Multiple Linear Regression - Estimated Regression Equation
PSS[t] = + 10.2762770653839 -0.115368910871645G[t] -0.034037448000849T[t] + 0.0258113556187272`T-G`[t] + 1.21566788645044HPP[t] -0.233157283641214`HPP-G`[t] + 1.08065169306244TGYW[t] + 0.0279738297900176`TGYW-G`[t] -0.706210467475046POP[t] + 0.0201781082769364`POP-G`[t] -0.779717192161222IDT[t] + 0.112898678160323`IDT-G `[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)10.27627706538390.56907618.057800
G-0.1153689108716450.014626-7.888200
T-0.0340374480008490.012952-2.62790.0111610.00558
`T-G`0.02581135561872720.0234881.09890.2766820.138341
HPP1.215667886450440.1967426.17900
`HPP-G`-0.2331572836412140.217404-1.07250.2882830.144141
TGYW1.080651693062440.1836675.883700
`TGYW-G`0.02797382979001760.1931940.14480.8854110.442705
POP-0.7062104674750460.198308-3.56120.000780.00039
`POP-G`0.02017810827693640.1935120.10430.9173390.458669
IDT-0.7797171921612220.165482-4.71181.8e-059e-06
`IDT-G `0.1128986781603230.2438580.4630.6452470.322623


Multiple Linear Regression - Regression Statistics
Multiple R0.986956099413074
R-squared0.974082342168669
Adjusted R-squared0.968802819277101
F-TEST (value)184.501963941569
F-TEST (DF numerator)11
F-TEST (DF denominator)54
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.976960556198783
Sum Squared Residuals51.5404041318847


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
11414.1593430366491-0.159343036649138
21818.3435035220866-0.343503522086643
31111.4511820632625-0.451182063262535
41211.92026637871740.07973362128259
51616.2460060994724-0.246006099472436
61818.2073537300832-0.207353730083249
71414.6920035438345-0.69200354383447
81414.7314728205198-0.731472820519797
91515.1027113390928-0.102711339092772
101515.0686738910919-0.0686738910919228
111716.83788941741010.162110582589931
121918.78284623423940.217153765760621
131010.1584747400738-0.158474740073768
141615.71224129124980.287758708750251
151818.275457923663-0.275457923663007
161414.335282559448-0.335282559447990
171413.88524945821430.114750541785695
181716.58323646762260.41676353237738
191413.46541795499290.534582045007081
201615.94396729753390.0560327024661264
211816.99985384393541.00014615606460
221110.87797727593260.122022724067418
231414.0123881824329-0.0123881824328886
241211.89055407299330.109445927006671
251716.59660920401710.40339079598287
2699.12088363090492-0.120883630904922
271615.59764641644370.402353583556341
281413.97721713581660.0227828641833592
291514.7228968799770.277103120022986
301111.0395694627813-0.0395694627813440
311615.43453917613650.565460823863461
321313.2211544785248-0.221154478524814
331716.14618147229610.853818527703937
341514.25177513907150.748224860928453
351414.1789868041877-0.178986804187676
361615.52361158500460.476388414995437
3799.7360370177535-0.736037017753507
381514.39853387738790.601466122612143
391716.37790747858020.622092521419813
401313.1361318942706-0.136131894270630
411514.79644876389580.203551236104156
421615.06012724812720.9398727518728
431615.76250718194080.237492818059173
441212.4364090107412-0.436409010741242
451213.7035673272813-1.70356732728132
4634.71839414864835-1.71839414864835
4744.43275716005653-0.432757160056531
4844.52257170303608-0.522571703036082
4954.330771176407920.66922882359208
5046.15203190796087-2.15203190796087
5134.05404389076897-1.05404389076897
5236.29533950531307-3.29533950531307
5344.12645907299968-0.126459072999681
5433.92177050869039-0.921770508690391
5544.95151233823011-0.95151233823011
5643.394436962211730.605563037788271
5743.276905577168790.723094422831207
5833.24862398883875-0.248623988838746
5933.64719788223852-0.647197882238522
6033.21396560043436-0.213965600434361
6130.3132292053041182.68677079469588
6244.44045804769021-0.440458047690209
6342.982484225958531.01751577404147
6442.671035881747981.32896411825202
6542.461219721014171.53878027898583
6630.9426971655880962.05730283441190


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
153.27546150420365e-466.55092300840731e-461
168.96407902070025e-631.79281580414005e-621
171.26195156110071e-742.52390312220143e-741
185.28098335318425e-891.05619667063685e-881
191.44388085084654e-1022.88776170169308e-1021
203.53228177085652e-1217.06456354171305e-1211
213.53852669562443e-1387.07705339124886e-1381
229.99809141128964e-1481.99961828225793e-1471
231.18439747930645e-1632.36879495861291e-1631
243.04689969793533e-1806.09379939587067e-1801
251.26134831717725e-1992.5226966343545e-1991
263.41948288701283e-2066.83896577402565e-2061
272.85018361786788e-2245.70036723573576e-2241
288.97161354570427e-2381.79432270914085e-2371
293.42299848787061e-2536.84599697574122e-2531
303.19620151350113e-2616.39240302700226e-2611
314.50685558632148e-2909.01371117264295e-2901
329.852684624043e-2931.9705369248086e-2921
331.08199317552590e-3092.16398635105179e-3091
341.48219693752374e-3232.96439387504748e-3231
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
4514.79493237056467e-1292.39746618528234e-129
4616.50526225231077e-1183.25263112615539e-118
4714.95022766509705e-982.47511383254853e-98
4812.48072570741738e-871.24036285370869e-87
4911.2838600417397e-746.4193002086985e-75
5012.67871065317829e-581.33935532658914e-58
5114.92610463050927e-442.46305231525464e-44


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level371NOK
5% type I error level371NOK
10% type I error level371NOK