Historical data y x1 x2 X3 x4 249 24 23 91 101 232 30 21 89 95 276 45 25 88 111 291 59 26 87 89 290 65 25 90 95 299 72 25 93 98 287 79 24 88 96 306 85 26 86 97 284 74 24 88 111 281 61 24 91 104 291 51 26 90 99 255 39 23 89 97 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. ŷ = 0 + O×₁ + O×2 + (1) ×3+ (1) ×4 (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 = 78°F, x2 = 26 days, x3 = 88%, and x4 = 108 tons. The predicted power consumption is ☐ . (Round to one decimal place as needed.)
Historical data y x1 x2 X3 x4 249 24 23 91 101 232 30 21 89 95 276 45 25 88 111 291 59 26 87 89 290 65 25 90 95 299 72 25 93 98 287 79 24 88 96 306 85 26 86 97 284 74 24 88 111 281 61 24 91 104 291 51 26 90 99 255 39 23 89 97 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. ŷ = 0 + O×₁ + O×2 + (1) ×3+ (1) ×4 (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 = 78°F, x2 = 26 days, x3 = 88%, and x4 = 108 tons. The predicted power consumption is ☐ . (Round to one decimal place as needed.)
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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I need help with this please
Transcribed Image Text:Historical data
y
x1
x2
X3
x4
249
24
23
91
101
232
30
21
89
95
276
45
25
88
111
291
59
26
87
89
290
65
25
90
95
299
72
25
93
98
287
79
24
88
96
306
85
26
86
97
284
74
24
88
111
281
61
24
91
104
291
51
26
90
99
255
39
23
89
97
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.
ŷ = 0 + O×₁ + O×2 + (1) ×3+ (1) ×4
(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 = 78°F, x2 = 26 days, x3 = 88%, and x4 = 108 tons.
The predicted power consumption is ☐ .
(Round to one decimal place as needed.)
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