Book Image

Data Analysis with IBM SPSS Statistics

By : Ken Stehlik-Barry, Anthony Babinec
Book Image

Data Analysis with IBM SPSS Statistics

By: Ken Stehlik-Barry, Anthony Babinec

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)
4
Dealing with Missing Data and Outliers
10
Crosstabulation Patterns for Categorical Data

Linear Regression

Multiple regression is an important statistical procedure for estimating the relationship between a numeric target and one or more numeric predictors. It is used for prediction--to predict the response variable based on the predictor variable's values -- or for explanation--learning the relationship between the response variable and the predictor variables. Since its results include an equation with coefficients, multiple regression produces a transparent model that lends itself to interpretation and also makes it easy to predict new cases.

Through the use of various types of coding and transformation, multiple regression is actually very general in its applicability. For this reason, multiple regression is popular in areas such as the physical and social sciences, policy studies, and classic business applications. Regression has been so successful and popular...