QUESTION 1 For a large retailer, the plot below gives the sales for the first quarter of 1998 through the last quarter of 2002 in millions of dollars. 3500 2500 Sales 1500 500 Quarta Year 1998 1999 2000 2001 2002 This time series of sales exhibits: O A. seasonal variation. B. an increasing trend. C. biannual variation. O D. seasonal variation with an increasing trend. QUESTION 2 3500 250 Sale 1500 500 Quarts Year 1998 1999 2000 2001 2002 Using regression software, the estimated linear trend model is Sales = 1322 + 50.7x where x is the number of quarters elapsed since the beginning of the series. Using this trend-only model, the predicted sales for the second quarter of 2003 are: A. $1322 million. O B. $1423.4 million. O c. $2386.7 million. D. $2437.4 million.QUESTION 17 Determining the sale price of a home is an important task for city assessors as it helps the city project future tax revenue. Regression models using the physical characteristics of a home to predict the sale price is standard practice for many assessors. A random sample of 724 homes sold in Ames, lowa, between 2006 and 2010 was obtained to build such a model for the city of Ames. The assessor considered the following variables in their initial model: Variable Description LotArea Lot size (in thousands of square feet) LivingArea Living space (in thousands of square feet) Bedrooms Number of bedrooms Rooms Number of rooms Fireplaces Number of fireplaces Bath Number of bathrooms Age Age of the home (in years) Price Sale price of the home (in thousands of Below is the output obtained from the statistical software: Std Estimate Error t value Pr(It (Intercept) 100.55 6.167 16.31 It (Intercept) 100.55 6.167 16.31 |+1] (Intercept) 100.55 6.167 16.31 It (Intercept) 100.55 6.167 .6.31