In Excel’s regression, Y is the dependent variable and X is the independent variable. For
this exercise, Y = Sales and X = Advertising.
1. Input this data on a spreadsheet. Using the regression option, generate regression
predictions. Write your results as an estimated regression equation, as shown in my lecture
notes. In particular,
(a) Write the regression equation, with estimated coefficients,
(b) Below the regression equation, list in parentheses the t-values of the coefficient estimates

(c) To the right of the estimated equation write R =
Equation: ____Y_=___________________________________


R =

2. Multivariate Regression. Now add to your above regression in a price variable, with values:
8, 7.5, 7.25, 7.25, 6, 6.75, 6, 5, 4.4, 5.2. Estimate the new multiple regression equation (now
we have 2 independent variables). Print regression results. Write out the estimated demand
equation, as in 1 above.

Equation: ____Y_=___________________________________
t-values (

R2 =3. Evaluating regression results: A Descriptive Statistic. With the data you generated in (2) above
do the following.

a. Interpret the R . (In a sentence)

b. At an approximate 95% level of confidence, can you conclude that price affects sales? In
other words, is the estimated coefficient of PRICE statistically significant at the 5% level?
Does Price affect Sales? Y/N (circle one)
Reason: __________________________________________________