Multiple Linear Regression - Estimated Regression Equation
cons[t] = + 0.197315 + 0.00330776income[t] -1.04441price[t] + 0.00345843temp[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+0.1973 0.2702+7.3020e-01 0.4718 0.2359
income+0.003308 0.001171+2.8240e+00 0.008989 0.004494
price-1.044 0.8344-1.2520e+00 0.2218 0.1109
temp+0.003458 0.0004456+7.7620e+00 3.1e-08 1.55e-08


Multiple Linear Regression - Regression Statistics
Multiple R 0.8479
R-squared 0.719
Adjusted R-squared 0.6866
F-TEST (value) 22.17
F-TEST (DF numerator)3
F-TEST (DF denominator)26
p-value 2.45e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.03683
Sum Squared Residuals 0.03527


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 0.386 0.3151 0.07088
2 0.374 0.3578 0.01622
3 0.393 0.3938-0.0008221
4 0.425 0.4047 0.02033
5 0.406 0.4033 0.002744
6 0.344 0.4065-0.06248
7 0.327 0.3923-0.0653
8 0.288 0.3423-0.05432
9 0.269 0.2826-0.0136
10 0.256 0.2523 0.003672
11 0.286 0.2709 0.01514
12 0.298 0.2864 0.0116
13 0.329 0.3084 0.02063
14 0.318 0.3105 0.00755
15 0.381 0.3761 0.004922
16 0.381 0.3867-0.005686
17 0.47 0.4185 0.05149
18 0.443 0.415 0.02798
19 0.386 0.4176-0.03158
20 0.342 0.4-0.05799
21 0.319 0.3257-0.006677
22 0.307 0.3237-0.01668
23 0.284 0.3296-0.04561
24 0.326 0.2973 0.02865
25 0.309 0.3139-0.004864
26 0.359 0.3522 0.006781
27 0.376 0.3733 0.00273
28 0.416 0.4179-0.001929
29 0.437 0.4398-0.002758
30 0.548 0.469 0.07899


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 0.6175 0.7651 0.3825
8 0.7796 0.4408 0.2204
9 0.8127 0.3745 0.1873
10 0.773 0.454 0.227
11 0.6615 0.6771 0.3385
12 0.7229 0.5543 0.2771
13 0.6636 0.6727 0.3364
14 0.5698 0.8603 0.4302
15 0.4578 0.9156 0.5422
16 0.347 0.694 0.653
17 0.4523 0.9046 0.5477
18 0.3911 0.7821 0.6089
19 0.316 0.6321 0.684
20 0.5439 0.9122 0.4561
21 0.4265 0.8531 0.5735
22 0.4157 0.8314 0.5843
23 0.5318 0.9365 0.4682


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.8233, df1 = 2, df2 = 24, p-value = 0.01735
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.4272, df1 = 6, df2 = 20, p-value = 0.2533
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.7184, df1 = 2, df2 = 24, p-value = 0.0392


Variance Inflation Factors (Multicollinearity)
> vif
  income    price     temp 
1.144186 1.035673 1.144367