Create the fitted regression equation


Discuss the below:

In the following regression, X=total assets ($ billions), Y=total revenue ($ billions), and n=64 large banks.

a. Write the fitted regression equation.

b. State the degrees of freedom for a two-tailed test for zero slope, and use Appendix D (see attached data) to find the critical value at a=.05.

c. What is your conclusion about the slope?

d. Interpret the 95 percent confidence limits for the the slope.

e. Verify that F=t^2 for the slope.

f. Describe the fit of this regression.

R-squared 0.519




Std. Error 6.977




n 64











ANOVA table          
Source SS df MS F p-value
Regression  3260.0981 1 3260.0981 66.97 1.90E-11
Residual 3018.3339 62 48.6828    
Total 6278.432 63










Regression Output         Confidence Interval
variables coefficients std. error t(df=62) p-value 95% lower 95% upper
Intercept 6.5763 1.9254 3.416 0.0011 2.7275 10.4252
X1 0.0452 0.0055 8.183 1.90E-11 0.0342 0.0563


Confidence Level

0.8 0.9 0.95 0.98 0.99

Significance Level for Two-Tailed Test

0.2 0.1 0.05 0.02 0.01

Significance Level for One-Tailed Test
Degrees of Freedom (v) 0.1 0.05 0.025 0.01 0.005
1 3.078 6.314 12.706 31.821 63.656
2 1.886 2.92 4.303 6.965 9.925
3 1.638 2.353 3.182 4.541 5.841
4 1.533 2.132 2.776 3.747 4.604
5 1.476 2.015 2.571 3.365 4.032
6 1.44 1.943 2.447 3.143 3.707
7 1.415 1.895 2.365 2.998 3.499
8 1.397 1.86 2.306 2.896 3.355
9 1.383 1.833 2.262 2.821 3.25
10 1.372 1.812 2.228 2.764 3.169
11 1.363 1.796 2.201 2.718 3.106
12 1.356 1.782 2.179 2.681 3.055
13 1.35 1.771 2.16 2.65 3.012
14 1.345 1.761 2.145 2.624 2.977
15 1.341 1.753 2.131 2.602 2.947
16 1.337 1.746 2.12 2.583 2.921
17 1.333 1.74 2.11 2.567 2.898
18 1.33 1.734 2.101 2.552 2.878
19 1.328 1.729 2.093 2.539 2.861
20 1.325 1.725 2.086 2.528 2.845
21 1.323 1.721 2.08 2.518 2.831
22 1.321 1.717 2.074 2.508 2.819
23 1.319 1.714 2.069 2.5 2.807
24 1.318 1.711 2.064 2.492 2.797
25 1.316 1.708 2.06 2.485 2.787
26 1.315 1.706 2.056 2.479 2.779
27 1.314 1.703 2.052 2.473 2.771
28 1.313 1.701 2.048 2.467 2.763
29 1.311 1.699 2.045 2.462 2.756
30 1.31 1.697 2.042 2.457 2.75
31 1.309 1.696 2.04 2.453 2.744
32 1.309 1.694 2.037 2.449 2.738
33 1.308 1.692 2.035 2.445 2.733
34 1.307 1.691 2.032 2.441 2.728
35 1.306 1.69 2.03 2.438 2.724
36 1.306 1.688 2.028 2.434 2.719
37 1.305 1.687 2.026 2.431 2.715
38 1.304 1.686 2.024 2.429 2.712
39 1.304 1.685 2.023 2.426 2.708
40 1.303 1.684 2.021 2.423 2.704
41 1.303 1.683 2.02 2.421 2.701
42 1.302 1.682 2.018 2.418 2.698
43 1.302 1.681 2.017 2.416 2.695
44 1.301 1.68 2.015 2.414 2.692
45 1.301 1.679 2.014 2.412 2.69
46 1.3 1.679 2.013 2.41 2.687
47 1.3 1.678 2.012 2.408 2.685
48 1.299 1.677 2.011 2.407 2.682
49 1.299 1.677 2.01 2.405 2.68
50 1.299 1.676 2.009 2.403 2.678
55 1.297 1.673 2.004 2.396 2.668
60 1.296 1.671 2 2.39 2.66
65 1.295 1.669 1.997 2.385 2.654
70 1.294 1.667 1.994 2.381 2.648
75 1.293 1.665 1.992 2.377 2.643
80 1.292 1.664 1.99 2.374 2.639
85 1.292 1.663 1.988 2.371 2.635
90 1.291 1.662 1.987 2.368 2.632
95 1.291 1.661 1.985 2.366 2.629
100 1.29 1.66 1.984 2.364 2.626
110 1.289 1.659 1.982 2.361 2.621
120 1.289 1.658 1.98 2.358 2.617
130 1.288 1.657 1.978 2.355 2.614
140 1.288 1.656 1.977 2.353 2.611
150 1.287 1.655 1.976 2.351 2.609
1.282 1.645 1.96 2.326 2.576

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Applied Statistics: Create the fitted regression equation
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