Hjem

Introduction
Chapter 1 Building a Collector Planning an Approach A Meaningful Variable Identifying Sales Planning the Workbook Structure Query Sheets Summary Sheets Snapshot Formulas More Complicated Breakdowns The VBA Code The DoItAgain Subroutine The GetNewData Subroutine The GetRank Function The GetUnitsLeft Function The RefreshSheets Subroutine The Analysis Sheets Defining a Dynamic Range Name Using the Dynamic Range Name
Chapter 2 Linear Regression Correlation and Regression Charting the Relationship Calculating Pearson''s Correlation Coefficient Correlation Is Not Causation Simple Regression Array-Entering Formulas Array-Entering LINEST() Multiple Regression Creating the Composite Variable Analyzing the Composite Variable Assumptions Made in Regression Analysis Variability Using Excel''s Regression Tool Accessing the Data Analysis Add-In Running the Regression Tool
Chapter 3 Forecasting with Moving Averages About Moving Averages Signal and Noise Smoothing Versus Tracking Weighted and Unweighted Moving Averages Criteria for Judging Moving Averages Mean Absolute Deviation Least Squares Using Least Squares to Compare Moving Averages Getting Moving Averages Automatically Using the Moving Average Tool
Chapter 4 Forecasting a Time Series: Smoothing Exponential Smoothing: The Basic Idea Why "Exponential" Smoothing? Using Excel''s Exponential Smoothing Tool Understanding the Exponential Smoothing Dialog Box Choosing the Smoothing Constant Setting Up the Analysis Using Solver to Find the Best Smoothing Constant Understanding Solver''s Requirements The Point Handling Linear Baselines with Trend Characteristics of Trend First Differencing Holt''s Linear Exponential Smoothing About Terminology and Symbols in Handling Trended Series Using Holt Linear Smoothing
Chapter 5 Forecasting a Time Series: Regression Forecasting with Regression Linear Regression: An Example Using the LINEST() Function Forecasting with Autoregression Problems with Trends Correlating at Increasing Lags A Review: Linear Regression and Autoregression Adjusting the Autocorrelation Formula Using ACFs Understanding PACFs Using the ARIMA Workbook
Chapter 6 Logistic Regression: The Basics Traditional Approaches to the Analysis Z-tests and the Central Limit Theorem Using Chi-Square Preferring Chi-square to a Z-test Regression Analysis on Dichotomies Homoscedasticity Residuals Are Normally Distributed Restriction of Predicted Range Ah, But You Can Get Odds Forever Probabilities and Odds How the Probabilities Shift Moving On to the Log Odds
Chapter 7 Logistic Regression: Further Issues An Example: Predicting Purchase Behavior Using Logistic Regression Calculation of Logit or Log Odds Comparing Excel with R: A Demonstration Getting R Running a Logistic Analysis in R The Purchase Data Set Statistical Tests in Logistic Regression Models Comparison in Multiple Regression Calculating the Results of Different Models Testing the Difference Between the Models Models Comparison in Logistic Regression
Chapter 8 Principal Components Analysis The Notion of a Principal Component Reducing Complexity Understanding Relationships Among Measurable Variables Maximizing Variance Components Are Mutually Orthogonal Using the Principal Components Add-In The R Matrix The Inverse of the R Matrix Matrices, Matrix Inverses, and Identity Matrices Features of the Correlation Matrix''s Inverse Matrix Inverses and Beta Coefficients Singular Matrices Testing for Uncorrelated Variables Using Eigenvalues Using Component Eigenvectors Factor Loadings Factor Score Coefficients Principal Components Distinguished from Factor Analysis Distinguishing the Purposes Distinguishing Unique from Shared Variance Rotating Axes
Chapter 9 Box-Jenkins ARIMA Models The Rationale for ARIMA Deciding to Use ARIMA ARIMA Notation Stages in ARIMA Analysis The Identification Stage Identifying an AR Process Identifying an MA Process Differencing in ARIMA Analysis Using the ARIMA Workbook Standard Errors in Correlograms White Noise and Diagnostic Checking Identifying Seasonal Models The Estimation Stage Estimating the Parameters for ARIMA(1,0,0) Comparing Excel''s Results to R''s Exponential Smoothing and ARIMA(0,0,1) Using ARIMA(0,1,1) in Place of ARIMA(0,0,1) The Diagnostic and Forecasting Stages

Predictive Analytics