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Historical data y x1 X2 X3 x4 251 25 23 92 101 225 30 20 90 94 274 45 25 87 109 292 61 26 87 89 302 64 26 92 95 307 71 26 93 100 293 79 25 86 97 286 85 24 85 96 286 75 289 60 281 50 264 39 1222 24 89 109 25 91 105 25 90 100 24 89 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. (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 = 76°F, x2 = 26 days, x3 = 92%, and x4 = 89 tons. The predicted power consumption is ☐ (Round to one decimal place as needed.)

Historical data y x1 X2 X3 x4 251 25 23 92 101 225 30 20 90 94 274 45 25 87 109 292 61 26 87 89 302 64 26 92 95 307 71 26 93 100 293 79 25 86 97 286 85 24 85 96 286 75 289 60 281 50 264 39 1222 24 89 109 25 91 105 25 90 100 24 89 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. (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 = 76°F, x2 = 26 days, x3 = 92%, and x4 = 89 tons. The predicted power consumption is ☐ (Round to one decimal place as needed.)

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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I need help with part and b please. Question and historical data is attached

Historical data y x1 X2 X3 x4 251 25 23 92 101 225 30 20 90 94 274 45 25 87 109 292 61 26 87 89 302 64 26 92 95 307 71 26 93 100 293 79 25 86 97 286 85 24 85 96 286 75 289 60 281 50 264 39 1222 24 89 109 25 91 105 25 90 100 24 89 98
Transcribed Image Text:Historical data y x1 X2 X3 x4 251 25 23 92 101 225 30 20 90 94 274 45 25 87 109 292 61 26 87 89 302 64 26 92 95 307 71 26 93 100 293 79 25 86 97 286 85 24 85 96 286 75 289 60 281 50 264 39 1222 24 89 109 25 91 105 25 90 100 24 89 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. (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 = 76°F, x2 = 26 days, x3 = 92%, and x4 = 89 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. (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 = 76°F, x2 = 26 days, x3 = 92%, and x4 = 89 tons. The predicted power consumption is ☐ (Round to one decimal place as needed.)
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