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Data Analysis with IBM SPSS Statistics
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Summary
Thisbookisdesignedforanalystsandresearcherswhoneedtoworkwithdatatodiscovermeaningfulpatternsbutdonothavethetime(orinclination)tobecomeprogrammers.Weassumeafoundationalunderstandingofstatisticssuchasonewouldlearninabasiccourseortwoonstatisticaltechniquesandmethods.
目錄(228章)
倒序
- cover
- Title Page
- Copyright
- Data Analysis with IBM SPSS Statistics
- Credits
- About the Authors
- Acknowledgement
- About the Reviewers
- www.PacktPub.com
- Why subscribe?
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Errata
- Piracy
- Questions
- Installing and Configuring SPSS
- The SPSS installation utility
- Installing Python for the scripting
- Licensing SPSS
- Confirming the options available
- Launching and using SPSS
- Setting parameters within the SPSS software
- Executing a basic SPSS session
- Summary
- Accessing and Organizing Data
- Accessing and organizing data overview
- Reading Excel files
- Reading delimited text data files
- Saving IBM SPSS Statistics files
- Reading IBM SPSS Statistics files
- Demo - first look at the data - frequencies
- Variable properties
- Variable properties - name
- Variable properties - type
- Variable properties - width
- Variable properties - decimals
- Variable properties - label
- Variable properties - values
- Variable properties - missing
- Variable properties - columns
- Variable properties - align
- Variable properties - measure
- Variable properties - role
- Demo - adding variable properties to the Variable View
- Demo - adding variable properties via syntax
- Demo - defining variable properties
- Summary
- Statistics for Individual Data Elements
- Getting the sample data
- Descriptive statistics for numeric fields
- Controlling the descriptives display order
- Frequency distributions
- Discovering coding issues using frequencies
- Using frequencies to verify missing data patterns
- Explore procedure
- Stem and leaf plot
- Boxplot
- Using explore to check subgroup patterns
- Summary
- Dealing with Missing Data and Outliers
- Outliers
- Frequencies for histogram and percentile values
- Descriptives for standardized scores
- The Examine procedure for extreme values and boxplot
- Detecting multivariate outliers
- Missing data
- Missing values in Frequencies
- Missing values in Descriptives
- Missing value patterns
- Replacing missing values
- Summary
- Visually Exploring the Data
- Graphs available in SPSS procedures
- Obtaining bar charts with frequencies
- Obtaining a histogram with frequencies
- Creating graphs using chart builder
- Building a scatterplot
- Create a boxplot using chart builder
- Summary
- Sampling Subsetting and Weighting
- Select cases dialog box
- Select cases - If condition is satisfied
- Example
- If condition is satisfied combined with Filter
- If condition is satisfied combined with Copy
- If condition is satisfied combined with Delete unselected cases
- The Temporary command
- Select cases based on time or case range
- Using the filter variable
- Selecting a random sample of cases
- Split File
- Weighting
- Summary
- Creating New Data Elements
- Transforming fields in SPSS
- The RECODE command
- Creating a dummy variable using RECODE
- Using RECODE to rescale a field
- Respondent's income using the midpoint of a selected category
- The COMPUTE command
- The IF command
- The DO IF/ELSE IF command
- General points regarding SPSS transformation commands
- Summary
- Adding and Matching Files
- SPSS Statistics commands to merge files
- Example of one-to-many merge - Northwind database
- Customer table
- Orders table
- The Customer-Orders relationship
- SPSS code for a one-to-many merge
- Alternate SPSS code
- One-to-one merge - two data subsets from GSS2016
- Example of combining cases using ADD FILES
- Summary
- Aggregating and Restructuring Data
- Using aggregation to add fields to a file
- Using aggregated variables to create new fields
- Aggregating up one level
- Preparing the data for aggregation
- Second level aggregation
- Preparing aggregated data for further use
- Matching the aggregated file back to find specific records
- Restructuring rows to columns
- Patient test data example
- Performing calculations following data restructuring
- Summary
- Crosstabulation Patterns for Categorical Data
- Percentages in crosstabs
- Testing differences in column proportions
- Crosstab pivot table editing
- Adding a layer variable
- Adding a second layer
- Using a Chi-square test with crosstabs
- Expected counts
- Context sensitive help
- Ordinal measures of association
- Interval with nominal association measure
- Nominal measures of association
- Summary
- Comparing Means and ANOVA
- SPSS procedures for comparing Means
- The Means procedure
- Adding a second variable
- Test of linearity example
- Testing the strength of the nonlinear relationship
- Single sample t-test
- The independent samples t-test
- Homogeneity of variance test
- Comparing subsets
- Paired t-test
- Paired t-test split by gender
- One-way analysis of variance
- Brown-Forsythe and Welch statistics
- Planned comparisons
- Post hoc comparisons
- The ANOVA procedure
- Summary
- Correlations
- Pearson correlations
- Testing for significance
- Mean differences versus correlations
- Listwise versus pairwise missing values
- Comparing pairwise and listwise correlation matrices
- Pivoting table editing to enhance correlation matrices
- Creating a very trimmed matrix
- Visualizing correlations with scatterplots
- Rank order correlations
- Partial correlations
- Adding a second control variable
- Summary
- Linear Regression
- Assumptions of the classical linear regression model
- Example - motor trend car data
- Exploring associations between the target and predictors
- Fitting and interpreting a simple regression model
- Residual analysis for the simple regression model
- Saving and interpreting casewise diagnostics
- Multiple regression - Model-building strategies
- Summary
- Principal Components and Factor Analysis
- Choosing between principal components analysis and factor analysis
- PCA example - violent crimes
- Simple descriptive analysis
- SPSS code - principal components analysis
- Assessing factorability of the data
- Principal components analysis of the crime variables
- Principal component analysis – two-component solution
- Factor analysis - abilities
- The reduced correlation matrix and its eigenvalues
- Factor analysis code
- Factor analysis results
- Summary
- Clustering
- Overview of cluster analysis
- Overview of SPSS Statistics cluster analysis procedures
- Hierarchical cluster analysis example
- Descriptive analysis
- Cluster analysis - first attempt
- Cluster analysis with four clusters
- K-means cluster analysis example
- Descriptive analysis
- K-means cluster analysis of the Old Faithful data
- Further cluster profiling
- Other analyses to try
- Twostep cluster analysis example
- Summary
- Discriminant Analysis
- Descriptive discriminant analysis
- Predictive discriminant analysis
- Assumptions underlying discriminant analysis
- Example data
- Statistical and graphical summary of the data
- Discriminant analysis setup - key decisions
- Priors
- Pooled or separate
- Dimensionality
- Syntax for the wine example
- Examining the results
- Scoring new observations
- Summary 更新時間：2021-07-02 18:14:26
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