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Data Analysis and Research

Logistic Regression Using SPSS

0 students
Last updated Jul 2026

Course Overview

About This Course

Welcome to Logistic Regression Analysis Using IBM SPSS Statistics, a comprehensive course designed to help you understand and apply Logistic Regression, one of the most widely used predictive modeling techniques in statistics, research, and data science. Whether you are a student, researcher, academic, or data analyst, this course provides the theoretical foundation and practical skills required to perform logistic regression with confidence using IBM SPSS Statistics.


The course begins with the fundamentals of logistic regression, explaining its purpose, assumptions, and applications when the dependent variable is categorical. You will learn how to prepare data, build logistic regression models in SPSS, interpret model coefficients, odds ratios, classification tables, goodness-of-fit statistics, and model diagnostics. Through step-by-step demonstrations and practical examples, you will gain hands-on experience in testing research hypotheses, making predictions, and reporting findings according to APA style.


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

Learning Outcomes

Understand the concepts, assumptions, and applications of Logistic Regression Analysis.

Perform Logistic Regression using IBM SPSS Statistics with confidence.

Interpret logistic regression outputs, odds ratios, and model fit statistics accurately.

Develop and test research hypotheses using logistic regression models.

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

Curriculum

6 Sections • 47 Lectures
What is Logistic Regression?
0:34
Understanding the Logistic Regression Model
5:38
Understanding and Logistic Regression Model: Shape, Logit and Probabilities
7:56
Understanding the Equation of Logistic Regression
3:39
Concept of Odd Ratios
1:22
Fundamentals, Derivation & Calculation of Odd ratios
18:55
Example: Calculating Odds of Lung Cancer with Smoking
8:25
Dataset and Resources
0:19
Journal Articles
Requirements for Logistic Regression Analysis
0:43
Assumptions of Logistic Regression
5:33
Setting Data and Understanding the Data File
4:21
Dataset
9:00
How to Code the Binary Dependent Variable?
4:25
Understanding Block Option and Interaction Option
3:11
Selecting Method and Coding Categorical Variable as Dummy Variable
6:33
Understanding Save Option: Predicted Probabilities & Group Membership
3:10
Understanding Save Option: Influence - Cook's Distance & DFBeta Options
3:42
Understanding Save: Residuals – Standardized
1:48
Understanding Classification Plots Option
1:27
Understanding Hosmer-Lemeshow Goodness of Fit Test
1:49
Understanding Case-wise Listing of Residuals
1:23
Understanding Correlation of Estimates Option
0:38
Understanding Iteration History Option
0:40
Understanding CI for Exp(B) Option
1:42
Including Constant in Model
1:06
Understanding Classification Cutoff .5 & Bootstrapping
4:47
Understanding Case Processing Summary & Dummy Variable Coding
3:34
Understanding Block 0 vs Other Blocks & Iteration History
2:14
Understanding -2 Log Likelihood & R squares (Cox & Snell, Negelkerke)
6:15
Understanding Classification Table (Sensitivity & Specificity)
4:22
Understanding Variables in Equation - Baseline Model Interpretation
1:18
Understanding Hosmer-Lemeshow & Contingency Table for Baseline Model
1:11
Interpretation of Hosmer-Lemeshow Test for Default Model
1:34
Interpreting Variables in Equation for Default Model
2:40
Interpreting Wald's Test for Default Model
5:28
Interpreting Odd Ratios for Variables in Equation Table
3:08
Interpreting Correlation Table and Understanding Multi-collinearity
3:10
Interpretation and Application of Classification Plot
7:45
Interpreting Case-wise Listing of Residuals Output
3:10
Interpreting Predicted Probabilities and Group Membership
3:52
Interpreting Cook's Distance and DFBeta
4:18
Interpreting Omnibus Test Output
4:04
Explaining Pseudo R Squares - 2Log Likelihood, Cox & Snell and Negelkerke
6:05
Writing Final Equation of Logistic Regression Manually
2:39
APA Style Presentation of Table and Results
17:35
Logistic Regression APA Style Output
1:00
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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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