Regression Statistics

Gain Important and Highly Marketable Skills in Regression Analysis - Tame the Regression Beast Today!

Last updated 2022-01-10 | 4.4

- Understand when to use simple
- multiple
- and hierarchical regression
- Understand the meaning of R-Square and the role it plays in regression
- Assess a regression model for statistical significance
- including both the overall model and the individual predictors

What you'll learn

Understand when to use simple

multiple

and hierarchical regression

Understand the meaning of R-Square and the role it plays in regression

Assess a regression model for statistical significance

including both the overall model and the individual predictors

Effectively utilize regression models in your own work and be able to critically evaluate the work of others

Understand predicted values and their role in the overall quality of a regression model

Understand hierarchical regression

including its purpose and when it should be used

Use regression to assess the relative value of competing predictors

Make business decisions about the best models to maximize profits while minimizing risk

Critically evaluate regression models used by others

Learn how to conduct correlation and regression using both IBM SPSS and Microsoft Excel

* Requirements

* Many of the videos use SPSS in running regression models and some use the Microsfot Excel Data Analysis ToolPak. While SPSS is not required to understand the material or follow the videos
* if you want to reproduce the analyses on your own
* SPSS will be needed. However
* other software (such as R
* SAS
* or Minitab) can be used to reach the same statistical decisions about the regressions models (as are illustrated here).

Description

November, 2019.

Get marketable and highly sought after skills in this course while substantially increasing your knowledge of data analytics in regression. All course videos created and narrated by an award winning instructor and textbook author of quantitative methods.

This course covers running and evaluating linear regression models (simple regression, multiple regression, and hierarchical regression), including assessing the overall quality of models and interpreting individual predictors for significance. R-Square is explored in depth, including how to interpret R-Square for significance. Together with coverage of simple, multiple and hierarchical regression, we'll also explore correlation, an important statistical procedure that is closely related to regression.

By the end of this course you will be skilled in running and interpreting your own linear regression analyses, as well as critically evaluating the work of others. Examples of running regression in both SPSS and Excel programs provided. Lectures provided in high quality, HD video with course quizzes available to help cement the concepts. Taught by a PhD award-winning university instructor with over 15 years of teaching experience. At Quantitative Specialists, our highest priority is in creating crystal-clear, accurate, easy-to-follow videos.

Tame the regression beast once and for all – enroll today!

Who this course is for:

Anyone interested in learning more about regression analysis.
This course is not for those looking for a general introduction to statistics course. For this we recommend taking a look at our descriptive statistics or inferential statistics courses. (This course specializes in regression analysis.)
Those looking to increase their knowledge of regression.

Course content

6 sections • 21 lectures

Introduction 6 lectures

Introduction Preview 05:58

In this video, the author background, an overview of the topics covered, and the goals of the course are described.

Correlation - Part 1 Preview 06:47

In this video we take at an example using correlation, which measures the linear relationship between two variables (Part 1 of 2).

Note: The SPSS data files for the entire course can be downloaded here.

Correlation - Part 2 Preview 07:38

In this video we take at an example using correlation, which measures the linear relationship between two variables (Part 2 of 2).

Note: A copy of the SPSS output is provided (as Downloadable material) with this lecture.

Correlation With More Than Two Variables - Excel Preview 04:28

In this video we look at correlation between a number of different variables, with two variables correlated at a time. The resulting output produces a correlation matrix, which is a table of correlations. This example uses Microsoft Excel.

Note: The Excel data file and a copy of the output are provided (as Downloadable materials) with this lecture.

Correlation with More than Two Variables - SPSS Preview 05:24

Correlation

Simple Regression - One Predictor (or IV) 4 lectures

Simple Regression - Example 1 (Part 1) Preview 10:26

This video covers simple regression, which is used when there is one predictor (independent variable) and one criterion (dependent variable). (Part 1 of 2)

Note: The SPSS data files for the entire course are located in the Correlation - Part 1 folder.

Simple Regression - Example 1 (Part 2) Preview 06:46

This video covers simple regression, which is used when there is one predictor (independent variable) and one criterion (dependent variable). (Part 2 of 2)

Note: A copy of the SPSS output is provided (as Downloadable material) with this lecture.

Simple Regression - Example 2 Preview 11:22

This video covers a second example on simple regression.

Note: A copy of the SPSS output is provided (as Downloadable material) with this lecture.

Simple Regression

Multiple Regression - 2+ Predictors (IVs) 5 lectures

Multiple Regression - Part 1 Preview 06:42

This video covers multiple regression, which is used when there are two or more predictors (independent variables) and one criterion (dependent variable). (Part 1 of 2)

Note: The SPSS data files for the entire course are located in the Correlation - Part 1 folder.

Multiple Regression - Part 2 Preview 09:03

This video covers multiple regression, which is used when there are two or more predictors (independent variables) and one criterion (dependent variable). (Part 2 of 2)

Note: A copy of the SPSS output is provided (as Downloadable material) with this lecture.

Finding Predicted Values - Part 1 Preview 07:24

This video covers how to find predicted values in regression. Predicted values can be solved for in both simple and multiple regression (Part 1 of 2).

Note: The SPSS data files for the entire course are located in the Correlation - Part 1 folder.

Finding Predicted Values - Part 2 Preview 09:18

This video covers how to find predicted values in regression. Predicted values can be solved for in both simple and multiple regression.(Part 2 of 2)

Note: A copy of the SPSS output is provided (as Downloadable material) with this lecture.

Multiple Regression

Hierarchical Regression - 2+ Predictors (with order of entry) 3 lectures

Hierarchical Regression - Part 1 Preview 11:08

This video covers hierarchical regression, which is used when predictors are entered in multiple steps. Hierarchical regression allows one to assess the unique effect of one or more predictors that are added to the model in a later step in the analysis. (Part 1 of 2)

Note: The SPSS data files for the entire course are located in the Correlation - Part 1 folder.

Hierarchical Regression - Part 2 Preview 09:15

Note: A copy of the SPSS output is provided (as Downloadable material) with this lecture.

Hierarchical Regression

Regression in Microsoft Excel 2 lectures

Multiple Regression in Excel - Part 1 Preview 06:41

This video covers multiple regression in Microsoft Excel, for those who would like to see how to use Excel to run a regression (Part 1 of 2). Along with the quantitative predictors of SAT score and social support, we also have a categorical variable, gender, in this example.

Multiple regression is used when there are two or more predictors (independent variables) and one criterion (dependent variable).

Note: The data files for the entire course are located in the Correlation - Part 1 folder.

Multiple Regression in Excel - Part 2 Preview 09:36

This video covers multiple regression in Microsoft Excel, for those who would like to see how to use Excel to run a regression (Part 2 of 2). Along with the quantitative predictors of SAT score and social support, we also have a categorical variable, gender, in this example.

Multiple regression is used when there are two or more predictors (independent variables) and one criterion (dependent variable).

Note: The Excel data file and a copy of the output are provided (as Downloadable materials) with this lecture.

Conclusion 1 lectures

Course Conclusion Preview 01:15

The conclusion to the course is presented in this video along with a brief introduction to some of the other courses available by Quantitative Specialists.