Statistical Analysis and Modeling with SPSS

Welcome to the course "Statistical Analysis and Modeling with SPSS." In this comprehensive program, you will embark on a journey to master statistical analysis techniques and predictive modeling using two powerful tools: SPSS (Statistical Package for the Social Sciences). Whether you're a beginner or an experienced data analyst, this course will equip you with the knowledge and skills needed to conduct robust statistical analyses, build predictive models, and derive meaningful insights from your data.

Throughout this course, you will learn how to import, clean, and explore datasets, perform correlation analyses, conduct linear and multiple regression modeling, delve into logistic regression for binary outcomes, and explore multinomial regression for categorical outcomes. Hands-on exercises and real-world examples will reinforce your understanding and enable you to apply these techniques to diverse datasets.

By the end of this course, you will have a deep understanding of statistical analysis concepts, proficiency in using SPSS for data analysis, and the ability to leverage statistical models to make informed decisions in various domains. Whether you're in academia, business, or research, the skills acquired in this course will empower you to extract valuable insights from data and drive meaningful outcomes.

Section 1: Importing Dataset

This section initiates with the fundamental task of importing datasets in various formats such as text, CSV, xlsx, and xls. It provides insights into user operating concepts, software menus, and statistical measures like mean and standard deviation. Practical implementation using SPSS further solidifies understanding.

Section 2: Correlation Techniques

Here, learners delve into correlation theory, exploring concepts through implementations and practical demonstrations. Various correlation techniques are elucidated, including basic correlation theory, data editor functions, and statistical analysis through scatter plots. Through examples, learners gain proficiency in interpreting and implementing correlation analyses on different datasets.

Section 3: Linear Regression Modeling

Linear regression, a cornerstone of statistical analysis, is comprehensively covered in this section. From an introduction to linear regression modeling to practical examples involving stock returns, copper expansion, and energy consumption, learners understand the intricacies of regression analysis. Through hands-on exercises, they interpret regression equations and analyze real-world datasets.

Section 4: Multiple Regression Modeling

Building upon linear regression, this section delves into multiple regression modeling. Learners explore essential output variables, conduct multiple regression examples, and interpret results. With detailed examples spanning multiple scenarios, learners grasp the nuances of multiple regression analysis and its application in predictive modeling.

Section 5: Logistic Regression

Logistic regression, a vital tool in predictive analytics, is thoroughly explored in this section. Learners understand logistic regression concepts, work with SPSS Statistics Data Editor, and implement logistic regression using MS Excel. Through case studies like smoke preferences and heart pulse studies, learners interpret logistic regression outputs and derive meaningful insights.

Section 6: Multinomial Regression

This section introduces learners to multinomial-polynomial regression, a powerful statistical technique. Through examples like health studies of marathoners, learners explore case processing summaries, model fitting information, and parameter estimates. They interpret outputs, understand correlations, and draw insights crucial for decision-making in various domains.