Multiple Linear Regression - Estimated Regression Equation
BCTsRes[t] = + 98.4748 -0.5105Cito[t] + 0.0795Lot[t] + 0.239076CD[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+98.47 0.6505+1.5140e+02 4.368e-52 2.184e-52
Cito-0.5105 0.2733-1.8680e+00 0.06991 0.03496
Lot+0.0795 0.2733+2.9090e-01 0.7728 0.3864
CD+0.2391 0.04757+5.0260e+00 1.389e-05 6.947e-06


Multiple Linear Regression - Regression Statistics
Multiple R 0.6669
R-squared 0.4447
Adjusted R-squared 0.3984
F-TEST (value) 9.61
F-TEST (DF numerator)3
F-TEST (DF denominator)36
p-value 8.473e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.8642
Sum Squared Residuals 26.89


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 98.88 98.28 0.5971
2 98.79 98.52 0.268
3 98.98 98.76 0.2189
4 98.52 99-0.4801
5 98.79 99.24-0.4492
6 100 99.48 0.5217
7 99.99 99.72 0.2726
8 100 99.96 0.04356
9 100 100.2-0.1955
10 99.99 100.4-0.4446
11 98.67 98.36 0.3076
12 97.32 98.6-1.281
13 99.52 98.84 0.6794
14 98.73 99.08-0.3496
15 99.8 99.32 0.4813
16 100 99.56 0.4422
17 100 99.8 0.2031
18 100 100-0.03594
19 100 100.3-0.275
20 99.99 100.5-0.5241
21 99 97.77 1.228
22 95.17 98.01-2.841
23 97.24 98.25-1.011
24 98.61 98.49 0.1204
25 98.66 98.73-0.06871
26 99.98 98.97 1.012
27 99.95 99.21 0.7431
28 99.81 99.45 0.3641
29 99.84 99.69 0.155
30 99.87 99.92-0.05409
31 99.26 97.85 1.408
32 99.27 98.09 1.179
33 96.67 98.33-1.66
34 97.87 98.57-0.6991
35 97.37 98.81-1.438
36 99.84 99.05 0.7927
37 99.88 99.29 0.5936
38 99.92 99.53 0.3946
39 99.81 99.76 0.04548
40 99.74 100-0.2636


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 0.2536 0.5071 0.7464
8 0.1237 0.2475 0.8763
9 0.05585 0.1117 0.9442
10 0.02599 0.05198 0.974
11 0.01012 0.02025 0.9899
12 0.03936 0.07872 0.9606
13 0.06425 0.1285 0.9358
14 0.03475 0.06949 0.9653
15 0.02916 0.05833 0.9708
16 0.0204 0.0408 0.9796
17 0.01095 0.02189 0.9891
18 0.005221 0.01044 0.9948
19 0.002447 0.004893 0.9976
20 0.001208 0.002416 0.9988
21 0.001043 0.002086 0.999
22 0.3041 0.6082 0.6959
23 0.3151 0.6302 0.6849
24 0.2791 0.5581 0.7209
25 0.2326 0.4653 0.7674
26 0.249 0.498 0.751
27 0.2067 0.4134 0.7933
28 0.1413 0.2827 0.8587
29 0.08672 0.1734 0.9133
30 0.04822 0.09644 0.9518
31 0.08524 0.1705 0.9148
32 0.2693 0.5386 0.7307
33 0.2824 0.5649 0.7176


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3 0.1111NOK
5% type I error level70.259259NOK
10% type I error level120.444444NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.9896, df1 = 2, df2 = 34, p-value = 0.06367
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6308, df1 = 6, df2 = 30, p-value = 0.1731
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 5.5448, df1 = 2, df2 = 34, p-value = 0.008239


Variance Inflation Factors (Multicollinearity)
> vif
Cito  Lot   CD 
   1    1    1