Multiple Linear Regression - Estimated Regression Equation
verkoop[t] = + 190.67 + 0.419366winkels[t] + 2.22225advertenties[t] + 0.592604influencers[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+190.7 42.49+4.4870e+00 0.0001531 7.657e-05
winkels+0.4194 0.157+2.6710e+00 0.01336 0.00668
advertenties+2.222 1.596+1.3920e+00 0.1766 0.0883
influencers+0.5926 0.1203+4.9250e+00 5.032e-05 2.516e-05


Multiple Linear Regression - Regression Statistics
Multiple R 0.8186
R-squared 0.6701
Adjusted R-squared 0.6289
F-TEST (value) 16.25
F-TEST (DF numerator)3
F-TEST (DF denominator)24
p-value 5.575e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 60.25
Sum Squared Residuals 8.712e+04


Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 236 257.9-21.86
2 244 266.1-22.15
3 252.9 276.7-23.85
4 263.1 291-27.94
5 290.8 297.6-6.789
6 300.7 309-8.294
7 331.6 392.4-60.78
8 338.8 392.8-53.99
9 361.8 417.2-55.34
10 369.9 419.3-49.45
11 378.7 422.2-43.5
12 387.9 425.3-37.33
13 470.2 424.5 45.73
14 482.7 428 54.71
15 499.8 442.7 57.1
16 506.1 429.4 76.7
17 431.5 405.5 25.95
18 441.2 406.5 34.67
19 448.7 406.8 41.96
20 468.7 436.8 31.95
21 434.3 454.1-19.81
22 443 522-79.02
23 451.3 519.3-68
24 486 587.7-101.8
25 536.7 506.1 30.61
26 540.4 476.5 63.95
27 550.5 454.8 95.74
28 560.8 440 120.8


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 0.00704 0.01408 0.993
8 0.001444 0.002889 0.9986
9 0.009 0.018 0.991
10 0.002903 0.005806 0.9971
11 0.001031 0.002061 0.999
12 0.0004688 0.0009376 0.9995
13 0.0001331 0.0002661 0.9999
14 5.231e-05 0.0001046 0.9999
15 0.02356 0.04713 0.9764
16 0.08033 0.1607 0.9197
17 0.2246 0.4492 0.7754
18 0.1604 0.3207 0.8396
19 0.1964 0.3927 0.8036
20 0.2295 0.459 0.7705
21 0.1375 0.275 0.8625


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level6 0.4NOK
5% type I error level90.6NOK
10% type I error level90.6NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 24.193, df1 = 2, df2 = 22, p-value = 2.781e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 16.631, df1 = 6, df2 = 18, p-value = 1.861e-06
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 12.252, df1 = 2, df2 = 22, p-value = 0.0002656


Variance Inflation Factors (Multicollinearity)
> vif
     winkels advertenties  influencers 
    1.713016     1.907646     1.234221