Multiple Linear Regression - Estimated Regression Equation

Age[t] = + 10.5583940599721 -0.0234786166384589Student[t] + 0.108577819596049Height[t] -0.362944869999979`Self-Esteem`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	10.5583940599721	2.580241	4.092	0.000851	0.000425
Student	-0.0234786166384589	0.027965	-0.8396	0.413523	0.206762
Height	0.108577819596049	0.051981	2.0888	0.053062	0.026531
`Self-Esteem`	-0.362944869999979	0.533717	-0.68	0.506205	0.253103

Multiple Linear Regression - Regression Statistics
Multiple R	0.582860632485432
R-squared	0.339726516901317
Adjusted R-squared	0.215925238820315
F-TEST (value)	2.7441277034235
F-TEST (DF numerator)	3
F-TEST (DF denominator)	16
p-value	0.077286054360856
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.672216233808483
Sum Squared Residuals	7.22999463993058

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	16	16.4301332088651	-0.430133208865103
2	17	16.5509156160148	0.449084383985206
3	15	15.8405925190119	-0.840592519011875
4	17	17.0108586351221	-0.0108586351220682
5	17	15.5770909293507	1.42290907064925
6	16	15.8070624389044	0.192937561095615
7	16	16.2895671504383	-0.289567150438285
8	16	16.2657828923959	-0.265782892395882
9	17	16.4954487605456	0.504551239454425
10	17	16.472581426715	0.527418573284995
11	16	16.4131139644805	-0.413113964480493
12	16	16.389940989246	-0.389940989245978
13	15	15.7506786592233	-0.750678659223333
14	16	15.7638001709888	0.236199829011183
15	15	15.4868714281583	-0.486871428158261
16	15	15.571359348308	-0.571359348307963
17	16	15.7287418838616	0.271258116138396
18	17	16.0313023674152	0.968697632584762
19	15	15.7186904203926	-0.718690420392573
20	16	15.405467190562	0.59453280943798