Introduction to Statistical Analysis Using IBM SPSS Statistics is a two day instructor-led classroom course that provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing underlying relationships. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results. Further to this, students will get to spend one day with the software for hands-on training.

## Pre-requisites

This basic course is for students with:

- Anyone who has worked with IBM SPSS Statistics and wants to become better versed in the basic statistical capabilities of IBM SPSS Statistics Base
- Anyone with limited or no statistical background
- Anyone who wants to refresh their knowledge and statistical experience that were gained many years ago

## High-level Curriculum

### Course Assessment

Students will be assessed through 3 random exercises selected from the pool of 12 learning activities covering the following topics:

- Introduction to Statistical Analysis
- Understanding Data Distributions – Theory
- Data Distributions for Categorical Variables
- Data Distributions for Scale Variables
- Making Inferences about Populations from Samples
- Relationships between Categorical Variables
- The Independent-Samples T Test
- The Paired-Samples T Test
- One-Way ANOVA
- Bivariate Plots and Correlations for Scale Variables
- Regression Analysis
- Non-Parametric Tests

Assessments will be marked and scores sent to the client after the training.

## Course Contents

### Lesson 1: Course Introduction

- Introduction
- Course Objectives
- About IBM Business Analytics
- Supporting Materials
- Course Assumptions
- References

### Lesson 2: Introduction to Statistical Analysis

- Objectives
- Introduction
- Basic Steps of the Research Process
- Populations and Samples
- Research Design
- Independent and Dependent Variables
- Lesson Summary

### Lesson 3: Understanding Data Distributions – Theory

- Objectives
- Introduction
- Levels of Measurement and Statistical Methods
- Measures of Central Tendency and Dispersion
- Normal Distributions
- Standardized (Z-) Scores
- Requesting Standardized (Z-) Scores
- Standardized (Z-) Scores Output
- Procedure: Descriptives for Standardized (Z-) Scores
- Demonstration: Descriptives for Z-Scores
- Lesson Summary

### Lesson 4: Data Distributions for Categorical Variables

- Objectives
- Introduction
- Using Frequencies to Summarize Nominal and Ordinal Variables
- Requesting Frequencies
- Frequencies Output
- Procedure: Frequencies
- Demonstration: Frequencies
- Lesson Summary

### Lesson 5: Data Distributions for Scale Variables

- Objectives
- Introduction
- Summarizing Scale Variables Using Frequencies
- Requesting Frequencies
- Frequencies Output
- Procedure: Frequencies
- Demonstration: Frequencies
- Summarizing Scale Variables Using Descriptives
- Requesting Descriptives
- Descriptives Output
- Procedure: Descriptives
- Demonstration: Descriptives
- Summarizing Scale Variables Using the Explore Procedure
- Requesting Explore
- Procedure: Explore
- Demonstration: Explore
- Lesson Summary

### Lesson 6: Making Inferences about Populations from Samples

- Objectives
- Introduction
- Basics of Making Inferences About Populations from Samples
- Influence of Sample Size
- Hypothesis Testing
- The Nature of Probability
- Types of Statistical Errors
- Statistical Significance and Practical Importance
- Lesson Summary

### Lesson 7: Relationships between Categorical Variables

- Objectives
- Introduction
- Crosstabs
- Crosstabs Assumptions
- Requesting Crosstabs
- Crosstabs Output
- Procedure: Crosstabs
- Example: Crosstabs
- Chi-Square Test
- Requesting the Chi-Square Test
- Chi-Square Output
- Procedure: Chi-Square Test
- Example: Chi-Square Test
- Clustered Bar Chart
- Requesting Clustered Bar Chart with Chart Builder
- Clustered Bar Chart from Chart Builder Output
- Procedure: Clustered Bar Chart with Chart Builder
- Example: Clustered Bar Chart with Chart Builder
- Adding a Control Variable
- Requesting a Control Variable
- Control Variable Output
- Procedure: Adding a Control Variable
- Example: Adding a Control Variable
- Syntax-Only Crosstabs Features
- Extensions: Beyond Crosstabs
- Association Measures
- Lesson Summary

### Lesson 8: The Independent-Samples T Test

- Objectives
- Introduction
- The Independent-Samples T Test
- Independent-Samples T Test Assumptions
- Requesting the Independent-Samples T Test
- Independent-Samples T Test Output
- Procedure: Independent-Samples T Test
- Demonstration: Independent-Samples T Test
- Error Bar Chart
- Requesting an Error Bar Chart with Chart Builder
- Error Bar Chart Output
- Demonstration: Error Bar Chart with Chart Builder
- Lesson Summary

### Lesson 9: The Paired-Samples T Test

- Objectives
- Introduction
- The Paired-Samples T Test
- Assumptions for the Paired-Samples T Test
- Requesting a Paired-Samples T Test
- Paired-Samples T Test Output
- Procedure: Paired-Samples T Test
- Demonstration: Paired-Samples T Test
- Lesson Summary

### Lesson 10: One-Way ANOVA

- Objectives
- Introduction
- One-Way ANOVA
- Assumptions of One-Way ANOVA
- Requesting One-Way ANOVA
- One-Way ANOVA Output
- Procedure: One-Way ANOVA
- Demonstration: One-Way ANOVA
- Post Hoc Tests with a One-Way ANOVA
- Requesting Post Hoc Tests with a One-Way ANOVA
- Post Hoc Tests Output
- Procedure: Post Hoc Tests with a One-Way ANOVA
- Demonstration: Post Hoc Tests with a One-Way ANOVA
- Error Bar Chart with Chart Builder
- Requesting an Error Bar Chart with Chart Builder
- Error Bar Chart Output
- Procedure: Error Bar Chart with Chart Builder
- Demonstration: Error Bar Chart with Chart Builder
- Lesson Summary

### Lesson 11: Bivariate Plots and Correlations for Scale Variables

- Objectives
- Introduction
- Scatterplots
- Requesting a Scatterplot
- Scatterplot Output
- Procedure: Scatterplot
- Demonstration: Scatterplot
- Adding a Best Fit Straight Line to the Scatterplot
- Pearson Correlation Coefficient
- Requesting a Pearson Correlation Coefficient
- Bivariate Correlations Output
- Procedure: Pearson Correlation with Bivariate Correlations
- Demonstration: Pearson Correlation with Bivariate Correlations
- Lesson Summary

### Lesson 12: Regression Analysis

- Objectives
- Introduction
- Simple Linear Regression
- Simple Linear Regression Assumptions
- Requesting Simple Linear Regression
- Simple Linear Regression Output
- Procedure: Simple Linear Regression
- Demonstration: Simple Linear Regression
- Multiple Regression
- Multiple Linear Regression Assumptions
- Requesting Multiple Linear Regression
- Multiple Linear Regression Output
- Procedure: Multiple Linear Regression
- Demonstration: Multiple Linear Regression
- Automatic Linear Modeling
- Lesson Summary

### Lesson 13: Non-Parametric Tests

- Objectives
- Introduction
- Non Parametric Analyses
- The Independent Samples Nonparametric Analysis
- Requesting an Independent Samples Nonparametric Analysis
- Independent Samples Nonparametric Tests Output
- Procedure: Independent Samples Nonparametric Tests
- Demonstration: Independent Samples Nonparametric Tests
- The Related Samples Nonparametric Analysis
- Requesting a Related Samples Nonparametric Analysis
- Related Samples Nonparametric Tests Output
- Procedure: Related Samples Nonparametric Tests
- Demonstration: Related Samples Nonparametric Tests
- Lesson Summary