Enterprise Industries produces Fresh, a liquid laundry detergent product. In order to study the relationship between price and sales of Fresh, the company has gathered data concerning the demand for Fresh over the last 30 sales periods (each sales period is four weeks. Thus, 30 sales periods mean 120 weeks or roughly two years and four months). The data can be found in the SPSS data file Fresh. sav. Here is the description of the variables in the data file.

2. (Simple Linear Regression, Week 13) Enterprise Industries wants to know how the price of Fresh (the variable Price) is related to its sales volume (the variable Sales). That is, the company wants to estimate the following regression model: Sales=0+1Price+. Estimate this regression model in SPSS and answer the following questions below.

2-1) Copy and paste the regression result from SPSS and write the estimated regression model. To write the estimated regression model, you will need to find the estimated s from the SPSS output and plug them into the regression equation. (5 points)

2-2) What is the average effect of Price on Sales? That is, if Enterprise Industries increases Fresh price by one dollar, the sales of Fresh will decrease by _______. Fill the blank and explain how you derived the number. (5 points)

PLEASE USE SPSS

Fresh.sav a 15: Price 3.75 Period Sales Price Comp Price val var var 1 1 7.3800000000000 3.85 3.8000000000000 2. 2 8.5100000000000 3.75 4.0000000000000 3 3 9.5200000000000 3.70 4.3000000000000 4 4 7.5000000000000 3.70 3.7000000000000 5 5 9.3300000000000 3.60 3.8500000000000 6 6 8.2800000000000 3.60 3.8000000000000 7 7 8.7500000000000 3.60 3.7500000000000 8 8 7.8700000000000 3.80 3.8500000000000 9 3.80 3.6500000000000 9 7.1000000000000 10 8.0000000000000 10 3.85 4.0000000000000 11 11 7.8900000000000 3.90 4.1000000000000 12 12 8.1500000000000 3.90 4.0000000000000 13 13 9.1000000000000 3.70 4.1000000000000 14 14 8.8600000000000 3.75 4.2000000000000 15 15 8.9000000000000 3.75 4.1000000000000 16 16 8.8700000000000 3.80 4.1000000000000 17 17 9.2600000000000 3.70 4.2000000000000 18 18 9.0000000000000 3.80 4.3000000000000 19 19 8.7500000000000 3.70 4.1000000000000 20 20 7.9500000000000 3.80 3.7500000000000 21 21 7.6500000000000 3.80 3.7500000000000 22 22 7.2700000000000 3.75 3.6500000000000 23 23 8.0000000000000 3.70 3.9000000000000 24 24 8.5000000000000 3.55 3.6500000000000 25 25 8.7500000000000 3.60 4.1000000000000 26 26 9.2100000000000 3.65 4.2500000000000 27 27 8.2700000000000 3.70 3.6500000000000 28 28 7.6700000000000 3.75 3.7500000000000 29 29 7.9300000000000 3.80 3.8500000000000 30 30 9.2600000000000 3.70 4.2500000000000 31 Fresh (1).sav [DataSet1] - IBM SPSS Statistics Data Editor 10. Q Name Type Width Decimals Label Values Missing Columns 1 Period Numeric 2 0 None None 12 Align Right Right 2 Sales Numeric 31 15 None None 12 3 Price Numeric 4 2 None None 12 Right Right 4 Comp Price Numeric 31 15 None None 12 Fresh.sav a 15: Price 3.75 Period Sales Price Comp Price val var var 1 1 7.3800000000000 3.85 3.8000000000000 2. 2 8.5100000000000 3.75 4.0000000000000 3 3 9.5200000000000 3.70 4.3000000000000 4 4 7.5000000000000 3.70 3.7000000000000 5 5 9.3300000000000 3.60 3.8500000000000 6 6 8.2800000000000 3.60 3.8000000000000 7 7 8.7500000000000 3.60 3.7500000000000 8 8 7.8700000000000 3.80 3.8500000000000 9 3.80 3.6500000000000 9 7.1000000000000 10 8.0000000000000 10 3.85 4.0000000000000 11 11 7.8900000000000 3.90 4.1000000000000 12 12 8.1500000000000 3.90 4.0000000000000 13 13 9.1000000000000 3.70 4.1000000000000 14 14 8.8600000000000 3.75 4.2000000000000 15 15 8.9000000000000 3.75 4.1000000000000 16 16 8.8700000000000 3.80 4.1000000000000 17 17 9.2600000000000 3.70 4.2000000000000 18 18 9.0000000000000 3.80 4.3000000000000 19 19 8.7500000000000 3.70 4.1000000000000 20 20 7.9500000000000 3.80 3.7500000000000 21 21 7.6500000000000 3.80 3.7500000000000 22 22 7.2700000000000 3.75 3.6500000000000 23 23 8.0000000000000 3.70 3.9000000000000 24 24 8.5000000000000 3.55 3.6500000000000 25 25 8.7500000000000 3.60 4.1000000000000 26 26 9.2100000000000 3.65 4.2500000000000 27 27 8.2700000000000 3.70 3.6500000000000 28 28 7.6700000000000 3.75 3.7500000000000 29 29 7.9300000000000 3.80 3.8500000000000 30 30 9.2600000000000 3.70 4.2500000000000 31 Fresh (1).sav [DataSet1] - IBM SPSS Statistics Data Editor 10. Q Name Type Width Decimals Label Values Missing Columns 1 Period Numeric 2 0 None None 12 Align Right Right 2 Sales Numeric 31 15 None None 12 3 Price Numeric 4 2 None None 12 Right Right 4 Comp Price Numeric 31 15 None None 12