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More Predictive Analytics: Microsoft Excel

Book Description

Accurate, practical Excel predictive analysis: powerful smoothing techniques for serious data crunchers!

In More Predictive Analytics, Microsoft Excel® MVP Conrad Carlberg shows how to use intuitive smoothing techniques to make remarkably accurate predictions. You won’t have to write a line of code--all you need is Excel and this all-new, crystal-clear tutorial.

Carlberg goes beyond his highly-praised Predictive Analytics, introducing proven methods for creating more specific, actionable forecasts. You’ll learn how to predict what customers will spend on a given product next year… project how many patients your hospital will admit next quarter… tease out the effects of seasonality (or patterns that recur over a day, year, or any other period)… distinguish real trends from mere “noise.”

Drawing on more than 20 years of experience, Carlberg helps you master powerful techniques such as autocorrelation, differencing, Holt-Winters, backcasting, polynomial regression, exponential smoothing, and multiplicative modeling.

Step by step, you’ll learn how to make the most of built-in Excel tools to gain far deeper insights from your data. To help you get better results faster, Carlberg provides downloadable Excel workbooks you can easily adapt for your own projects.

If you’re ready to make better forecasts for better decision-making, you’re ready for More Predictive Analytics.

  • Discover when and how to use smoothing instead of regression

  • Test your data for trends and seasonality

  • Compare sets of observations with the autocorrelation function

  • Analyze trended time series with Excel’s Solver and Analysis ToolPak

  • Use Holt's linear exponential smoothing to forecast the next level and trend, and extend forecasts further into the future

  • Initialize your forecasts with a solid baseline

  • Improve your initial forecasts with backcasting and optimization

  • Fully reflect simple or complex seasonal patterns in your forecasts

  • Account for sudden, unexpected changes in trends, from fads to new viral infections

  • Use range names to control complex forecasting models more easily

  • Compare additive and multiplicative models, and use the right model for each task

  • Table of Contents

    1. Title Page
    2. Copyright Page
    3. Contents at a Glance
    4. Table of Contents
    5. About the Author
    6. We Want to Hear from You!
    7. Reader Services
    8. Introduction
    9. 1. Smoothing and Its Alternatives
      1. Regression in Forecasting
      2. Regression or Smoothing?
    10. 2. Diagnosing Trend and Seasonality
      1. Inside the Autocorrelation
      2. Testing for Trend in the Observations
      3. Testing for Seasonality in a Time Series
    11. 3. Working with Trended Time Series
      1. Smoothing: The Basic Idea
      2. About the Smoothing Approaches to Forecasting
      3. Dealing with Trend: Holt’s Linear Exponential Smoothing
      4. Using Holt’s Linear Exponential Smoothing
    12. 4. Initializing Forecasts
      1. Setting Initial Values
      2. Getting an Initial Forecast with Backcasting
      3. Getting an Initial Forecast with Optimization
      4. Initializing with Regression
      5. Deciding to Use Polynomial Regression
    13. 5. Working with Seasonal Time Series
      1. Simple Seasonal Averages
      2. Moving Averages and Centered Moving Averages
      3. Linear Regression with Coded Vectors
      4. Simple Seasonal Exponential Smoothing
      5. Holt-Winters Models
    14. 6. Names, Addresses, and Formulas
      1. We Interrupt This Program...
      2. Establishing Names for Forecasting Formulas
      3. Using the Names in Forecasting Formulas
      4. Deriving the Error Correction Formulas
      5. Deriving the Formulas on the Worksheet
      6. Named Ranges for Holt-Winters Models
      7. Error Correction Formulas for Holt-Winters Models
    15. 7. Multiplicative and Damped Trend Models
      1. About Multiplicative Models
      2. Comparing Multiplicative and Additive Models
      3. Additive and Multiplicative Seasonal Models in Holt-Winters Analysis
      4. Damped Trend Forecasts