The following table shows the appraised value of a house over time. Age (years) Value (thousands) 0 140 3 148 6 160 9 162 12 185 15 178 18 194 Using the data from the table determine if there is a linear trend between the age of a house and its value and determine if there is an exact linear fit of the data. Describe the linear trend if there is one. a. Negative linear trend, an exact linear fit. b. Negative linear trend, not an exact linear fit. c. Positive linear trend, an exact linear fit. d. Positive linear trend, not an exact linear fit.

1. the manager of collins import autos believes the number of cars sold in a day (q) depends on two factors: (1) the number of hours the dealership is open (h) and (2) the number of salespersons working that day (s). after collecting data for two months (53 days), the manager estimates the following log-linear model: q 4 equation 1 a. explain, how to transform the log-linear model into linear form that can be estimated using multiple regression analysis. the computer output for the multiple regression analysis is shown below: q4 table b. how do you interpret coefficients b and c? if the dealership increases the number of salespersons by 20%, what will be the percentage increase in daily sales? c. test the overall model for statistical significance at the 5% significance level. d. what percent of the total variation in daily auto sales is explained by this equation? what could you suggest to increase this percentage? e. test the intercept for statistical significance at the 5% level of significance. if h and s both equal 0, are sales expected to be 0? explain why or why not? f. test the estimated coefficient b for statistical significance. if the dealership decreases its hours of operation by 10%, what is the expected impact on daily sales?