a. State before doing any calculations whether you think they are positively or negatively correlated. What is your rationale?
Example: I test for a correlation between the quantity of coffee that people buy (Y) with the price of coffee (X1) and the household income (X2).
I hypothesize that there is a negative correlation between quantity and price because people like to buy goods at lower rather than higher prices. I also hypothesize that there is also a positive correlation between the quantity of coffee and household income because people can buy more coffee when their income increases.
b. Draw a graph of each of the two independent variables with the dependent variable either by hand or by using Excel. (Do this by inserting an XY/Scatter chart.)
c. Use Excel to do the necessary regression. Give the values for the y-intercept, b1 and b2. Write out the equation. Also show adjusted R-square, the F-statistic and its p-value and the t-statistics with their respective p-values.
d. Test for multicollinearity using the rule that the two independent variables are multicollinear if their correlation coefficient is .70 or greater (implying r-square is .49 or greater). If they are multicolliear, give a brief statement on why do you think that is the case.