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Regression Analysis Using SPSS

Welcome to Regression Analysis Using IBM SPSS Statistics, a comprehensive course designed to help you master one of the most important statistical techniques used for prediction, e...

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Published 2026 · Last updated Aug 2026

Regression Analysis Using SPSS

Course instructors · lifetime access
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Course overview

Course Overview

Welcome to Regression Analysis Using IBM SPSS Statistics, a comprehensive course designed to help you master one of the most important statistical techniques used for prediction, explanation, and hypothesis testing. Whether you are a student, researcher, academic, or data analyst, this course provides the theoretical foundation and practical skills required to perform and interpret regression analyses using IBM SPSS Statistics.


The course begins with the fundamentals of regression analysis, explaining its purpose, assumptions, and applications across various research disciplines. You will learn how to conduct Simple Linear Regression, Multiple Linear Regression, and Hierarchical Regression Analysis using SPSS. The course covers data preparation, testing model assumptions, interpreting regression coefficients, evaluating model fit, understanding R-squared and adjusted R-squared, assessing multicollinearity, and reporting results according to APA style. Through step-by-step demonstrations and practical examples, you will gain hands-on experience in building, interpreting, and validating regression models for real-world research and data analysis.


By the end of this course, you will have the confidence to perform regression analysis using IBM SPSS Statistics and apply these techniques effectively in academic research, dissertations, theses, business analytics, social sciences, healthcare, and professional data analysis.

Learning Outcomes

Understand the concepts, assumptions, and applications of regression analysis.

Perform and interpret Simple Linear Regression using IBM SPSS Statistics.

Conduct and evaluate Multiple Linear Regression models for prediction and hypothesis testing.

Apply Hierarchical Regression Analysis to assess the contribution of predictor variables.

Report regression analysis results in APA style and research-standard format.

What you walk away with

Career Map & Skills You Gain

Every course adds to three macro skills. Log in to swap platform averages for your own numbers.

Financial skills

62th

percentile

Your salary vs. learners in these roles

After this course+14 pts → 76th

Data Analysis and Research Specialist

78% course score

+8

Project Contributor

66% course score

+7

Independent Practitioner

61% course score

+6

Employability skills

71th

percentile

Quiz scores from the courses you finish

After this course+18 pts → 89th

Understand the concepts, assumpt...

64% course score

+6 pts

Perform and interpret Simple Lin...

70% course score

+7 pts

Conduct and evaluate Multiple Li...

76% course score

+8 pts

Apply Hierarchical Regression An...

82% course score

+8 pts

Life skills

48th

percentile

Life-skill test scores after each course

After this course+11 pts → 59th

Critical thinking

76% course score

+8 pts

Decision-making

71% course score

+7 pts

Analytical mindset

68% course score

+7 pts

Curriculum

4 modules · 21 lessons · 1 hr 31 min

Each module pairs the hand calculation with the same analysis in Excel, SPSS, R and Python.

What is Regression?
1:01
When to Use Linear Regression Vs. Multiple Regression?
1:34
Defining SPSS Input Options for Linear Regression
1:55
Interpreting Linear Regression Output Variables & Model Summary
4:55
Interpreting Linear Regression Output Constant, B, Beta, SE & t
5:22
What is Multiple Regression?
7:26
Testing Assumptions: Linearity & its Test in SPSS
3:55
Testing Assumptions: Independence of Errors & Lack of Autocorrelations
2:18
Testing Assumptions: Homoscedasticity of Errors
1:35
Testing Assumption: Multivariate Normality
0:40
Testing Assumptions: Multicollinearity
2:55
Enter Method
3:13
Stepwise Regression
5:22
Backward Elimination Method
4:20
Forward Selection Method
5:57
Remove Method
3:48
What is Hierarchical Regression Analysis and When to Use It?
1:48
Setting Data and Defining Model in Hierarchical Regression
4:48
Refining Model and Detecting Multicollinearity through Correlation Matrix
9:40
Taming Bad Data Using beta, R squared and p values
3:27
Interpreting the Output of Hierarchical Regression
14:20
₹850.00 Enroll Now

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Questions

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Who is this course for?

This course is designed for learners who want a practical, comprehensive path through the course subject.

What will I learn?

You will build practical knowledge through the course lessons, examples, and activities.

How long do I have access?

Your course access remains available for the lifetime of your account, including future updates.

Do I get a certificate?

Yes. A verifiable certificate of completion is issued when you finish the course requirements.

Is there a refund policy?

Yes. There is a 30-day money-back guarantee, no questions asked.

We are committed to your progress

Lifetime access, mentor Q&A on every lesson, and a certificate you can share the moment you finish. If it is not right for you, the first 30 days are on us.

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