Multiple Linear Regression - Estimated Regression Equation

Learning[t] = -25.0502944996332 + 0.835228820194156Connected[t] + 0.316316527346005Separate[t] + 0.30523886686134Software[t] + 0.0923332609152583Happiness[t] -0.252127439604043Depression[t] + 0.183095606876437Belonging[t] -0.298838505205408Belonging_Final[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)	-25.0502944996332	29.962779	-0.836	0.556698	0.278349
Connected	0.835228820194156	0.784156	1.0651	0.479929	0.239964
Separate	0.316316527346005	0.415828	0.7607	0.586001	0.293001
Software	0.30523886686134	0.545434	0.5596	0.675194	0.337597
Happiness	0.0923332609152583	0.525552	0.1757	0.889283	0.444642
Depression	-0.252127439604043	0.225474	-1.1182	0.464509	0.232254
Belonging	0.183095606876437	0.286651	0.6387	0.638133	0.319066
Belonging_Final	-0.298838505205408	0.482867	-0.6189	0.647192	0.323596

Multiple Linear Regression - Regression Statistics
Multiple R	0.982367301396426
R-squared	0.965045514852896
Adjusted R-squared	0.720364118823172
F-TEST (value)	3.9440902762205
F-TEST (DF numerator)	7
F-TEST (DF denominator)	1
p-value	0.36995359696414
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.182446364908
Sum Squared Residuals	1.39817940588414

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	16	16.4669512966246	-0.466951296624549
2	14	13.2035062182192	0.796493781780773
3	10	10.4627118993026	-0.462711899302641
4	17	17.1625678027355	-0.162567802735492
5	13	12.7585951085486	0.241404891451398
6	15	14.6200348558324	0.379965144167645
7	16	15.8976944451218	0.102305554878165
8	12	12.1978280740413	-0.197828074041278
9	13	13.230110299574	-0.23011029957402