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The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature x₁, the number of days in the month x2, the average product purity as a percent x3, and the tons of product produced x4. The past year's historical data are available and are presented in the accompanying table. Complete parts (a) and (b) below. Click the icon to view the historical data. (a) Fit a multiple linear regression model using the data set. y = CD + (x + CDX + CDX3 + Oxa (Round the constant to two decimal places as needed. Round all other values to four decimal places as needed.) (b) Predict power consumption for a month in which x₁ = 65°F, x2 = 27 days, x3 = 85%, and x4 = 104 tons. The predicted power consumption is (Round to one decimal place as needed.) Historical data y x1 x2 X3 x4 260 24 24 92 100 234 30 21 90 95 268 44 24 89 111 291 59 26 87 87 281 64 24 90 94 317 73 27 93 99 302 79 26 86 98 303 83 26 85 97 283 75 24 87 111 289 60 25 91 104 288 49 244 38 22 26 89 100 22 88 98

The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature x₁, the number of days in the month x2, the average product purity as a percent x3, and the tons of product produced x4. The past year's historical data are available and are presented in the accompanying table. Complete parts (a) and (b) below. Click the icon to view the historical data. (a) Fit a multiple linear regression model using the data set. y = CD + (x + CDX + CDX3 + Oxa (Round the constant to two decimal places as needed. Round all other values to four decimal places as needed.) (b) Predict power consumption for a month in which x₁ = 65°F, x2 = 27 days, x3 = 85%, and x4 = 104 tons. The predicted power consumption is (Round to one decimal place as needed.) Historical data y x1 x2 X3 x4 260 24 24 92 100 234 30 21 90 95 268 44 24 89 111 291 59 26 87 87 281 64 24 90 94 317 73 27 93 99 302 79 26 86 98 303 83 26 85 97 283 75 24 87 111 289 60 25 91 104 288 49 244 38 22 26 89 100 22 88 98

College Algebra
College Algebra
1st Edition
ISBN:9781938168383
Author:Jay Abramson
Publisher:Jay Abramson
Chapter6: Exponential And Logarithmic Functions
Section: Chapter Questions
Problem 33PT: Enter the data from Table 2 into a graphing calculator and graph the ranking scatter plot. Determine...
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Solve the attached question and round as needed in each part please

The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature x₁, the number of days in the month x2, the average product purity as a percent x3, and the tons of product produced x4. The past year's historical data are available and are presented in the accompanying table. Complete parts (a) and (b) below. Click the icon to view the historical data. (a) Fit a multiple linear regression model using the data set. y = CD + (x + CDX + CDX3 + Oxa (Round the constant to two decimal places as needed. Round all other values to four decimal places as needed.) (b) Predict power consumption for a month in which x₁ = 65°F, x2 = 27 days, x3 = 85%, and x4 = 104 tons. The predicted power consumption is (Round to one decimal place as needed.)
Transcribed Image Text:The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature x₁, the number of days in the month x2, the average product purity as a percent x3, and the tons of product produced x4. The past year's historical data are available and are presented in the accompanying table. Complete parts (a) and (b) below. Click the icon to view the historical data. (a) Fit a multiple linear regression model using the data set. y = CD + (x + CDX + CDX3 + Oxa (Round the constant to two decimal places as needed. Round all other values to four decimal places as needed.) (b) Predict power consumption for a month in which x₁ = 65°F, x2 = 27 days, x3 = 85%, and x4 = 104 tons. The predicted power consumption is (Round to one decimal place as needed.)
Historical data y x1 x2 X3 x4 260 24 24 92 100 234 30 21 90 95 268 44 24 89 111 291 59 26 87 87 281 64 24 90 94 317 73 27 93 99 302 79 26 86 98 303 83 26 85 97 283 75 24 87 111 289 60 25 91 104 288 49 244 38 22 26 89 100 22 88 98
Transcribed Image Text:Historical data y x1 x2 X3 x4 260 24 24 92 100 234 30 21 90 95 268 44 24 89 111 291 59 26 87 87 281 64 24 90 94 317 73 27 93 99 302 79 26 86 98 303 83 26 85 97 283 75 24 87 111 289 60 25 91 104 288 49 244 38 22 26 89 100 22 88 98
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