Available dates

Nov 18—21, 2019
4 days
Washington, District of Columbia, United States
USD 1659
USD 414 per day
Jan 21—24, 2020
4 days
Washington, District of Columbia, United States
USD 1659
USD 414 per day
Mar 17—20, 2020
4 days
Washington, District of Columbia, United States
USD 1659
USD 414 per day
+6 more options

Disclaimer

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Full disclaimer.

About the course

Data analysis is only useful if it pertains to an organization’s most complex challenges and requirements. You will learn the core tools used to analyze data, how to model data with appropriate analysis techniques, and the skills to interpret and then answer the hypotheses you constructed. Lessons are enhanced with scenarios, collaborative exercises, and a simulation to connect these concepts and skills to real-life application.

Learning Objectives

  • Generate and test null and alternate hypotheses
  • Select the appropriate technique for your analysis situation
  • Evaluate how well an analysis meets organizational goals
  • Use analyses of variance (ANOVAs) to evaluate differences between data sets
  • Use simulation, sensitivity analysis, and other techniques to solve complex problems
  • Explain relationships in data using regression analysis
  • Use regression and other techniques to forecast probable events

Course Topics

The Analytics Process Model

  • The Analytics Process Model
  • Applying the Analytics Process Model to Small Analyses
  • The Problem at the Wild Plant Control Agency (WPCA)

Data Definitions and Analysis Techniques

  • Elements, Variables, and Observations
  • Levels of Measurement
  • Selecting Analytical Techniques
  • Evaluating the Analysis

Descriptive Statistics

  • Measures of Central Tendency
  • Analysis ToolPak Descriptive Statistics

Statistical Hypothesis Generation and Testing

  • Null and Alternate Hypotheses
  • Statistical Significance
  • Rejecting the Null Hypothesis
  • Problems with Statistical Significance
  • Types of Statistical Error
  • Guarding Against Statistical Error

Chi-Square Test

  • Chi-Square Test
  • Chi-Square Test for Goodness of Fit
  • Performing a Goodness of Fit Test in Excel
  • Cross Tabulations and the Chi-Square Test for Independence
  • Performing a Test for Independence with CrossTabs in Excel

t-Test

  • The Normal Distribution
  • Introduction to the z Statistic
  • Reading a Normal Probability Table
  • The t Statistic
  • Single Sample t Test
  • Analysis ToolPak t Test Two Sample Assuming Unequal Variance
  • Analysis ToolPak t Test Paired Two Sample for Means
  • Exercise: WPCA t Test

Analysis of Variance (ANOVA)

  • ANOVA Overview
  • One-Way (Single-Factor) ANOVA
  • Post Hoc Tests
  • Performing a Single-Factor ANOVA with the Analysis ToolPak
  • Constructing the Scheffé Test in Excel
  • Exercise: WPCA ANOVA

Correlation

  • Correlation
  • Obtaining a Correlation Matrix with the Analysis ToolPak
  • Exercise: WPCA Correlation

Regression

  • Simple Linear Regression
  • Calculating the Linear Regression Equation with the Analysis ToolPak
  • Multiple Regression
  • Multiple Regression and Calculating the Regression Equation with the Analysis ToolPak

Forecasting

  • Overview of Forecasting
  • Forecasting with Simple Regression
  • Trendline Forecasting Options
  • Time Series Analysis
  • Forecasting with Multiple Regression
  • Dummy Variables
  • Other Forecasting Methods
  • Measuring Forecast Accuracy
  • Exercise: WPCA Simple and Multiple Regression Forecasting
  • Exercise: WPCA Time Series Analysis Forecasting

Simulation

  • Simulation Overview
  • Monte Carlo Simulations

Sensitivity Analysis

  • Overview of Sensitivity Analyses
  • Developing Business Scenarios
  • Performing a Sensitivity Analysis Using Excel’s Scenario Manager
  • Performing a Sensitivity Analysis Using Excel’s Goal Seek Feature
  • Performing a Sensitivity Analysis Using Excel’s Data Table Feature

Statistical Process Control

  • Statistical Process Control
  • Variation and Control
  • Control Charts

Who should attend

This course is designed for professionals who need to interpret data to aid in organizational decision-making and problem-solving.

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