Reseña del editor:
Accurate, practical Excel predictive analysis: powerful smoothing techniques for serious data crunchers! In More Predictive Analytics, Microsoft Excel (R) 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
Reseña del editor:
Organizations of all types have discovered the immense power of predictive analytics for improving decision-making and profitability. Not everyone has access to expensive predictive analytics tools, and not everyone appreciates R's challenging interface -- but virtually every business professional is comfortable with one tool that'll serve the purpose admirably: Microsoft Excel. In More Predictive Analysis: Microsoft Excel, world-renowned Excel analytics expert Conrad Carlberg takes sophisticated Excel users to the cutting-edge with predictive analytics. Going far beyond the core techniques he introduced in Predictive Analytics: Microsoft Excel, Carlberg introduces advanced methods for solving a wide range of important analytical problems with Excel: solutions covered in no competitive book. Bringing the same clarity and insight that has made him the world's most respected author on Excel analytics and statistics, Carlberg covers all this, and more: * Multinomial logistic regression for applications with more than just two categorical outcomes (for example, patients exhibiting symptoms consistent with three or more possible diseases) * Probit analysis for scenarios where outcome variables are distributed categorically, with people or things assigned to a few genuinely different states of nature * Seasonal time series, such as home purchases and agricultural crop yields which exhibit seasonal peaks and valleys * Survival analysis using curvilinear regression, including Kaplan-Meier curves and the Cox Proportional Hazards model * Box-Jenkins models from start to finish: identification, estimation, diagnostics, and forecasting As always, Carlberg helps you create more credible, reliable forecasts -- and avoid pitfalls associated with simply "plugging" numbers into Excel's tools. He also provides downloadable Excel workbooks you can easily adapt to your unique requirements.
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