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Basic Statistics you WILL use! October Online Training
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This is a live online training on October 22.

 Export to Your Calendar 10/22/2020
When: Thursday
12:00 PM
Where: GoToTraining Platform (Eastern Time)
United States

Online registration is available until: 10/21/2020
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Basic Statistics you WILL use (and maybe even love)!

Presented by M. Sami Khawaja, PhD, The Cadmus Group

Date: Thursday, Oct 22, 2020
Time: Noon – 4:00pm (ET)
Trainer: M. Sami Khawaja, PhD
CEUs: 0.2
Fee:  Members - $299 or 1 Group Member Point;
Non-members - $399

Basic Statistics. Reasons for statistics. What the various statistical measures (mean, variance, proportion, range) are. How are they used. How to compute/interpret them. Statistical Tests. How to use statistics for testing differences among groups (e.g., participants and nonparticipants). Among different time periods (e.g., pre and post). What are the statistical tests. How you compute/interpret them. Confidence and precision. Point and interval estimates. Sampling. Types, sample size determination. Data Sources/cleaning. Program tracking, weather, economic, demographics. Outliers, identification, corrections. Explaining variation. Regression models, assessing quality of the regression model. Additional tools (time permitting). You will learn how to say and explain cool things like ANOVA, covariance and correlation (and explain the difference), and dummy variables (their name has nothing to do with their intelligence).

Who should attend.

Evaluators, program staff and program managers with limited knowledge of statistics. All statistical terms will be explained in simple terms using data from real project. The emphasis of the workshop is on interpretation of statistical results.

This class is about making statistics useful and, more importantly, making it fun.


  • Understand basis statistical analysis for evaluation of DSM programs
  • Identify best methods for analyzing different types of data
  • Know the difference between mean, media, and mode and brag about it
  • Understand how variation in data matters
  • Define various ways to test particular program outcomes



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