Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 3.90251060291830e-15 -2.60766319228936e-18BodyW[t] + 3.73591105584469e-19BrainW[t] -0.999999999999999PS[t] + 1TS[t] -6.5599409757104e-18LifeSpan[t] -1.18067716440613e-18GT[t] -1.80651147672450e-15PI[t] -1.41855482167557e-15SEI[t] + 3.09735631561724e-15ODI[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)3.90251060291830e-1500.95070.3463110.173156
BodyW-2.60766319228936e-180-0.35130.7268440.363422
BrainW3.73591105584469e-1900.08550.932240.46612
PS-0.9999999999999990-134546664360785900
TS10435239754646410800
LifeSpan-6.5599409757104e-180-0.11580.9082980.454149
GT-1.18067716440613e-180-0.12330.9023880.451194
PI-1.80651147672450e-150-1.23840.2213350.110668
SEI-1.41855482167557e-150-1.56280.1244140.062207
ODI3.09735631561724e-1501.46410.1494180.074709


Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)4.51833201861869e+30
F-TEST (DF numerator)9
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.48660196962828e-15
Sum Squared Residuals1.00647986169362e-27


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
16.36.299999999999972.82984272371989e-14
22.12.1-1.55057448124169e-15
39.19.1-6.74002398415465e-16
415.815.83.78604810682705e-15
55.25.2-8.71674959388824e-16
610.910.9-1.07496909021451e-15
78.38.39.1097574417062e-16
811112.612964737267e-15
93.23.2-1.3106652950192e-17
106.36.3-2.41050600199826e-15
118.68.63.59745374515703e-16
126.66.6-2.9170739510406e-15
139.59.52.85417236666445e-16
143.33.3-1.50251045196857e-16
151111-1.63236646445596e-15
164.74.71.93624121990118e-15
1710.410.4-1.83067179595234e-15
187.47.41.08509994120049e-15
192.12.1-9.89600837025628e-16
207.77.7-1.36165324727408e-15
2117.917.9-7.1910182012252e-16
226.16.1-1.11743333014729e-15
2311.911.9-5.04616756415198e-16
2410.810.8-1.26573067885998e-15
2513.813.82.1235389234113e-15
2614.314.33.23214756066576e-15
2715.215.2-1.36179893330729e-15
281010-6.80158153322428e-16
2911.911.9-3.97863046295061e-16
306.56.5-1.05719883892986e-15
317.57.5-8.1623669131203e-17
3210.610.6-3.48939743051969e-15
337.47.4-2.11976525850569e-15
348.48.41.58302997856186e-15
355.75.7-2.28555979761372e-16
364.94.9-2.22485258119647e-15
373.23.2-2.86132929834189e-16
381111-3.62311786501690e-15
394.94.9-3.64065293609689e-15
4013.213.29.11009610508859e-17
419.79.7-2.65600488809830e-15
4212.812.89.984541986002e-16
436.36.3-5.38475534204902e-15
442.12.11.67252468663414e-15
459.19.1-1.03060624012443e-15
4615.815.8-3.47248712996384e-16
475.25.21.55585115730031e-15
4810.910.9-1.07496909021452e-15
498.38.39.10975744170619e-16
5011112.61296473726701e-15
513.23.2-1.31066529501912e-17
526.36.3-2.41050600199826e-15
538.68.63.59745374515703e-16
546.66.6-2.9170739510406e-15
559.59.52.85417236666445e-16
563.33.3-1.50251045196857e-16
571111-1.63236646445596e-15
584.74.71.93624121990118e-15
5910.410.4-1.83067179595234e-15
607.47.41.08509994120049e-15


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.4174571306295110.8349142612590220.582542869370489
140.02356975169804910.04713950339609820.97643024830195
150.9398484498985360.1203031002029270.0601515501014637
167.38772163032634e-050.0001477544326065270.999926122783697
170.01657093135126180.03314186270252370.983429068648738
180.6862309275611710.6275381448776580.313769072438829
199.78609864952819e-061.95721972990564e-050.99999021390135
200.1326750642748200.2653501285496410.86732493572518
210.9999671054346476.57891307069267e-053.28945653534633e-05
220.05166806880680730.1033361376136150.948331931193193
230.4688524830026980.9377049660053950.531147516997302
244.39279151787076e-058.78558303574153e-050.99995607208482
250.9999988737528822.25249423583883e-061.12624711791941e-06
262.04884776505975e-084.0976955301195e-080.999999979511522
270.1493340973843730.2986681947687460.850665902615627
280.3735690269342740.7471380538685480.626430973065726
290.9985620698026460.002875860394708030.00143793019735401
300.585555661857280.8288886762854390.414444338142719
310.9999347501367830.0001304997264337026.52498632168508e-05
320.000392219867058920.000784439734117840.99960778013294
330.5352288570378420.9295422859243170.464771142962158
340.0002268273317951630.0004536546635903260.999773172668205
350.4889701917027520.9779403834055050.511029808297248
360.146552079021590.293104158043180.85344792097841
370.9480446567547580.1039106864904840.0519553432452419
380.794617064881140.4107658702377190.205382935118860
390.007220147514304220.01444029502860840.992779852485696
400.04500608303753990.09001216607507980.95499391696246
410.3888475852530400.7776951705060810.61115241474696
420.009140192727355610.01828038545471120.990859807272644
430.9529763581482240.09404728370355270.0470236418517763
440.7324237913520840.5351524172958320.267576208647916
450.5456316527141760.9087366945716480.454368347285824
460.2463897046793450.4927794093586910.753610295320654
470.2958928552783870.5917857105567740.704107144721613


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.285714285714286NOK
5% type I error level140.4NOK
10% type I error level160.457142857142857NOK