Simple Predictive Modeling with Microsoft® Excel

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This course teaches participants how to apply appropriate statistical procedures and models to answer business questions and, most importantly, effectively communicate impact to stakeholders.

Students participating in this 2-day course will learn how to:

  • Decide which tool or technique to use in a given situation
  • Apply data gathering and input considerations, including a conceptual groundwork of statistical reasoning
  • Set up appropriate hypotheses and run analyses in Excel
  • Interpret the results in context of the problem and communicate to stakeholders
  • Identify and avoid key pitfalls
  • Visualize and communicate the results in a fair, objective and unbiased manner

Students will leave the course able to use Excel to build statistical models that answer questions like:

  • What’s the relationship between a variable and an outcome?
  • If I adjust X, what will be the impact on Y? Are there natural limits I should be aware of?
  • Are we meeting expectations?
  • What’s coming next? Are we going up or down and by how much?
  • Why are we going up or down? How impactful is each variable? (in other words, what should I focus on first?)
  • Are there any unusual outliers? What caused those? Do I need to do something about this?
  • How likely is any given idea or decision or campaign to be successful?
  • Did any given change or decision make a material business impact?

Section I: Background Information

  • Why Use Statistics?
  • Installing the Data Analysis Tool Pack add-in for Excel

Section II: Analysis Fundamentals

  • Exploring and visualizing data
  • Descriptive statistics o Uses for specific measures and how to visualize
  • Samples vs. populations
  • Average, median, standard deviation, quartiles, percentiles, z-scores
  • Looking at the shape of the data and the impact of outliers
  • Cautions and common pitfalls (e.g. Anscombe’s Quartet)
  • Examining Relationships
  • Overview of Probability
  • Sampling Distributions and the Central Limit Theorem
  • Overview of Inference o Confidence intervals and p-values

Section III: Predictive Models

  • Method for Creating Predictive Models
  • How to Choose and Assess an Appropriate Model
  • Regression
  • When to use it
  • How to interpret meaningfully
  • For nonlinear data
  • Exponential Regression
  • Logarithmic Regression
  • Polynomial Regression
  • ANOVA
  • Chi-Square
  • Time Series & Forecasting
  • Logis
Course Information
  • Course Level:Advanced
  • Prerequisite:Students should be familiar with writing calculations and formulas within Excel.
  • Credit:16
  • Fee:$695
  • Length:2 days

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