Multiple Linear Regression - Estimated Regression Equation
Placebo[t] = -0.905429 + 0.710485Slaappil[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-0.9054 0.5758-1.5720e+00 0.1545 0.07726
Slaappil+0.7105 0.1916+3.7090e+00 0.005965 0.002982


Multiple Linear Regression - Regression Statistics
Multiple R 0.7952
R-squared 0.6323
Adjusted R-squared 0.5863
F-TEST (value) 13.76
F-TEST (DF numerator)1
F-TEST (DF denominator)8
p-value 0.005965
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.151
Sum Squared Residuals 10.59


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 0.7 0.4445 0.2555
2-1.6-0.337-1.263
3-0.2-0.1239-0.0761
4-1.2-0.8344-0.3656
5-0.1-0.9765 0.8765
6 3.4 2.221 1.179
7 3.7 3.002 0.6978
8 0.8 0.2313 0.5687
9 0 2.363-2.363
10 2 1.51 0.4898


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.0054112, df1 = 2, df2 = 6, p-value = 0.9946
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.0054112, df1 = 2, df2 = 6, p-value = 0.9946
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.0054112, df1 = 2, df2 = 6, p-value = 0.9946