Open an Excel file-go to “Data”-click on “Data Analysis”-click on “Regression”-select the dependent variable (Input Y Range)-Select the independent variables (Input X Range)-Check Labels box if you include variable names in your data ranges-Select an output range that does not overlap with the data-Click OK button.

Chapter10 introduces bivariate and multiple regression analysis. In the previous unit, we learned an essential method (contingency table analysis) for analyzing the relationship between an independent variable (nominal or ordinal) and a dependent variable (nominal or ordinal). We also learned about the role of inferential statistics in evaluating the statistical significance of a relationship. We are now familiar with Chi-square test and correlation analysis. We will now shift from analyzing relations to making predictions. Chapter 10 helps us learn one such prediction tool, called regression. Simple linear regression enable us not only to quantify the relation between two variable but allows us to predict a person’s score on the dependent variable from their score on one independent variable. Multiple linear regression predicts scores on a single dependent variable from scores on more than one independent variable. By doing this, multiple regression allows us to better predict a given outcome.

Please read the Wang text, chapter 10 along with my supplementary notes # 5 to understand what regression add above and beyond what we learn from correlation, the form of a simple linear regression equation, the interpretation of a regression line (the slope and the intercept), the assumptions of linear regression, and finally the interpretation of the coefficient of determination (R2)

Q1. Solve the case study problem in chapter 10 of Wang (Page 230-234) [2.5 points]

Q2. Solve practice problem 10.1 from chapter 10 of the Wang text.