4. Let’s compare the results you calculated for Q3b with results from a multiple linear regression.
4a. Would additionally controlling for ‘depth’ and ‘latitude’ be helpful? In other words, is a model that includes ‘depth’, ‘latitude’ and ‘longitude’ superior in model fit to a model that includes only ‘longitude’? Output for a multiple linear regression which includes longitude, depth, and latitude is provided below. (2 points)
4b. Interpret the parameter estimate for ‘longitude’ from the multiple linear regression output. (1 point)
|
Analysis of Variance |
|||||
|
Source |
DF |
Sum of |
Mean |
F Value |
Pr > F |
|
Model |
3 |
1.07870 |
0.35957 |
4.38 |
0.0159 |
|
Error |
20 |
1.64090 |
0.08204 |
||
|
Corrected Total |
23 |
2.71960 |
|||
|
Root MSE |
0.27953 |
R-Square |
0.3966 |
|
Dependent Mean |
2.98200 |
Adj R-Sq |
0.3104 |
|
Coeff Var |
9.37398 |
|
Parameter Estimates |
|||||
|
Variable |
DF |
Parameter |
Standard |
t Value |
Pr > |t| |
|
Intercept |
1 |
4.86602 |
0.85582 |
5.69 |
<.0001 |
|
Depth |
1 |
0.00131 |
0.01084 |
0.12 |
0.9049 |
|
Latitude |
1 |
0.00564 |
0.01108 |
0.51 |
0.6157 |
|
Longitude |
1 |
0.01849 |
0.00561 |
3.29 |
0.0035 |
PLEASE WITH FORMULA AND EXPLANATION
4. Let’s compare the results you calculated for Q3b with results from a multiple linear regression....
Consider the multiple linear regression given below. How many predictor variables currently look "significant" in the model? Multiple Regression Standard T Parameter Estimate Error Statistic P-Value CONSTANT 33113.2 9684.08 3.41934 0.0009 Latitude -269.803 191.056 -1.41216 0.1610 Longitude 29.9439 65.7703 0.455279 0.6499 AthleticRevenue 0.0001411 0.0000350024 4.03115 0.0001 Endowment 0.00173455 0.00106854 1.62329 0.1076 Analysis of Variance Source Sum of Squares Df Mean Square F-Ratio P-Value Model 3.52982E9 4 882,455,000 0.0000 Residual 9.8797E9 101 97,818,800 Total (Corr.) 1.34095E10 105 R-squared = 26.3232 percent
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Simple Linear Regression Problem
Simple Linear Regression
Problem
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