Multiple Linear Regression - Estimated Regression Equation
totaal[t] = + 28077.1236605879 -21314442.052228jaar[t] -13.1476462703488pop[t] + 0.836477807566883vlaams_man[t] + 0.0471501485953764man_vlaams_pop[t] + 1.13238757187277vlaams_vrouw[t] -0.0449098556338475vrouw_vlaams_pop[t] + 1.11206792400799waals_man[t] -0.0337743960440693man_waals_pop[t] + 0.92867218471356waals_vrouw[t] + 0.0395007801556062vrouw_waals_pop[t] + 0.951118040980139brussel_man[t] -0.0226033357346419man_brussel_pop[t] + 1.10169157043926brussel_vrouw[t] + 0.0138893774853077vrouw_brussel_pop[t] -13.8206845223669t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)28077.123660587917923.1498221.56650.1264860.063243
jaar-21314442.05222813605470.329206-1.56660.1264670.063234
pop-13.147646270348829.76534-0.44170.6614960.330748
vlaams_man0.8364778075668830.1061317.881600
man_vlaams_pop0.04715014859537640.0391471.20440.2367350.118367
vlaams_vrouw1.132387571872770.1073110.552500
vrouw_vlaams_pop-0.04490985563384750.036031-1.24640.2211260.110563
waals_man1.112067924007990.08552713.002600
man_waals_pop-0.03377439604406930.047276-0.71440.4798480.239924
waals_vrouw0.928672184713560.1072328.660400
vrouw_waals_pop0.03950078015560620.0662860.59590.5551760.277588
brussel_man0.9511180409801390.1182368.044200
man_brussel_pop-0.02260333573464190.075357-0.30.766040.38302
brussel_vrouw1.101691570439260.1116259.869600
vrouw_brussel_pop0.01388937748530770.0677210.20510.838720.41936
t-13.82068452236698.781073-1.57390.1247670.062384


Multiple Linear Regression - Regression Statistics
Multiple R0.999999127416479
R-squared0.999998254833719
Adjusted R-squared0.999997484907419
F-TEST (value)1298823.34762231
F-TEST (DF numerator)15
F-TEST (DF denominator)34
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.625454159930041
Sum Squared Residuals13.300558809909


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
191909190.03166215015-0.031662150147516
292519250.72597219090.274027809101506
393289328.3339453377-0.333945337697938
494289428.84450862113-0.844508621128191
594999498.89629435840.103705641601395
695569555.855858730620.144141269378778
796069604.781732829111.21826717088526
896329631.242857535860.757142464144093
996609660.24842602742-0.248426027417757
1096519650.888191824310.111808175694669
1196959695.93415407212-0.934154072116918
1297279727.25016631417-0.250166314165801
1397579757.1816671442-0.181667144196003
1497889788.10043647916-0.100436479158595
1598139812.122129749360.877870250641633
1698239822.682704783850.317295216152959
1798379836.902525758020.0974742419762931
1898429842.24991108841-0.249911088412445
1998559856.01210009834-1.01210009833895
2098639864.22528281931-1.22528281931034
2198559854.34884137530.651158624696052
2298589858.09679191355-0.0967919135492537
2398539853.22374877865-0.223748778650978
2498589857.351708782430.648291217567291
2598599858.613717005630.386282994368908
2698659864.335668222410.664331777592909
2798769876.15155822476-0.15155822475686
2899289927.70177365820.298226341803655
2999489948.51274419602-0.512744196018935
3099879988.08478798247-1.08478798247262
311002210022.0338159539-0.0338159539116831
321006810067.94011482460.0598851753513681
331010110100.85369623770.146303762256546
341013110130.71803060350.281969396543092
351014310142.74237241680.257627583160653
361017010169.48327667030.516723329680716
371019210191.8334785260.16652147397086
381021410213.5623908510.437609149046182
391023910239.3015960772-0.301596077247167
401026310263.1713153628-0.17131536280786
411031010309.91995786910.0800421309329473
421035510355.1961176177-0.196117617745985
431039610396.491422587-0.491422587005978
441044610446.0635656821-0.0635656820742312
451051110510.7303955450.269604455042736
461058510584.44998181470.550018185337703
471066710666.98386792640.0161320735950905
481075310753.6265638849-0.626563884893406
491084010840.6560847692-0.656084769217903
501095110950.31008672810.689913271920019


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.2814684976018110.5629369952036220.718531502398189
200.3065884978928810.6131769957857610.693411502107119
210.1766513348121350.353302669624270.823348665187865
220.176425847463290.3528516949265790.82357415253671
230.4427707467245120.8855414934490250.557229253275488
240.5894891539931810.8210216920136380.410510846006819
250.8285228372540480.3429543254919040.171477162745952
260.9509780747330150.0980438505339710.0490219252669855
270.9955756790369130.008848641926174320.00442432096308716
280.9971164716329630.005767056734073820.00288352836703691
290.9916005011577010.01679899768459810.00839949884229906
300.983421457940880.03315708411823910.0165785420591196
310.9981109648894650.003778070221070240.00188903511053512


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.230769230769231NOK
5% type I error level50.384615384615385NOK
10% type I error level60.461538461538462NOK