SimpleLinearRegression.pptVIP

Chapter 12 Simple Linear Regression Correction from last week:ANOVA=t-test SSB algebra Chapter Goals After completing this chapter, you should be able to: Explain the simple linear regression model Obtain and interpret the simple linear regression equation for a set of data Evaluate regression residuals for aptness of the fitted model Understand the assumptions behind regression analysis Explain measures of variation and determine whether the independent variable is significant Chapter Goals After completing this chapter, you should be able to: Calculate and interpret confidence intervals for the regression coefficients Use the Durbin-Watson statistic to check for autocorrelation Form confidence and prediction intervals around an estimated Y value for a given X Recognize some potential problems if regression analysis is used incorrectly Correlation vs. Regression A scatter plot (or scatter diagram) can be used to show the relationship between two variables Correlation analysis is used to measure strength of the association (linear relationship) between two variables Correlation is only concerned with strength of the relationship No causal effect is implied with correlation Correlation was first presented in Chapter 3 Introduction to Regression Analysis Regression analysis is used to: Predict the value of a dependent variable based on the value of at least one independent variable Explain the impact of changes in an independent variable on the dependent variable Dependent variable: the variable we wish to explain Independent variable: the variable used to explain the dependent variable Simple Linear Regression Model Only one independent variable, X Relationship between X and Y is described by a linear function Changes in Y are assumed to be caused by changes in X Types of Relationships Types of Relationships Types of Relationships Simple Linear Regression Model Simple Linear Regression Model Simple Linear Regression Equation Least Squares Meth