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Decision Analytics Microsoft Excel

ISBN-10: 0789751682
ISBN-13: 9780789751683
Edition: 2014
Authors: Conrad Carlberg
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Description: Overwhelmed by all the “Big Data” now available to you? Dumbfounded by all the variables and observations you can make? Not sure what questions to ask? Using proven decision analytics techniques, you can easily distill all that data into manageable  More...

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Book details

List price: $39.99
Copyright year: 2014
Publisher: Pearson Education
Publication date: 11/5/2013
Binding: Paperback
Pages: 288
Size: 7.25" wide x 9.25" long x 0.75" tall
Weight: 1.232
Language: English

Overwhelmed by all the “Big Data” now available to you? Dumbfounded by all the variables and observations you can make? Not sure what questions to ask? Using proven decision analytics techniques, you can easily distill all that data into manageable sets — and you can do it with Microsoft Excel, a tool you already know. In Decision Analytics: Microsoft Excel, best-selling statistics expert and consultant Conrad Carlberg will show you how. Carlberg’s hands-on tutorial guides you through using decision analytics to segment customers (or anything else) into sensible and actionable groups and clusters — and then using those groups to improve everything from pricing to cross-selling, investment decisions to inventory management. Throughout, he provides Excel workbooks containing complete sample data and analyses: workbooks designed so you can easily adapt them for your unique requirements. Carlberg begins by reviewing each component of the decision analysis system, and then guides you step-by-step through:Logistic regressionUnivariate Analysis of Variance (ANOVA)Multivariate Analysis of Variance (MANOVA)Discriminant Function Analysis (basic and advanced)Cluster Analysis (basic and advanced)Through practical and realistic examples, he helps you understand the techniques and assumptions that underlie decision analytics, and use simple Excel charts to intuitively grasp the results. With this foundation in place, you can either do your own analyses in Excel, or begin working with and evaluating results produced by advanced statistical packages such as SAS and SPSS. Note: This book assumes some basic familiarity with regression analysis; if you need to learn this, also read his outstanding companion guide, Predictive Analytics. Microsoft Excel.

What's in the Book
Why Use Excel?
Components of Decision Analytics
Classifying According to Existing Categories
Using a Two-Step Approach
Multiple Regression and Decision Analytics
Access to a Reference Sample
Multivariate Analysis of Variance
Discriminant Function Analysis
Logistic Regression
Classifying According to Naturally Occurring Clusters
Principal Components Analysis
Cluster Analysis
Some Terminology Problems
The Design Sets the Terms
Causation Versus Prediction
Why the Terms Matter
Logistic Regression
The Rationale for Logistic Regression
The Scaling Problem
About Underlying Assumptions
Equal Spread
Equal Variances with Dichotomies
Equal Spread and the Range
The Distribution of the Residuals
Calculating the Residuals
The Residuals of a Dichotomy
Using Logistic Regression
Using Odds Rather Than Probabilities
Using Log Odds
Using Maximum Likelihood Instead of Least Squares
Maximizing the Log Likelihood
Setting Up the Data
Setting Up the Logistic Regression Equation
Getting the Odds
Getting the Probabilities
Calculating the Log Likelihood
Finding and Installing Solver
Running Solver
The Rationale for Log Likelihood
The Probability of a Correct Classification
Using the Log Likelihood
The Statistical Significance of the Log Likelihood
Setting Up the Reduced Model
Setting Up the Full Model
Univariate Analysis of Variance (ANOVA)
The Logic of ANOVA
Using Variance
Partitioning Variance
Expected Values of Variances (Within Groups)
Expected Values of Variances (Between Groups)
The F-Ratio
The Noncentral F Distribution
Single Factor ANOVA
Adopting an Error Rate
Computing the Statistics
Deriving the Standard Error of the Mean
Using the Data Analysis Add-In
Installing the Data Analysis Add-In
Using the ANOVA: Single Factor Tool
Understanding the ANOVA Output
Using the Descriptive Statistics
Using the Inferential Statistics
The Regression Approach
Using Effect Coding
The LINEST() Formula
The LINEST() Results
LINEST() Inferential Statistics
Multivariate Analysis of Variance (MANOVA)
The Rationale for MANOVA
Correlated Variables
Correlated Variables in ANOVA
Visualizing Multivariate ANOVA
Univariate ANOVA Results
Multivariate ANOVA Results
Means and Centroids
Using SSCP Instead of SS
Getting the Among and the Within SSCP Matrices
Sums of Squares and SSCP Matrices
Getting to a Multivariate F-Ratio
Wilks' Lambda and the F-Ratio
Converting Wilks' Lambda to an F Value
Running a MANOVA in Excel
Laying Out the Data
Running the MANOVA Code
Descriptive Statistics
Equality of the Dispersion Matrices
The Univariate and Multivariate F-Tests
After the Multivariate Test
Discriminant Function Analysis: The Basics
Treating a Category as a Number
The Rationale for Discriminant Analysis
Multiple Regression and Discriminant Analysis
Adjusting Your Viewpoint
Discriminant Analysis and Multiple Regression
Regression, Discriminant Analysis, and Canonical Correlation
Coding and Multiple Regression
The Discriminant Function and the Regression Equation
From Discriminant Weights to Regression Coefficients
Eigenstructures from Regression and Discriminant Analysis
Structure Coefficients Can Mislead
Wrapping It Up
Discriminant Function Analysis: Further Issues
Using the Discriminant Workbook
Opening the Discriminant Workbook
Using the Discriminant Dialog Box
Why Run a Discriminant Analysis on Irises?
Evaluating the Original Measures
Discriminant Analysis and Investment
Benchmarking with R
Downloading R
Arranging the Data File
Running the Analysis
The Results of the Discrim Add-In
The Discriminant Results
Interpreting the Structure Coefficients
Eigenstructures and Coefficients
Other Uses for the Coefficients
Classifying the Cases
Distance from the Centroids
Correcting for the Means
Adjusting for the Variance-Covariance Matrix
Assigning a Classification
Creating the Classification Table
Training Samples: The Classification Is Known Beforehand
Principal Components Analysis
Establishing a Conceptual Framework for Principal Components Analysis
Principal Components and Tests
PCA's Ground Rules
Correlation and Oblique Factor Rotation
Using the Principal Components Add-In
The Correlation Matrix
The Inverse of the R Matrix
The Sphericity Test
Counting Eigenvalues, Calculating Coefficients and Understanding Communalities
How Many Components?
Factor Score Coefficients
Relationships Between the Individual Results
Using the Eigenvalues and Eigenvectors
Eigenvalues, Eigenvectors, and Loadings
Eigenvalues, Eigenvectors, and Factor Coefficients
Getting the Eigenvalues Directly from the Factor Scores
Getting the Eigenvalues and Eigenvectors
Iteration and Exhaustion
Rotating Factors to a Meaningful Solution
Identifying the Factors
The Varimax Rotation
Classification Examples
State Crime Rates
Physical Measurements of Aphids
Cluster Analysis: The Basics
Cluster Analysis, Discriminant Analysis, and Logistic Regression
Euclidean Distance
Mahalanobis' D<sup>2</sup> and Cluster Analysis
Finding Clusters: The Single Linkage Method
The Self-Selecting Nature of Cluster Analysis
Finding Clusters: The Complete Linkage Method
Complete Linkage: An Example
Other Linkage Methods
Finding Clusters: The K-means Method
Characteristics of K-means Analysis
A K-means Example
Benchmarking K-means with R
Cluster Analysis: Further Issues
Using the K-means Workbook
Deciding on the Number of Clusters
The Cluster Members Worksheet
The Cluster Centroids Worksheet
The Cluster Variances Worksheet
The F-Ratios Worksheet
Reporting Process Statistics
Cluster Analysis Using Principal Components
Principal Components Revisited
Clustering Wines
Cross-Validating the Results

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