$33.99
What is included with this book?
Introduction | p. 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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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