Multiple Linear Regression - Estimated Regression Equation
werkjarenhb[t] = + 4.67588 + 0.585209lto1[t] + 0.089438lto2[t] -0.639622lto3[t] -2.35412lto4[t] + 2.25776leeftijd[t] -0.0597828schooljaren[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+4.676 5.797+8.0660e-01 0.4222 0.2111
lto1+0.5852 1.024+5.7130e-01 0.5694 0.2847
lto2+0.08944 1.196+7.4790e-02 0.9406 0.4703
lto3-0.6396 0.9403-6.8030e-01 0.4983 0.2491
lto4-2.354 0.9857-2.3880e+00 0.01923 0.009613
leeftijd+2.258 0.4087+5.5240e+00 3.815e-07 1.907e-07
schooljaren-0.05978 0.203-2.9450e-01 0.7691 0.3846


Multiple Linear Regression - Regression Statistics
Multiple R 0.5821
R-squared 0.3389
Adjusted R-squared 0.2905
F-TEST (value) 7.006
F-TEST (DF numerator)6
F-TEST (DF denominator)82
p-value 4.866e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 8.259
Sum Squared Residuals 5593


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 2 2.879-0.8787
2 25 17.14 7.862
3 5 13.56-8.564
4 15 10.21 4.786
5 5 12.53-7.531
6 1 2.92-1.92
7 34 19.93 14.07
8 30 12.95 17.05
9 10 19.63-9.629
10 18 19.06-1.06
11 3 5.233-2.233
12 7 11.31-4.312
13 30 14.92 15.08
14 25 14.15 10.85
15 10 7.162 2.838
16 0.75 1.332-0.582
17 1-1.296 2.296
18 1 15.83-14.83
19 17 14.65 2.354
20 4 5.788-1.788
21 0 4.182-4.182
22 31 13.1 17.9
23 6 5.674 0.3257
24 0 2.228-2.228
25 8 12.38-4.375
26 36 18.88 17.12
27 20 16.66 3.336
28 13 17.05-4.054
29 12 18.06-6.061
30 1 1.206-0.206
31 4 8.016-4.016
32 27 14.45 12.55
33 20 14.19 5.809
34 6 7.266-1.266
35 1.5-2.421 3.921
36 2 16.96-14.96
37 13 11.66 1.34
38 1 17.25-16.25
39 7 6.08 0.9201
40 22 16.09 5.915
41 9 14.66-5.659
42 21 17.08 3.917
43 28 16.38 11.62
44 27 16.23 10.77
45 2 10.51-8.511
46 6 9.127-3.127
47 20 13.56 6.441
48 29 12.12 16.88
49 25 13.37 11.63
50 7 9.03-2.03
51 18 12.35 5.648
52 3 16.17-13.17
53 5 6.255-1.255
54 0.08333 7.825-7.742
55 4 4.39-0.3905
56 16 8.835 7.165
57 6 10.37-4.37
58 0.25 4.903-4.653
59 7 4.499 2.501
60 3 5.848-2.848
61 4 3.464 0.5363
62 2 5.876-3.876
63 4 2.818 1.182
64 6 8.238-2.238
65 10 6.469 3.531
66 1 13.9-12.9
67 1 3.715-2.715
68 6 1.606 4.394
69 4 13.11-9.111
70 0 15-15
71 0 3.757-3.757
72 4 7.776-3.776
73 4 6.153-2.153
74 3 5.758-2.758
75 8 5.758 2.242
76 12 4.598 7.402
77 12 15.49-3.489
78 1 9.683-8.683
79 17 14.68 2.317
80 27 17.26 9.743
81 0 22.97-22.97
82 3.7 5.364-1.664
83 4 7.608-3.608
84 12 7.861 4.139
85 2 2.271-0.2705
86 8 1.677 6.323
87 3 5.024-2.024
88 0.75 5.849-5.099
89 21 14.92 6.079


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
10 0.6049 0.7903 0.3951
11 0.4361 0.8722 0.5639
12 0.3699 0.7397 0.6301
13 0.5301 0.9398 0.4699
14 0.5573 0.8854 0.4427
15 0.5344 0.9311 0.4656
16 0.4503 0.9007 0.5497
17 0.351 0.702 0.649
18 0.3033 0.6066 0.6967
19 0.2616 0.5233 0.7384
20 0.193 0.3861 0.807
21 0.1652 0.3304 0.8348
22 0.7164 0.5671 0.2836
23 0.7301 0.5398 0.2699
24 0.7208 0.5584 0.2792
25 0.7616 0.4769 0.2384
26 0.8791 0.2418 0.1209
27 0.8433 0.3135 0.1567
28 0.8185 0.363 0.1815
29 0.8311 0.3379 0.1689
30 0.783 0.434 0.217
31 0.743 0.5141 0.257
32 0.7836 0.4328 0.2164
33 0.7696 0.4607 0.2304
34 0.7241 0.5519 0.2759
35 0.6921 0.6158 0.3079
36 0.8112 0.3777 0.1888
37 0.7664 0.4672 0.2336
38 0.8644 0.2712 0.1356
39 0.84 0.3201 0.16
40 0.8228 0.3543 0.1772
41 0.8105 0.3789 0.1895
42 0.7801 0.4397 0.2199
43 0.8429 0.3141 0.1571
44 0.8923 0.2153 0.1077
45 0.8876 0.2247 0.1124
46 0.8559 0.2881 0.1441
47 0.8432 0.3136 0.1568
48 0.9613 0.07731 0.03866
49 0.9871 0.02586 0.01293
50 0.9811 0.03789 0.01895
51 0.9856 0.02872 0.01436
52 0.9866 0.02673 0.01337
53 0.9805 0.0391 0.01955
54 0.9881 0.02378 0.01189
55 0.9816 0.03685 0.01843
56 0.9853 0.02939 0.01469
57 0.9786 0.04282 0.02141
58 0.9682 0.06358 0.03179
59 0.9555 0.08892 0.04446
60 0.9406 0.1188 0.0594
61 0.9165 0.167 0.08349
62 0.899 0.2021 0.101
63 0.8618 0.2764 0.1382
64 0.8218 0.3564 0.1782
65 0.7864 0.4272 0.2136
66 0.8331 0.3338 0.1669
67 0.8194 0.3612 0.1806
68 0.7608 0.4784 0.2392
69 0.729 0.542 0.271
70 0.8139 0.3721 0.1861
71 0.7773 0.4455 0.2227
72 0.7388 0.5223 0.2612
73 0.6745 0.6511 0.3255
74 0.5738 0.8523 0.4262
75 0.4858 0.9717 0.5142
76 0.3826 0.7653 0.6174
77 0.2792 0.5584 0.7208
78 0.3631 0.7262 0.6369
79 0.2258 0.4517 0.7742


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level90.128571NOK
10% type I error level120.171429NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 3.7937, df1 = 2, df2 = 80, p-value = 0.02666
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3905, df1 = 12, df2 = 70, p-value = 0.1913
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.052685, df1 = 2, df2 = 80, p-value = 0.9487


Variance Inflation Factors (Multicollinearity)
> vif
       lto1        lto2        lto3        lto4    leeftijd schooljaren 
   1.071307    1.123786    1.085548    1.051206    1.156189    1.065262