing linear regression analysis using R, SPSS, SAS, Python

What you'll learn
Understanding how linear regression analysis works, including theoretical foundations, techniques, worked examples, live demonstrations of four software
Fundamentals and requirements for doing good linear regression, including data requirements, and tools for preliminary investigations (eg graphical plots)
How to use a variety of tools, measures, and metrics for evaluating if your linear regression model is a good fit for your data, and ways to improve the fit
Doing linear regression analysis in any of (or all) the four software covered, namely R, SPSS, SAS, Python. You will see learn from demonstrations of software
This course covers applied linear regression analysis fully. So you should not need to do a similar course again(except to learn to use a different software)

Description
This course teaches you how to do linear regression analysis from the very basic level, to advanced/expert level, depending on your needs.

A fundamental teaching (or knowledge transfer) philosophy which I have adopted for this course is that students should learn and understand the 'fundamentals of the analytics methodology' first, before learning how to apply those methodologies to do data analysis via software. This is different from some (similar) courses where the focus tends to be on teaching you how to use a software for running regression analysis (without deep understanding of regression methodology itself). My intention is for you to develop mastery of regression analysis as a modelling technique first, and have the confidence to tackle any modelling/prediction problem which requires linear regression modelling. This means that the first part of the course is largely software-independent, although I use R-software to demonstrate the concepts and also to help with your understanding and interpretation of software outputs for regression analysis.

I believe that once you learn this important methodological part well, you should be able to use any software for applied regression analysis. As you will find out, the codes and steps/processes for linear regression analysis are very similar across the various software (including the four which we use in this course). Critically important also, is that outputs from regression analysis are incredibly and understandably similar in structure, across most software. Hence, my view (and reason for adopting this approach) is that, if you understand the fundamentals of regression methodology well initially, you should then be able to subsequently use any software and interpret any regression analysis outputs, and also should easily be able to move across and use a variety of software (provided you learn how to use or code in that particular software, of course).

The course is structured sequentially as follows.

1. Introduction to, and motivation for, doing linear regression. Here you are introduced to the concept and methodology of linear regression analysis and some practical examples of its applications are provided. We also review four real (publicly available) data sets which are used in the course, for demonstrating how to apply linear regression analysis in real life.

2. We get further into the concept and methodology of linear regression by considering the simple linear regression (SLR) situation. This is the situation where you would be building a model to explain or predict a dependent variable (Y) based on just one independent or predictor variable (X). This introductory part allows me to introduce terminologies and methodologies used for linear regression analysis. I use real-life data to demonstrate the methodologies, and during the applied theory part we use R-software to demonstrate the methodologies and to explain the typical outputs from software. For those interested, I also (optionally) cover the basics of the mathematical and statistical theory behind the Ordinary Least Squares method for Simple Linear Regression (SLR) Analysis. Those not interested in that aspect (theory/mathematical/statistical methodology) can skip some of those optional Lectures.

3. After studying SLR (simple linear regression), we move to the more complex multiple linear regression (MLR) situation, where you would now be modelling or predicting a dependent variable (Y) based on several independent or predictor variables (say several X- variables). We also learn the methodologies (and some theory) for MLR, and apply the methodologies on real data. Once again we use R-software for the demonstrations of regression analysis and interpreting outcomes. There are also optional lectures which cover the mathematical/statistical theory behind the Ordinary Least Squares methodology for Multiple Linear Regression (MLR) analysis, for those students interested in that aspect.

4. Note that in both SLR and MLR Sections, we cover areas such as (i) data and modelling requirements necessary for good linear regression analysis, (ii) exploratory data analysis required for regression analysis, (iii) estimating parameters/regression coefficients, (iv)interpreting outcomes of regression analysis from software outputs, (v) post-modelling diagnostics using visual plots and model-assessment metrics and measures, (vi) hypothesis testing about regression coefficients, and to check that modelling requirements are met, (vii) stepwise regression analysis for multiple linear regression (MLR) analysis.

5. After covering the 'regression methodologies in the earlier parts (Sections) of the course, we then move to learning how to run regression analysis using a variety of software. We spend many lectures in several Sections introducing and demonstrating how to use four different software for linear regression analysis. We cover each of the four software used here (R, SPSS, SAS, Python) in separate Sections, where (for each software) we demonstrate how to use it for (i) doing exploratory plots, (ii) running regression modelling, (iii) performing post-model diagnostics (visual plots, measures, metrics, hypothesis testing), and (iv) performing stepwise regression analysis, where possible. All these various software are demonstrated on several real data sets, which exhibit different aspects of regression modeling methodologies and outcomes. It is important to emphasise that all these software have different capabilities, which means that some parts of what we have learnt may not be able to be demonstrated in a particular software. For your information, R-software and Python are both free open-source software and one runs regression modelling in them typically via direct code. On the other hand SPSS and SAS are commercial and proprietary (i.e not open-source) software. Typically they have Graphical User Interface (GUI) which allows a user to run regression analysis purely by 'point-and-click' (if you cannot code in their proprietary code), but they can also run regression analysis purely via code.

This course will also allow you to check out and compare these various software, for regression analysis, if you are interested in that….and you don't have to do several courses for that.

Who this course is for:
Statistical modellers, data analysts, data scientists, students, and researchers who want to properly understand how linear regression works in practice/applications, AND/OR people who are interested in learning how to do regression analysis using one or more of the software used in this course (i.e SAS, R, SPSS, Python).
People who are interested in understanding how the four different software (used in this course) are used for linear regression analysis