Many of the statistical techniques under the Analyze menu in SPSS offer graphs as part of the optional output that you can request. In Chapter 3, Statistics for Individual Data Elements, a boxplot was requested as part of the Explore procedure output. Among the Descriptive Statistics procedures, Frequencies, Descriptives, Crosstabs, and Explore all have optional graphs available. As you saw in Chapter 4, Dealing with Outliers and Missing Data, there are also specialty graphs (P-P and Q-Q plots) available. Many of the statistical procedures, such as ANOVA and regression, also include graph options. They will be explored in the chapters that cover these topics.
Data Analysis with IBM SPSS Statistics
By :
Data Analysis with IBM SPSS Statistics
By:
Overview of this book
SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data.
The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed.
By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease.
Table of Contents (17 chapters)
Preface
Free Chapter
Installing and Configuring SPSS
Accessing and Organizing Data
Statistics for Individual Data Elements
Dealing with Missing Data and Outliers
Visually Exploring the Data
Sampling, Subsetting, and Weighting
Creating New Data Elements
Adding and Matching Files
Aggregating and Restructuring Data
Crosstabulation Patterns for Categorical Data
Comparing Means and ANOVA
Correlations
Linear Regression
Principal Components and Factor Analysis
Clustering
Discriminant Analysis
Customer Reviews