Multiple Linear Regression - Estimated Regression Equation |
werklh[t] = + 12.1281080048809 -0.0347898199866124ecogr[t] + 0.329371318768953M1[t] + 0.73661868884898M2[t] + 0.462018759129809M3[t] + 0.0654597778960518M4[t] -0.0300638017227109M5[t] + 0.287164807353566M6[t] + 0.402662218415118M7[t] + 0.61838748297653M8[t] + 0.232961244138489M9[t] -0.0169462063747899M10[t] + 0.233390782581000M11[t] -0.0295748486792233t + e[t] |
Multiple Linear Regression - Ordinary Least Squares | |||||
Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
(Intercept) | 12.1281080048809 | 0.858555 | 14.1262 | 0 | 0 |
ecogr | -0.0347898199866124 | 0.009502 | -3.6613 | 0.000646 | 0.000323 |
M1 | 0.329371318768953 | 0.291257 | 1.1309 | 0.263975 | 0.131988 |
M2 | 0.73661868884898 | 0.369783 | 1.992 | 0.052323 | 0.026162 |
M3 | 0.462018759129809 | 0.368707 | 1.2531 | 0.216509 | 0.108255 |
M4 | 0.0654597778960518 | 0.328953 | 0.199 | 0.843144 | 0.421572 |
M5 | -0.0300638017227109 | 0.292363 | -0.1028 | 0.918545 | 0.459272 |
M6 | 0.287164807353566 | 0.292981 | 0.9801 | 0.33214 | 0.16607 |
M7 | 0.402662218415118 | 0.298202 | 1.3503 | 0.183526 | 0.091763 |
M8 | 0.61838748297653 | 0.343869 | 1.7983 | 0.07869 | 0.039345 |
M9 | 0.232961244138489 | 0.309896 | 0.7517 | 0.456037 | 0.228018 |
M10 | -0.0169462063747899 | 0.307097 | -0.0552 | 0.956232 | 0.478116 |
M11 | 0.233390782581000 | 0.35896 | 0.6502 | 0.518806 | 0.259403 |
t | -0.0295748486792233 | 0.003198 | -9.2489 | 0 | 0 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.851639257562177 |
R-squared | 0.725289425021056 |
Adjusted R-squared | 0.647653827744398 |
F-TEST (value) | 9.34222766956312 |
F-TEST (DF numerator) | 13 |
F-TEST (DF denominator) | 46 |
p-value | 5.09720377017686e-09 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 0.420311541905972 |
Sum Squared Residuals | 8.1264424439313 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 9.3 | 9.0602499002666 | 0.239750099733406 |
2 | 9.3 | 8.83605853589895 | 0.463941464101053 |
3 | 8.7 | 8.66408507344968 | 0.0359149265503157 |
4 | 8.2 | 8.46060609145102 | -0.260606091451024 |
5 | 8.3 | 8.41552424912224 | -0.115524249122243 |
6 | 8.5 | 8.94670674942559 | -0.446706749425586 |
7 | 8.6 | 8.98740254582532 | -0.387402545825318 |
8 | 8.5 | 8.86740254582532 | -0.367402545825317 |
9 | 8.2 | 8.51850211628262 | -0.318502116282617 |
10 | 8.1 | 8.40253197102719 | -0.302531971027192 |
11 | 7.9 | 8.07361495551528 | -0.173614955515282 |
12 | 8.6 | 8.95871338381327 | -0.358713383813269 |
13 | 8.7 | 8.74362051810114 | -0.0436205181011353 |
14 | 8.7 | 8.51247118973622 | 0.187528810263777 |
15 | 8.5 | 8.4622620972401 | 0.0377379027599024 |
16 | 8.4 | 7.93523778936594 | 0.464762210634059 |
17 | 8.5 | 8.03627319098094 | 0.463726809019064 |
18 | 8.7 | 8.43873335733381 | 0.261266642666188 |
19 | 8.7 | 8.4655132257389 | 0.234486774261101 |
20 | 8.6 | 8.13677430581923 | 0.463225694180775 |
21 | 8.5 | 8.23666255410382 | 0.263337445896177 |
22 | 8.3 | 7.63711391103449 | 0.662886088965515 |
23 | 8 | 7.69436389737397 | 0.305636102626026 |
24 | 8.2 | 8.44726100972283 | -0.247261009722834 |
25 | 8.1 | 8.22173119801472 | -0.121731198014716 |
26 | 8.1 | 8.10190929360696 | -0.00190929360696137 |
27 | 8 | 7.73859182123133 | 0.261408178768673 |
28 | 7.9 | 7.43770134327015 | 0.46229865672985 |
29 | 7.9 | 7.69181195282624 | 0.208188047173760 |
30 | 8 | 7.84030643327684 | 0.159693566723155 |
31 | 8 | 7.9505818696498 | 0.0494181303501983 |
32 | 7.9 | 7.66706971571272 | 0.232930284287277 |
33 | 8 | 7.69389934202544 | 0.306100657974564 |
34 | 7.7 | 7.30308961887577 | 0.396910381124226 |
35 | 7.2 | 7.2490121812581 | -0.0490121812581021 |
36 | 7.5 | 7.8453551036672 | -0.345355103667206 |
37 | 7.3 | 7.73811067991357 | -0.43811067991357 |
38 | 7 | 7.82702769542549 | -0.82702769542549 |
39 | 7 | 7.17843369915963 | -0.178433699159634 |
40 | 7 | 7.06888723112483 | -0.068887231124826 |
41 | 7.2 | 7.40649340864879 | -0.206493408648785 |
42 | 7.3 | 7.32189609518909 | -0.0218960951890868 |
43 | 7.1 | 7.33476003559953 | -0.234760035599529 |
44 | 6.8 | 7.62527991144156 | -0.825279911441556 |
45 | 6.4 | 6.9493551740247 | -0.549355174024697 |
46 | 6.1 | 6.95514939872242 | -0.855149398722417 |
47 | 6.5 | 6.93934076309002 | -0.439340763090019 |
48 | 7.7 | 7.36869254956338 | 0.331307450436618 |
49 | 7.9 | 7.53628770370398 | 0.363712296296015 |
50 | 7.5 | 7.32253328533238 | 0.177466714667623 |
51 | 6.9 | 7.05662730891926 | -0.156627308919257 |
52 | 6.6 | 7.19756754478806 | -0.597567544788059 |
53 | 6.9 | 7.2498971984218 | -0.349897198421796 |
54 | 7.7 | 7.65235736477467 | 0.0476426352253289 |
55 | 8 | 7.66174232318645 | 0.338257676813547 |
56 | 8 | 7.50347352120118 | 0.496526478798821 |
57 | 7.7 | 7.40158081356343 | 0.298419186436574 |
58 | 7.3 | 7.20211510034013 | 0.0978848996598677 |
59 | 7.4 | 7.04366820276262 | 0.356331797237377 |
60 | 8.1 | 7.47997795323331 | 0.62002204676669 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
17 | 0.232798535945623 | 0.465597071891245 | 0.767201464054377 |
18 | 0.111478298039398 | 0.222956596078795 | 0.888521701960602 |
19 | 0.0481424319208368 | 0.0962848638416736 | 0.951857568079163 |
20 | 0.0313777781440658 | 0.0627555562881316 | 0.968622221855934 |
21 | 0.037795812178882 | 0.075591624357764 | 0.962204187821118 |
22 | 0.0216106839965348 | 0.0432213679930696 | 0.978389316003465 |
23 | 0.0108445604795176 | 0.0216891209590352 | 0.989155439520482 |
24 | 0.0125327111846496 | 0.0250654223692992 | 0.98746728881535 |
25 | 0.0485042131712622 | 0.0970084263425244 | 0.951495786828738 |
26 | 0.0533574183426069 | 0.106714836685214 | 0.946642581657393 |
27 | 0.0408886555111882 | 0.0817773110223765 | 0.959111344488812 |
28 | 0.0347217940734133 | 0.0694435881468266 | 0.965278205926587 |
29 | 0.0233959379209708 | 0.0467918758419416 | 0.97660406207903 |
30 | 0.0163297218771717 | 0.0326594437543434 | 0.983670278122828 |
31 | 0.0100440241309746 | 0.0200880482619491 | 0.989955975869026 |
32 | 0.00938834833886733 | 0.0187766966777347 | 0.990611651661133 |
33 | 0.0078353114176211 | 0.0156706228352422 | 0.99216468858238 |
34 | 0.0353796307088895 | 0.070759261417779 | 0.96462036929111 |
35 | 0.0517568018732366 | 0.103513603746473 | 0.948243198126763 |
36 | 0.0449967297259013 | 0.0899934594518026 | 0.95500327027410 |
37 | 0.0641916907821616 | 0.128383381564323 | 0.935808309217838 |
38 | 0.115895538027253 | 0.231791076054506 | 0.884104461972747 |
39 | 0.119364851995711 | 0.238729703991422 | 0.880635148004289 |
40 | 0.274358599736169 | 0.548717199472339 | 0.72564140026383 |
41 | 0.532463513582859 | 0.935072972834282 | 0.467536486417141 |
42 | 0.61965055997295 | 0.760698880054101 | 0.380349440027050 |
43 | 0.487709817682328 | 0.975419635364656 | 0.512290182317672 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | |||
Description | # significant tests | % significant tests | OK/NOK |
1% type I error level | 0 | 0 | OK |
5% type I error level | 8 | 0.296296296296296 | NOK |
10% type I error level | 16 | 0.592592592592593 | NOK |