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Predictive Analytics: Microsoft® Excel 2016, 2nd Edition

Predictive Analytics: Microsoft® Excel 2016, 2nd Edition

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Description

  • Copyright 2018
  • Dimensions: 7" x 9-1/8"
  • Pages: 384
  • Edition: 2nd
  • eBook (Watermarked)
  • ISBN-10: 0-13-468291-2
  • ISBN-13: 978-0-13-468291-4

EXCEL 2016 PREDICTIVE ANALYTICS FOR SERIOUS DATA CRUNCHERS!


Now, you can apply cutting-edge predictive analytics techniques to help your business win–and you don’t need multimillion-dollar software to do it. All the tools you need are available in Microsoft Excel 2016, 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 problems in areas ranging from sales and marketing to operations. Carlberg offers unprecedented insight into building powerful, credible, and reliable forecasts, helping you gain deep insights from Excel that would be difficult to uncover with costly tools such as SAS or SPSS.


Fully updated for Excel 2016, this guide contains valuable new coverage of accounting for seasonality and managing complex consumer choice scenarios. Throughout, Carlberg provides downloadable Excel 2016 workbooks you can easily adapt to your own needs, plus VBA code–much of it open-source–to streamline especially 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 the “how” and “why” of using data to make better decisions, and choose the right technique for each problem


  • 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
  • Account for trends and seasonality via Holt-Winters smoothing
  • Prevent trends from running out of control over long time horizons
  • Construct more accurate predictions by using Solver
  • Manage large numbers of variables and unwieldy datasets with principal components analysis and Varimax factor rotation
  • Apply ARIMA (Box-Jenkins) techniques to build better forecasts and clarify their meaning
  • Handle complex consumer choice problems with advanced logistic regression
  • Benchmark Excel results against R results

Downloads

Downloads

Download the online worksheets (5.5 MB .zip)

Sample Content

Table of Contents

Introduction to the 2013 Edition ....................... 1
    You, Analytics, and Excel .....................................2
    Excel as a Platform .......4
    What’s in This Book ......4
Introduction to this Edition ............................... 7
    Inside the Black Box .....8
    Helping Out Your Colleagues ..............................8
1 Building a Collector .....................................11
    Planning an Approach .....................................12
        A Meaningful Variable ...............................12
        Identifying Sales ..13
    Planning the Workbook Structure ....................13
        Query Sheets .......13
        Summary Sheets .18
        Snapshot Formulas ....................................20
        Customizing Your Formulas ........................21
    The VBA Code .............23
        The DoItAgain Subroutine ...................24
        The DontRepeat Subroutine ................25
        The PrepForAgain Subroutine ...........25
        The GetNewData Subroutine ................26
        The GetRank Function............................30
        The RefreshSheets Subroutine .......32
    The Analysis Sheets....33
        Defining a Dynamic Range Name ..............34
        Using the Dynamic Range Name ...............36
2 Linear Regression .......................................39
    Correlation and Regression .............................39
        Charting the Relationship .........................40
        Calculating Pearson’s Correlation Coefficient ......................................43
    Correlation Is Not Causation .............................45
    Simple Regression .....46
        Array-Entering Formulas ...........................48
        Array-Entering LINEST( ) ..........................49
    Multiple Regression ..49
        Creating the Composite Variable ..............50
        Entering LINEST( ) with Multiple Predictors .......................................51
        Merging the Predictors .............................51
        Analyzing the Composite Variable ............53
    Assumptions Made in Regression Analysis ......54
        Variability ...........55
        Measures of Variability: Bartlett’s Test of Homogeneity of Variance ...57
        Means of Residuals Are Zero .....................58
        Normally Distributed Forecasts .................59
    Using Excel’s Regression Tool ...........................59
        Accessing the Data Analysis Add-ln ..........59
        Accessing an Installed Add-ln ...................60
        Running the Regression Tool .....................61
        Understanding the Regression Tool’s Dialog Box ................................62
        Understanding the Regression Tool’s Output .....................................64
3 Forecasting with Moving Averages ..............71
    About Moving Averages ..................................71
        Signal and Noise .72
        Smoothing Out the Noise .........................73
        Lost Periods ........74
        Smoothing Versus Tracking .......................74
        Weighted and Unweighted Moving Averages ....................................76
        Total of Weights ..77
        Relative Size of Weights ............................78
        More Recent Weights Are Larger ...............78
    Criteria for Judging Moving Averages .............80
        Mean Absolute Deviation ..........................80
        Least Squares ......80
        Using Least Squares to Compare Moving Averages .............................81
    Getting Moving Averages Automatically .........82
        Using the Moving Average Tool .................83
        Labels .................85
        Output Range .....85
        Actuals and Forecasts ................................85
        Interpreting the Standard Errors–Or Failing to Do So .......................87
4 Forecasting a Time Series: Smoothing ..........89
    Exponential Smoothing: The Basic Idea............90
    Why “Exponential” Smoothing? .......................92
    Using Excel’s Exponential Smoothing Tool ........95
        Understanding the Exponential Smoothing Dialog Box ......................96
    Choosing the Smoothing Constant ................102
        Setting Up the Analysis ...........................103
        Using Solver to Find the Best Smoothing Constant ...........................105
        Understanding Solver’s Requirements .....110
        The Point ...........113
    Handling Linear Baselines with Trend ............114
        Characteristics of Trend ............................114
        First Differencing .....................................117
5 More Advanced Smoothing Models ............123
    Holt’s Linear Exponential Smoothing .............123
        About Terminology and Symbols in Handling Trended Series ...........124
        Using Holt’s Linear Smoothing .................124
        Holt’s Method and First Differences .........130
    Seasonal Models ......133
        Estimating Seasonal Indexes ...................134
        Estimating the Series Level and First Forecast ..................................135
        Extending the Forecasts to Future Periods ........................................136
        Finishing the One-Step-Ahead Forecasts .137
        Extending the Forecast Horizon ...............138
    Using Additive Holt-Winters Models ..............140
        Level ..................143
        Trend .................143
        Season ...............144
    Formulas for the Holt-Winters Additive and Multiplicative Models.........145
        Formulas for the Additive Model .............146
        Formulas for the Multiplicative Model .....148
    The Models Compared ...................................149
    Damped Trend Forecasts ................................151
6 Forecasting a Time Series: Regression ........153
    Forecasting with Regression ..........................153
        Linear Regression: An Example ................155
        Using the LINEST( ) Function ...................158
    Forecasting with Autoregression....................164
        Problems with Trends ..............................164
        Correlating at Increasing Lags ..................165
        A Review: Linear Regression and Autoregression ..............................168
        Adjusting the Autocorrelation Formula ....169
        Using ACFs .........171
        Understanding PACFs ...............................172
        Using the ARIMA Workbook .....................178
7 Logistic Regression: The Basics...................181
    Traditional Approaches to the Analysis ..........181
        Z-tests and the Central Limit Theorem .....181
        Sample Size and Observed Rate ...............183
        Binomial Distribution ..............................183
        Only One Comparison ..............................184
        Using Chi-Square .....................................185
        Preferring Chi-Square to a Z-test .............187
    Regression Analysis on Dichotomies .............191
        Homoscedasticity ....................................191
        Residuals Are Normally Distributed ........194
        Restriction of Predicted Range ................194
    Ah, But You Can Get Odds Forever .................195
        Probabilities and Odds .............................195
        How the Probabilities Shift .....................197
        Moving On to the Log Odds ....................200
8 Logistic Regression: Further Issues .............203
    An Example: Predicting Purchase Behavior ....204
        Using Logistic Regression ........................205
        Calculation of Logit or Log Odds ..............213
    Comparing Excel with R: A Demonstration .....228
        Getting R ...........229
        Running a Logistic Analysis in R ..............229
        Importing a csv File into R .......................230
        Importing From an Open Workbook Into R .......................................233
        Understanding the Long Versus Wide Shape ....................................234
        Running Logistic Regression Using glm ...235
    Statistical Tests in Logistic Regression ............240
        Models Comparison in Multiple Regression ......................................240
        Calculating the Results of Different Models ......................................241
        Testing the Difference Between the Models .....................................242
        Models Comparison in Logistic Regression .......................................243
9 Multinomial Logistic Regression ................253
    The Multinomial Problem ..............................253
    Three Alternatives and Three Predictors .........254
        Three Intercepts and Three Sets of Coefficients .................................256
        Dummy Coding to Represent the Outcome Value .............................256
        Calculating the Logits ..............................256
        Converting the Logits to Probabilities ......257
        Calculating the Log Likelihoods ...............258
        Understanding the Differences Between the Binomial and Multinomial Equations ...............258
        Optimizing the Equations ........................260
    Benchmarking the Excel Results Against R ....261
        Converting the Raw Data Frame with mlogit.data ...................262
        Calling the mlogit Function .................264
        Completing the mlogit Arguments ......266
    Four Outcomes and One Predictor ..................267
        Multinomial Analysis with an Individual-Specific Predictor ..............269
        Multinomial Analysis with an Alternative-Specific Predictor ............272
10 Principal Components Analysis ..................275
    The Notion of a Principal Component ............275
        Reducing Complexity ...............................276
        Understanding Relationships Among Measurable Variables .............277
        Maximizing Variance................................278
        Components Are Mutually Orthogonal ....280
    Using the Principal Components Add-In ........281
        The R Matrix ......284
        The Inverse of the R Matrix ......................284
        Matrices, Matrix Inverses, and Identity Matrices ...............................287
        Features of the Correlation Matrix’s Inverse ......................................288
        Matrix Inverses and Beta Coefficients ......290
        Singular Matrices .....................................293
        Testing for Uncorrelated Variables ...........293
        Using Eigenvalues ....................................295
        Using Component Eigenvectors ...............296
        Factor Loadings .299
        Factor Score Coefficients ..........................299
    Principal Components Distinguished from Factor Analysis ......................303
        Distinguishing the Purposes ....................303
        Distinguishing Unique from Shared Variance ....................................303
        Rotating Axes ....305
11 Box-Jenkins ARIMA Models ........................307
    The Rationale for ARIMA ................................307
        Deciding to Use ARIMA ............................308
        ARIMA Notation .308
    Stages in ARIMA Analysis ...............................310
    The Identification Stage .................................310
        Identifying an AR Process ........................310
        Identifying an MA Process .......................313
        Differencing in ARIMA Analysis ................315
        Using the ARIMA Workbook .....................320
        Standard Errors in Correlograms ..............321
        White Noise and Diagnostic Checking......322
        Identifying Seasonal Models ....................323
    The Estimation Stage .....................................324
        Estimating the Parameters for ARIMA(1,0,0) ....................................324
        Comparing Excel’s Results to R’s ...............326
        Exponential Smoothing and ARIMA(0,0,1) .......................................329
        Using ARIMA(0,1,1) in Place of ARIMA(0,0,1) ...................................332
    The Diagnostic and Forecasting Stages ..........333
12 Varimax Factor Rotation in Excel ................335
    Getting to a Simple Structure .......................335
        Rotating Factors: The Rationale ...............336
        Extraction and Rotation: An Example ......339
    Structure of Principal Components and Factors ......................................344
        Rotating Factors: The Results ..................345
        Charting Records on Rotated Factors ......348
        Using the Factor Workbook to Rotate Components ..........................350
9780789758354, ToC, 6/30/2017

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