If your ultimate goal is creating a model that will predict a future value for some dependent variable, then you need Regression Analysis. Regression Analysis is one of the most powerful statistical methods for determining the relationships between variables and using those relationships to forecast future outcomes.
Learn how to derive and implement simple and multiple linear regression models. Assess the overall quality of the models and interpret individual predictors for significance. Discover the assumptions that underlie the models and how to test whether your data meets those assumptions, and what to do when your assumptions are not met.
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- CEU Hours: 0.14
- Length: 1.42 Hours
- ASQ RU: 0.14
- Audience: Professional
- Provider: The Quality Group
- Review how to use a scatter plot to determine if two variables appear correlated and to what degree.
- Explain how to calculate the correlation coefficient and the coefficient of determination.
- Show how regression analysis can be used to predict the value of one variable from another variable by fitting a least squares regression line to the data and judging the validity of the model.
- Describe how to use information generated by a computer output from a simple linear regression to write the equation of the line and perform predictions based upon the model.
- Explain the purpose of multiple regression and how it differs from simple regression.
- Describe the elements of the multiple linear regression model and identify assumptions about the data required for regression analysis to work.
- Use the “best subsets” method to determine the possible regression models and apply several techniques for selecting the best model.
- Discuss what can go wrong with multiple regression that may lead to incorrect conclusions.
Prerequisites:Basic statistics and hypothesis testing familiarity
Who Should Attend:Persons with knowledge of inferential statistics including confidence intervals, hypothesis testing, and ANOVA.
Simple Linear Regression
- Univariate Data
- Bivariate Data
- Correlation and Regression
- Correlation Analysis
- Regression Analysis
- Interpreting the Model
- Interpreting the Estimates
- Model Assumptions
- Model Verification
- Simple Linear Regression (SLR) Steps/Review
- Multiple Regression
- Applications for Multiple Regression
- Multiple Regression Model
- Multiple Regression Example
- Types of Variables
- Dummy Variables
- Model Selection Criteria
- Model Selection Strategies
- Stepwise Regression
- Stepwise Selection
- Best Subsets
- Correlation Analysis
- Matrix Plot
- Variance Inflation Factor
- Subsets of Multiple Regression in Minitab
- Set Up Best Subsets
- Best Subsets Results
- Choosing the Model
- Standard Error of the Regression (S)
- Mallows Cp
- Rerun the Best Model
- Unusual Observations
- Residual Analysis
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|RATQG||Self-Paced||English||365 Days||List: $159.00 Member: $149.00|
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