64 : [4 x ( 27 - 5²)
= 64 : [ 4 x ( 27 - 25)
= 64 : ( 4 x 2)
= 64 : 8 = 8
5² = 5 x 5 = 25
50.64 - [4 x ( 27 - 5²)] = 50.64 - 4 ( 27 - 25) = 50.64 - 4 x 2 = 50.64 - 8 = 42.64
Answer:
Concept: Statistical Modeling
- N= 8 trials
- P= 0.25 or 1/4 chance
- You have 8 * 1/4 = 2 correct
I believe the correct answer to the question above this sentence would have to be 1.20300.
Answer:
SSE = 127.3
SST= 281.2
SSR = 153.9
R² = 0.5473
% = 54.73%
r= +0.7398
Step-by-step explanation:
xi yi Y^ (Yi-Y^)² (Yi-Y`)²
2 7 7.6 + 0.9(2) (7-9.4)² (7-16.6)²
= 9.4 = 5.76 92.16
6 18 7.6 + 0.9(6) (18-13)² (18-16.6)²
= 13 =25 1.96
9 9 7.6 + 0.9(9) (9-15.7)² (18-16.6)²
= 15.7 =44.89 57.76
13 26 7.6 + 0.9(13) (26-19.3)² (18-16.6)²
= 19.3 = 44.89 88.36
20 23 7.6 + 0.9(20) (23-25.6)² (18-16.6)²
<u> = 25.6 = 6.76 40.96 </u>
<u>∑50 83 83 127.3 281.2 </u>
Y~= ∑yi/n= 83/5= 16.6
SSE = ∑ (Yi-Y^)² = 127.3
SST= ∑(Yi-Y`)²=281.2
SST = SSR + SSE
SSR = SST- SSE
= 281.2- 127.3= 153.9
Co -efficient of determination= R² = SSR/ SST= 153.9/ 281.2= 0.5473
The regression equation is explained by 54.73 % of the total sum of squares.
The linear correlation coefficient is the square root of the co -efficient of determination
r= ±√r²= √0.5473= +0.7398
We only consider the positive value for the linear correlation coefficient to be positive.