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Logistic Regression Using SPSS
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.
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