Multiple Linear Regression - Estimated Regression Equation

TV[t] = + 329.981276186481 -15.8493756170929Prijs[t] + 54.0911020959416Adv[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)	329.981276186481	134.666799	2.4504	0.044084	0.022042
Prijs	-15.8493756170929	13.949998	-1.1362	0.293287	0.146644
Adv	54.0911020959416	33.916156	1.5948	0.154776	0.077388

Multiple Linear Regression - Regression Statistics
Multiple R	0.558846254667868
R-squared	0.312309136356303
Adjusted R-squared	0.115826032458104
F-TEST (value)	1.58949614577605
F-TEST (DF numerator)	2
F-TEST (DF denominator)	7
p-value	0.269697170969160
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	50.7726139089211
Sum Squared Residuals	18045.0082620106

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	350	421.310347209077	-71.3103472090773
2	460	389.611595974892	70.3884040251083
3	350	365.459577537563	-15.4595775375627
4	430	446.596230681475	-16.5962306814752
5	350	384.478828278074	-34.4788282780742
6	380	427.475367442051	-47.4753674420508
7	430	420.932392197388	9.06760780261209
8	470	428.682349992070	41.3176500079295
9	450	408.354504202626	41.6454957973735
10	490	467.098806484783	22.9011935152169