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E-raamat: Predictive Analytics: Microsoft Excel

  • Formaat: 304 pages
  • Ilmumisaeg: 26-Jun-2012
  • Kirjastus: Que Corporation,U.S.
  • Keel: eng
  • ISBN-13: 9780132967235
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  • Formaat: 304 pages
  • Ilmumisaeg: 26-Jun-2012
  • Kirjastus: Que Corporation,U.S.
  • Keel: eng
  • ISBN-13: 9780132967235
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Excel predictive analytics for serious data crunchers!

The movie Moneyball made predictive analytics famous: Now you can apply the same techniques to helpyour 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

Introduction 1(6)
1 Building a Collector
7(28)
Planning an Approach
8(1)
A Meaningful Variable
8(1)
Identifying Sales
8(1)
Planning the Workbook Structure
9(9)
Query Sheets
9(4)
Summary Sheets
13(2)
Snapshot Formulas
15(1)
More Complicated Breakdowns
16(2)
The VBA Code
18(10)
The DoItAgain Subroutine
19(1)
The GetNewData Subroutine
20(4)
The GetRank Function
24(2)
The GetUnitsLeft Function
26(1)
The RefreshSheets Subroutine
27(1)
The Analysis Sheets
28(7)
Defining a Dynamic Range Name
29(1)
Using the Dynamic Range Name
30(5)
2 Linear Regression
35(30)
Correlation and Regression
35(7)
Charting the Relationship
36(2)
Calculating Pearson's Correlation Coefficient
38(3)
Correlation Is Not Causation
41(1)
Simple Regression
42(3)
Array-Entering Formulas
44(1)
Array-Entering LINEST()
44(1)
Multiple Regression
45(5)
Creating the Composite Variable
45(3)
Analyzing the Composite Variable
48(2)
Assumptions Made in Regression Analysis
50(4)
Variability
50(4)
Using Excel's Regression Tool
54(11)
Accessing the Data Analysis Add-In
54(2)
Running the Regression Tool
56(9)
3 Forecasting with Moving Averages
65(18)
About Moving Averages
65(8)
Signal and Noise
66(2)
Smoothing Versus Tracking
68(2)
Weighted and Unweighted Moving Averages
70(3)
Criteria for Judging Moving Averages
73(3)
Mean Absolute Deviation
73(1)
Least Squares
74(1)
Using Least Squares to Compare Moving Averages
74(2)
Getting Moving Averages Automatically
76(7)
Using the Moving Average Tool
76(7)
4 Forecasting a Time Series: Smoothing
83(40)
Exponential Smoothing: The Basic Idea
84(2)
Why "Exponential" Smoothing?
86(3)
Using Excel's Exponential Smoothing Tool
89(7)
Understanding the Exponential Smoothing Dialog Box
90(6)
Choosing the Smoothing Constant
96(12)
Setting Up the Analysis
97(2)
Using Solver to Find the Best Smoothing Constant
99(5)
Understanding Solver's Requirements
104(3)
The Point
107(1)
Handling Linear Baselines with Trend
108(7)
Characteristics of Trend
108(3)
First Differencing
111(4)
Holt's Linear Exponential Smoothing
115(8)
About Terminology and Symbols in Handling Trended Series
115(1)
Using Holt Linear Smoothing
116(7)
5 Forecasting a Time Series: Regression
123(26)
Forecasting with Regression
123(10)
Linear Regression: An Example
125(3)
Using the LINEST() Function
128(5)
Forecasting with Autoregression
133(16)
Problems with Trends
134(1)
Correlating at Increasing Lags
134(3)
A Review: Linear Regression and Autoregression
137(2)
Adjusting the Autocorrelation Formula
139(1)
Using ACFs
140(2)
Understanding PACFs
142(5)
Using the ARIMA Workbook
147(2)
6 Logistic Regression: The Basics
149(20)
Traditional Approaches to the Analysis
149(9)
Z-tests and the Central Limit Theorem
149(4)
Using Chi-Square
153(2)
Preferring Chi-square to a Z-test
155(3)
Regression Analysis on Dichotomies
158(4)
Homoscedasticity
158(3)
Residuals Are Normally Distributed
161(1)
Restriction of Predicted Range
161(1)
Ah, But You Can Get Odds Forever
162(7)
Probabilities and Odds
163(1)
How the Probabilities Shift
164(2)
Moving On to the Log Odds
166(3)
7 Logistic Regression: Further Issues
169(42)
An Example: Predicting Purchase Behavior
170(23)
Using Logistic Regression
171(8)
Calculation of Logit or Log Odds
179(14)
Comparing Excel with R: A Demonstration
193(5)
Getting R
193(1)
Running a Logistic Analysis in R
194(1)
The Purchase Data Set
195(3)
Statistical Tests in Logistic Regression
198(13)
Models Comparison in Multiple Regression
198(1)
Calculating the Results of Different Models
199(1)
Testing the Difference Between the Models
200(1)
Models Comparison in Logistic Regression
201(10)
8 Principal Components Analysis
211(30)
The Notion of a Principal Component
211(5)
Reducing Complexity
212(1)
Understanding Relationships Among Measurable Variables
213(1)
Maximizing Variance
214(1)
Components Are Mutually Orthogonal
215(1)
Using the Principal Components Add-In
216(20)
The R Matrix
219(1)
The Inverse of the R Matrix
220(2)
Matrices, Matrix Inverses, and Identity Matrices
222(1)
Features of the Correlation Matrix's Inverse
223(2)
Matrix Inverses and Beta Coefficients
225(2)
Singular Matrices
227(1)
Testing for Uncorrelated Variables
228(1)
Using Eigenvalues
229(2)
Using Component Eigenvectors
231(2)
Factor Loadings
233(1)
Factor Score Coefficients
233(3)
Principal Components Distinguished from Factor Analysis
236(5)
Distinguishing the Purposes
236(1)
Distinguishing Unique from Shared Variance
237(1)
Rotating Axes
238(3)
9 Box-Jenkins ARIMA Models
241(26)
The Rationale for ARIMA
241(3)
Deciding to Use ARIMA
242(1)
ARIMA Notation
242(2)
Stages in ARIMA Analysis
244(1)
The Identification Stage
244(13)
Identifying an AR Process
244(4)
Identifying an MA Process
248(1)
Differencing in ARIMA Analysis
249(3)
Using the ARIMA Workbook
252(1)
Standard Errors in Correlograms
253(1)
White Noise and Diagnostic Checking
254(1)
Identifying Seasonal Models
255(2)
The Estimation Stage
257(7)
Estimating the Parameters for ARIMA(1,0,0)
257(2)
Comparing Excel's Results to R's
259(2)
Exponential Smoothing and ARIMA(0,0,1)
261(2)
Using ARIMA(0,1,1) in Place of ARIMA(0,0,1)
263(1)
The Diagnostic and Forecasting Stages
264(3)
10 Varimax Factor Rotation in Excel
267(16)
Getting to a Simple Structure
267(9)
Rotating Factors: The Rationale
268(3)
Extraction and Rotation: An Example
271(4)
Showing Text Labels Next to Chart Markers
275(1)
Structure of Principal Components and Factors
276(7)
Rotating Factors: The Results
277(2)
Charting Records on Rotated Factors
279(2)
Using the Factor Workbook to Rotate Components
281(2)
Index 283
Counting conservatively, this is Conrad Carlbergs eleventh book about quantitative analysis using Microsoft Excel, which he still regards with a mix of awe and exasperation. A look back at the About the Author paragraph in Carlbergs first book, published in 1995, shows that the only word that remains accurate is He. Scary.