Learning Outcome Statements PDF Download
|1. Simple Linear Regression|
a. describe a simple linear regression model and the roles of the dependent and independent variables in the model;
|2. Estimating the Parameters of a Simple Linear Regression|
b. describe the least squares criterion, how it is used to estimate regression coefficients, and their interpretation;
|3. Assumptions of the Simple Linear Regression Model|
c. explain the assumptions underlying the simple linear regression model, and describe how residuals and residual plots indicate if these assumptions may have been violated;
|4. Analysis of Variance|
d. calculate and interpret the coefficient of determination and the F-statistic in a simple linear regression;
e. describe the use of analysis of variance (ANOVA) in regression analysis, interpret ANOVA results, and calculate and interpret the standard error of estimate in a simple linear regression;
|5. Hypothesis Testing of Linear Regression Coefficients|
f. formulate a null and an alternative hypothesis about a population value of a regression coefficient, and determine whether the null hypothesis is rejected at a given level of significance;
|6. Prediction Using Simple Linear Regression and Prediction Intervals|
g. calculate and interpret the predicted value for the dependent variable, and a prediction interval for it, given an estimated linear regression model and a value for the independent variable;
|7. Functional Forms for Simple Linear Regression|
h. describe different functional forms of simple linear regressions.
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