The owners of a chain of ice cream stores have the business objective of improving the forecast of daily sales so that staffing shortages can be minimized during the summer season. As a starting point, the owners decide to develop a simplelinear regression model to predict daily sales based on atmospheric temperature. They select a sample of 15 consecutive days. The results are shown in the accompanying table. Complete parts (a) through (f) below.Click the icon to view the data table.E Click the icon to view a table of critical values of the Durbin-Watson statistic.a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients b, and b,.b, = - 2.7630 (Round to four decimal places as needed.)%3DIce cream salesb, = (Round to four decimal places as needed.)%3DDay Sales ($1,000)Temperature (°F)63121.521.68717531.921.79747642.022.366.812.162.69818842.9188103.179011123.023.27909399133.422.84888314152.54

The owners of a chain of ice cream stores have the business objective of improving the forecast of daily sales so that staffing shortages can be minimized duning the linear regression model to predict daily sales based on atmospheric temperature. They select a sample of 15 consecutive days. The results are shown in the accompanying table. Complete parts (a) through (f) below. staring point, A Click the icon to view the data table. Click the icon to view a table of critical values of the Durbin-Watson statistic. a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b,. bo = -2.7630 (Round to four decimal places as needed.) - X Ice cream sales b, = (Round to four decimal places as needed.) Temperature (F) Day Sales ($1,000) 1.52 1.68 1.92 63 71 3 75 74 4. 1.79 2.02 2.36 76 81 6. 7. 2.16 2.69 2.91 81 8 84 9 88 10 90 3.17 3.02 3.27 11 90 12 93 99 13 3.42 14 2.84 88 83 15 2.54

The owners of a chain of ice cream stores have the business objective of improving the forecast of daily sales so that staffing shortages can be minimized duning the linear regression model to predict daily sales based on atmospheric temperature. They select a sample of 15 consecutive days. The results are shown in the accompanying table. Complete parts (a) through (f) below. staring point, A Click the icon to view the data table. Click the icon to view a table of critical values of the Durbin-Watson statistic. a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b,. bo = -2.7630 (Round to four decimal places as needed.) - X Ice cream sales b, = (Round to four decimal places as needed.) Temperature (F) Day Sales ($1,000) 1.52 1.68 1.92 63 71 3 75 74 4. 1.79 2.02 2.36 76 81 6. 7. 2.16 2.69 2.91 81 8 84 9 88 10 90 3.17 3.02 3.27 11 90 12 93 99 13 3.42 14 2.84 88 83 15 2.54

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.4: Linear Regression

Problem 12SBE: Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4

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Question

The owners of a chain of ice cream stores have the business objective of improving the forecast of daily sales so that staffing shortages can be minimized during the summer season. As a starting point, the owners decide to develop a simple
linear regression model to predict daily sales based on atmospheric temperature. They select a sample of 15 consecutive days. The results are shown in the accompanying table. Complete parts (a) through (f) below.
Click the icon to view the data table.
E Click the icon to view a table of critical values of the Durbin-Watson statistic.
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients b, and b,.
b, = - 2.7630 (Round to four decimal places as needed.)
%3D
Ice cream sales
b, = (Round to four decimal places as needed.)
%3D
Day Sales ($1,000)
Temperature (°F)
63
1
2
1.52
1.68
71
75
3
1.92
1.79
74
76
4
2.02
2.36
6.
81
2.16
2.69
81
8
84
2.91
88
10
3.17
90
11
12
3.02
3.27
90
93
99
13
3.42
2.84
88
83
14
15
2.54

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