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Data Analysis and Research

Regression Analysis Using SPSS

0 students
Last updated Jul 2026

Course Overview

About This Course

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.

Curriculum

4 Sections • 21 Lectures
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
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An experienced and passionate instructor, [Name] is dedicated to fostering a dynamic and engaging learning environment where students feel motivated to grow both academically and personally. With a strong foundation in their subject area, they bring clarity, structure, and enthusiasm to every lesson, ensuring complex concepts are made accessible and relevant. Their teaching approach blends theoretical knowledge with practical application, encouraging students to think critically and develop problem-solving skills. Known for their supportive and approachable demeanor, [Name] builds meaningful connections with students, understanding that each learner has unique strengths and challenges. They are committed to creating an inclusive classroom atmosphere where curiosity is encouraged, questions are welcomed, and every student feels valued. By adapting teaching methods to suit diverse learning styles, they help students gain confidence and achieve their full potential. Beyond the classroom, [Name] is deeply invested in continuous learning and professional development, staying updated with the latest educational practices and advancements in their field. They often contribute to curriculum development, mentorship programs, and extracurricular activities, reinforcing their role as not just an instructor, but a mentor and guide. Their ultimate goal is to inspire lifelong learning and empower students to succeed in an ever-evolving world.

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