Chapter 1: Getting Started with SPSS
* This chapter provides an overview of SPSS and its capabilities.
* It explains how to open and create a new SPSS data file.
* Real example: Import a CSV file containing customer data, including demographics and purchase history.
Chapter 2: Exploring Your Data
* Chapter 2 introduces the data view and variable view windows in SPSS.
* It covers basic data analysis techniques such as descriptive statistics and frequency tables.
* Real example: Generate descriptive statistics (mean, median, standard deviation) for customer age and income.
Chapter 3: Cleaning and Transforming Data
* This chapter discusses data cleaning and transformation techniques.
* It covers topics such as missing data, outlier detection, and recoding variables.
* Real example: Remove duplicate records, impute missing values for income, and recode customer gender into binary categories.
Chapter 4: Creating and Modifying Graphs
* Chapter 4 provides a step-by-step guide to creating various types of graphs in SPSS.
* It covers bar charts, histograms, scatterplots, and more.
* Real example: Create a scatterplot showing the relationship between customer income and purchase amount.
Chapter 5: Hypothesis Testing and Significance
* This chapter introduces hypothesis testing and explains the concept of statistical significance.
* It covers t-tests, chi-square tests, and ANOVA.
* Real example: Perform a t-test to determine if there is a significant difference in purchase amount between male and female customers.
Chapter 6: Regression Analysis
* Chapter 6 covers regression analysis, a technique used to predict a dependent variable based on one or more independent variables.
* It explains the assumptions of regression and how to interpret the results.
* Real example: Build a regression model to predict customer purchase amount using income and age as predictors.
Chapter 7: Factor Analysis
* This chapter introduces factor analysis, a technique used to reduce the number of variables in a dataset and identify underlying patterns.
* It explains how to perform factor analysis and interpret the results.
* Real example: Perform factor analysis on customer demographics to identify groups of customers with similar characteristics.
Chapter 8: Advanced Statistical Techniques
* Chapter 8 covers advanced statistical techniques such as discriminant analysis, cluster analysis, and logistic regression.
* These techniques are used for more complex data analysis tasks.
* Real example: Use discriminant analysis to classify customers into different market segments based on their demographics and purchase behavior.
Chapter 9: Output Management and Presentation
* This chapter focuses on managing and presenting SPSS output.
* It covers topics such as exporting results, creating reports, and presenting findings effectively.
* Real example: Export regression analysis results to a spreadsheet and create a presentation summarizing the findings.
