Based on these data, multiple regression model equations can be obtained to predict per capita consumption which is influenced by paper consumption, fish consumption and fuel oil consumption. From Microsoft Excel processing, the following data are obtained:   Question : a. Make a multiple regression equation model! b. How is the hypothesis testing based on each independent variable! c. Based on the answer to b how to model the enhanced multiple regression equation

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Based on these data, multiple regression model equations can be obtained to predict per capita consumption which is influenced by paper consumption, fish consumption and fuel oil consumption. From Microsoft Excel processing, the following data are obtained:

 

Question :

a. Make a multiple regression equation model!
b. How is the hypothesis testing based on each independent variable!
c. Based on the answer to b how to model the enhanced multiple regression equation

Country
Fish
Consumption
per capita
Oil
Раper
Consumptions
(kg/1000
реople)
consumption
consumption
(kg)
1000
barrels/day
5454
Japan
Portugal
United States
19700
5570
76892
24126
158.7
132.7
277
84579
6860
15641
17033
430
16500
47
Venezuela
Greece
2090
4490
31.1
42.1
331
Italy
Norway
United
10790
13400
9040
43098
41575
52355
44.3
90.6
43.9
1936
183
1803
Kingdom
Philippines
640
940
76.3
235
Transcribed Image Text:Country Fish Consumption per capita Oil Раper Consumptions (kg/1000 реople) consumption consumption (kg) 1000 barrels/day 5454 Japan Portugal United States 19700 5570 76892 24126 158.7 132.7 277 84579 6860 15641 17033 430 16500 47 Venezuela Greece 2090 4490 31.1 42.1 331 Italy Norway United 10790 13400 9040 43098 41575 52355 44.3 90.6 43.9 1936 183 1803 Kingdom Philippines 640 940 76.3 235
OUTPUT
Regression Statistics
Multiple R
0,9677869
R Square
0,9366114
Adjusted R Square
0,8985783
Standard Error
2084,9394
Observations
9
ANOVA
Significance
df
SS
MS
F
F
Regression
321148160,6 107049387 24,62619 0,002013655
Residual
21734861,62
4346972,3
Total
8
342883022,2
Standard
Upper
Coefficients
Error
t Stat
P-value
Lower 95%
95%
-538,2796
1556,378436
0,3458539
0,74352
-4539,07774 3462,5185
Intercept
X Variable 1
X Variable 2
0,2336803
0,04397761
5,3136194
0,00315
0,12063223
0,3467283
18,087993
18,48045874
0,9787632
0,37264
-29,4175389
65,593524
X Variable 3
-0,211649
0,228690877
0,9254819
0,39718
-0,79951787
0,3762193
Transcribed Image Text:OUTPUT Regression Statistics Multiple R 0,9677869 R Square 0,9366114 Adjusted R Square 0,8985783 Standard Error 2084,9394 Observations 9 ANOVA Significance df SS MS F F Regression 321148160,6 107049387 24,62619 0,002013655 Residual 21734861,62 4346972,3 Total 8 342883022,2 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% -538,2796 1556,378436 0,3458539 0,74352 -4539,07774 3462,5185 Intercept X Variable 1 X Variable 2 0,2336803 0,04397761 5,3136194 0,00315 0,12063223 0,3467283 18,087993 18,48045874 0,9787632 0,37264 -29,4175389 65,593524 X Variable 3 -0,211649 0,228690877 0,9254819 0,39718 -0,79951787 0,3762193
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