Enhanced descriptions from Syndetics:

Excel predictive analytics for serious data crunchers!

The movie Moneyball made predictive analytics famous: Now you can apply the same techniques to help your business win. You don't need multimillion-dollar software: All the tools you need are available in Microsoft Excel, and all the knowledge and skills are right here, in this book!

Microsoft Excel MVP Conrad Carlberg shows you how to use Excel predictive analytics to solve real-world problems in areas ranging from sales and marketing to operations. Carlberg offers unprecedented insight into building powerful, credible, and reliable forecasts, showing how to gain deep insights from Excel that would be difficult to uncover with costly tools such as SAS or SPSS.

You'll get an extensive collection of downloadable Excel workbooks you can easily adapt to your own unique requirements, plus VBA code--much of it open-source--to streamline several of this book's most complex techniques.

Step by step, you'll build on Excel skills you already have, learning advanced techniques that can help you increase revenue, reduce costs, and improve productivity. By mastering predictive analytics, you'll gain a powerful competitive advantage for your company and yourself.

* Learn both the "how" and "why" of using data to make better tactical decisions

* Choose the right analytics technique for each problem

* Use Excel to capture live real-time data from diverse sources, including third-party websites

* Use logistic regression to predict behaviors such as "will buy" versus "won't buy"

* Distinguish random data bounces from real, fundamental changes

* Forecast time series with smoothing and regression

* Construct more accurate predictions by using Solver to find maximum likelihood estimates

* Manage huge numbers of variables and enormous datasets with principal components analysis and Varimax factor rotation

* Apply ARIMA (Box-Jenkins) techniques to build better forecasts and understand their meaning

Table of contents provided by Syndetics

• Introduction (p. 1)
• 1 Building a Collector (p. 7)
• Planning an Approach (p. 8)
• A Meaningful Variable (p. 8)
• Identifying Sales (p. 8)
• Planning the Workbook Structure (p. 9)
• Query Sheets (p. 9)
• Summary Sheets (p. 13)
• Snapshot Formulas (p. 15)
• More Complicated Breakdowns (p. 16)
• The VBA Code (p. 18)
• The DoltAgain Subroutine (p. 19)
• The GetNewData Subroutine (p. 20)
• The GetRank Function (p. 24)
• The GetUnitsLeft Function (p. 26)
• The RefreshSheets Subroutine (p. 27)
• The Analysis Sheets (p. 28)
• Defining a Dynamic Range Name (p. 29)
• Using the Dynamic Range Name (p. 30)
• 2 Linear Regression (p. 35)
• Correlation and Regression (p. 35)
• Charting the Relationship (p. 36)
• Calculating Pearson's Correlation Coefficient (p. 38)
• Correlation Is Not Causation (p. 41)
• Simple Regression (p. 42)
• Array-Entering Formulas (p. 44)
• Array-Entering LINEST() (p. 44)
• Multiple Regression (p. 45)
• Creating the Composite Variable (p. 45)
• Analyzing the Composite Variable (p. 48)
• Assumptions Made in Regression Analysis (p. 50)
• Variability (p. 50)
• Using Excel's Regression Tool (p. 54)
• Accessing the Data Analysis Add-In (p. 54)
• Running the Regression Tool (p. 56)
• 3 Forecasting with Moving Averages (p. 65)
• About Moving Averages (p. 65)
• Signal and Noise (p. 66)
• Smoothing Versus Tracking (p. 68)
• Weighted and Unweighted Moving Averages (p. 70)
• Criteria for Judging Moving Averages (p. 73)
• Mean Absolute Deviation (p. 73)
• Least Squares (p. 74)
• Using Least Squares to Compare Moving Averages (p. 74)
• Getting Moving Averages Automatically (p. 76)
• Using the Moving Average Tool (p. 76)
• 4 Forecasting a Time Series: Smoothing (p. 83)
• Exponential Smoothing: The Basic Idea (p. 84)
• Why "Exponential" Smoothing? (p. 86)
• Using Excel's Exponential Smoothing Tool (p. 89)
• Understanding the Exponential Smoothing Dialog Box (p. 90)
• Choosing the Smoothing Constant (p. 96)
• Setting Up the Analysis (p. 97)
• Using Solver to Find the Best Smoothing Constant (p. 99)
• Understanding Solver's Requirements (p. 104)
• The Point (p. 107)
• Handling Linear Baselines with Trend (p. 108)
• Characteristics of Trend (p. 108)
• First Differencing (p. 111)
• Holt's Linear Exponential Smoothing (p. 115)
• About Terminology and Symbols in Handling Trended Series (p. 115)
• Using Holt Linear Smoothing (p. 116)
• 5 Forecasting a Time Series: Regression (p. 123)
• Forecasting with Regression (p. 123)
• Linear Regression: An Example (p. 125)
• Using the LINEST() Function (p. 128)
• Forecasting with Autoregression (p. 133)
• Problems with Trends (p. 134)
• Correlating at Increasing Lags (p. 134)
• A Review: Linear Regression and Autoregression (p. 137)
• Adjusting the Autocorrelation Formula (p. 139)
• Using ACFs (p. 140)
• Understanding PACFs (p. 142)
• Using the ARIMA Workbook (p. 147)
• 6 Logistic Regression: The Basics (p. 149)
• Traditional Approaches to the Analysis (p. 149)
• Z-tests and the Central Limit Theorem (p. 149)
• Using Chi-Square (p. 153)
• Preferring Chi-square to a Z-test (p. 155)
• Regression Analysis on Dichotomies (p. 158)
• Homoscedasticity (p. 158)
• Residuals Are Normally Distributed (p. 161)
• Restriction of Predicted Range (p. 161)
• Ah, But You Can Get Odds Forever (p. 162)
• Probabilities and Odds (p. 163)
• How the Probabilities Shift (p. 164)
• Moving On to the Log Odds (p. 166)
• 7 Logistic Regression: Further Issues (p. 169)
• An Example: Predicting Purchase Behavior (p. 170)
• Using Logistic Regression (p. 171)
• Calculation of Logit or Log Odds (p. 179)
• Comparing Excel with R: A Demonstration (p. 193)
• Getting R (p. 193)
• Running a Logistic Analysis in R (p. 194)
• The Purchase Data Set (p. 195)
• Statistical Tests in Logistic Regression (p. 198)
• Models Comparison in Multiple Regression (p. 198)
• Calculating the Results of Different Models (p. 199)
• Testing the Difference Between the Models (p. 200)
• Models Comparison in Logistic Regression (p. 201)
• 8 Principal Components Analysis (p. 211)
• The Notion of a Principal Component (p. 211)
• Reducing Complexity (p. 212)
• Understanding Relationships Among Measurable Variables (p. 213)
• Maximizing Variance (p. 214)
• Components Are Mutually Orthogonal (p. 215)
• Using the Principal Components Add-In (p. 216)
• The R Matrix (p. 219)
• The Inverse of the R Matrix (p. 220)
• Matrices, Matrix Inverses, and Identity Matrices (p. 222)
• Features of the Correlation Matrix's Inverse (p. 223)
• Matrix Inverses and Beta Coefficients (p. 225)
• Singular Matrices (p. 227)
• Testing for Uncorrelated Variables (p. 228)
• Using Eigenvalues (p. 229)
• Using Component Eigenvectors (p. 231)
• Factor Loadings (p. 233)
• Factor Score Coefficients (p. 233)
• Principal Components Distinguished from Factor Analysis (p. 236)
• Distinguishing the Purposes (p. 236)
• Distinguishing Unique from Shared Variance (p. 237)
• Rotating Axes (p. 238)
• 9 Box-Jenkins ARIMA Models (p. 241)
• The Rationale for ARIMA (p. 241)
• Deciding to Use ARIMA (p. 242)
• ARIMA Notation (p. 242)
• Stages in ARIMA Analysis (p. 244)
• The Identification Stage (p. 244)
• Identifying an AR Process (p. 244)
• Identifying an MA Process (p. 248)
• Differencing in ARIMA Analysis (p. 249)
• Using the ARIMA Workbook (p. 252)
• Standard Errors in Correlograms (p. 253)
• White Noise and Diagnostic Checking (p. 254)
• Identifying Seasonal Models (p. 255)
• The Estimation Stage (p. 257)
• Estimating the Parameters for ARIMA(1,0,0) (p. 257)
• Comparing Excel's Results to R's (p. 259)
• Exponential Smoothing and ARIMA(0,0,1) (p. 261)
• Using ARIMA(0,1,1) in Place of ARIMA(0,0,1) (p. 263)
• The Diagnostic and Forecasting Stages (p. 264)
• 10 Varimax Factor Rotation in Excel (p. 267)
• Getting to a Simple Structure (p. 267)
• Rotating Factors: The Rationale (p. 268)
• Extraction and Rotation: An Example (p. 271)
• Showing Text Labels Next to Chart Markers (p. 275)
• Structure of Principal Components and Factors (p. 276)
• Rotating Factors: The Results (p. 277)
• Charting Records on Rotated Factors (p. 279)
• Using the Factor Workbook to Rotate Components (p. 281)
• Index (p. 283)