Predictive Analytics

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 Chapter 10 Varimax Factor Rotation in Excel Getting to a Simple Structure Rotating Factors: The Rationale Extraction and Rotation: An Example Showing Text Labels Next to Chart Markers Structure of Principal Components and Factors Rotating Factors: The Results Charting Records on Rotated Factors Using the Factor Workbook to Rotate Components 9780789749413 TOC 6/18/2012