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## I am trying to understand results from a regression analysis. Attached are the results. There is no page requirement. I just don’t understand fully how to read the results and need to add them to a pr

Regression Statistics R 0.95218 R-square 0.90664 Adjusted R0.87997 S 519,195.72 N 10 Enrollment = 1,255,008.97414 – 1,021.30038 * Tuition Costs -…

I am trying to understand results from a regression analysis. Attached are the results. There is no page requirement. I just don’t understand fully how to read the results and need to add them to a project for class. 1. What are the results of your regression analysis?  2. What are the implications of the t-stats, F test and Adjusted R2?  Are they consistent or contradictory?  If they seem to be contradictory, how can this be resolved? 3. What are key take-a-ways that can be applied in your own personal or professional real-world settings?  Provide concrete or hypothetical examples (if you are not in an applicable field) that support your conclusions.  How might this model change in the future given assumptions?

ATTACHMENT PREVIEW

Regression StaTsTcs
R
0.95218
R-square
0.90664
0.87997
S
519,195.72
N
10
Enrollment =
1,255,008.97414 – 1,021.30038 * ±uiTon Costs – 8.86787 * Financial Aid
ANOVA
d.f.
SS
MS
F
p-level
Regression
2
1.83E+13
9.16E+12
33.98956
0.00025
Residual
7
1.89E+12
2.70E+11
Total
9
2.00E+13
Coe±cient
Standard Error
LCL
UCL
t Stat
p-level
H0 (5%)
H0 (1%)
Intercept
1,255,008.97
2,976,291.61
-5,782,802.34
8,292,820.29
0.42167
0.68591 accepted
accepted
Tui±on Cos
-1,021.30
223.70142
-1,550.27
-492.33058
-4.56546
0.00259 rejected
rejected
Financial A
-8.86787
23.52699
-64.50037
46.76463
-0.37692
0.7174 accepted
accepted
T (5%)
2.36462
T (1%)
3.49948
LCL – Lower value of a reliable interval (LCL)
UCL – Upper value of a reliable interval (UCL)
Residuals
ObservaTon Enrollment
Predicted Y
Residual
Standardized StudenTzed Deleted t
Leverage
Cook’s D
DFI²
PRESS
1
20,427,711
19,597,628.99
830,082.01
1.59878
1.9751
2.74824
0.34476
0.68417
1.99347
1,266,830.92
2
19,102,814
19,047,144.96
55,669.04
0.10722
0.14043
0.13019
0.417
0.0047
0.11011
95,487.15
3
18,248,128
18,843,529.45
-595,401.45
-1.14678
-1.36812
-1.47984
0.2974
0.2641
-0.96279
-847,425.92
4
17,758,870
18,119,226.01
-360,356.01
-0.69407
-1.39044
-1.5131
0.75083
1.94192
-2.62658
-1,446,226.39
5
17,487,475
17,914,409.36
-426,934.36
-0.8223
-0.87283
-0.85601
0.11244
0.03217
-0.30468
-481,021.29
6
17,272,044
17,414,418.77
-142,374.77
-0.27422
-0.28924
-0.2694
0.10113
0.00314
-0.09036
-158,393.46
7
16,911,481
16,580,495.97
330,985.03
0.6375
0.69861
0.67058
0.1673
0.03268
0.30057
397,482.53
8
16,611,711
16,141,078.64
470,632.36
0.90646
1.01483
1.01737
0.20217
0.08699
0.51213
589,889.16
9
15,927,987
15,723,048.38
204,938.62
0.39472
0.4725
0.4446
0.30212
0.03222
0.29253
293,660.37
10
15,312,289
15,679,529.47
-367,240.47
-0.70733
-0.84836
-0.82921
0.30485
0.10521
-0.54912
-528,289.96
Minimum
15,312,289
15,679,529.47
-595,401.45
-1.14678
-1.39044
-1.5131
0.10113
0.00314
-2.62658
-1,446,226.39
Maximum
20,427,711
19,597,628.99
830,082.01
1.59878
1.9751
2.74824
0.75083
1.94192
1.99347
1,266,830.92
Mean
17,506,051
17,506,051
5.59E-10
0
-0.04675
0.00634
0.3
0.31873
-0.13247
-81,800.69