diagnostic-regression-panel · pygal · anyplot.ai

Residuals vs Fitted-16-16-12-12-8-8-4-4004488121250506060707080809090100100110110120120130130Residuals vs FittedFitted valuesResiduals93.96139167: -5.4721441781302.658979646884733.413566606108565.15177865: 0.1165367432614.3288651790856537.7220618115623103.4505231: 3.2739288021529.3768786390967427.163808072792277.66602871: 9.856075793913.3240363855751196.685405812904683.10232092: -7.0657585171043.209975407551789.2150683588334103.5026733: -3.3928822851530.622870078653660.606781882072183.53365328: -2.1692787621053.5155304145476617.7614632869679.75433374: -3.663162721963.2186059804876670.070837190859774.24428898: 8.918656225831.5705432984798229.509796463100278.01156076: 3.782850592921.5796180553857409.3435617271501353.31828593: -5.500794463331.5986043653462734.416776043869673.89112725: 4.580586972823.1326703044529381.4102739074048767.50450314: 14.80860385670.541043969605423.26923076923071762.68072377: 1.182102326555.28949220032500.410550336569747.0887047: 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Normal Q-Q-3-3-2-2-1-1001122-3-3-2-2-1-1001122Normal Q-QTheoretical QuantilesStandardized Residuals-2.638257273: -3.492926334343.85350894706961186.7307692307693-2.241402728: -2.318644256483.0473251390787969.332548414533-2.036834132: -2.315950787554.7982571444165968.8338987563102-1.893184535: -1.83436683605.1822978785553879.6768746448768-1.780464342: -1.535601124644.7180773882659824.3655196714665-1.686670816: -1.503018153677.615467288522818.3333404364798-1.605729606: -1.464811894706.0050020992614811.2601057317361-1.534120544: -1.325326201731.121354366198785.4367179836697-1.469612744: -1.314618777753.7469912755893783.4544217766364-1.410699178: -1.300157953774.4104916540441780.7772476390332-1.356311745: -1.272413738793.4864842234499775.640881321327-1.305664526: -1.259481933811.2506243844477773.2467790248879-1.258161561: -1.236823444827.9119402271785769.0519478732374-1.213339622: -1.157330783843.6329058661479754.3352426147903-1.170831119: 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Scale-Location0.20.20.40.40.60.60.80.8111.21.21.41.41.61.61.81.850506060707080809090100100110110120120130130Scale-LocationFitted values√|Standardized Residuals|93.96139167: 1.0142107751302.658979646884586.513031842962465.15177865: 0.14804942614.32886517908561157.2921577455103.4505231: 0.78675519781529.3768786390967736.40068798577177.66602871: 1.375645397913.3240363855751348.336465062734283.10232092: 1.1512281271043.209975407551496.22195098291103.5026733: 0.80503987371530.622870078653724.351534226231583.53365328: 0.64403051931053.5155304145476830.452756735014479.75433374: 0.8307934464963.2186059804876707.380560380328274.24428898: 1.293062739831.5705432984798402.7564144262455678.01156076: 0.8433477987921.5796180553857699.107549612108653.31828593: 1.020880042331.5986043653462582.118147881578173.89112725: 0.9266684412823.1326703044529644.201286167526767.50450314: 1.670790553670.5410439696054153.8430333716794462.68072377: 0.474941594555.28949220032941.87821856593647.0887047: 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Residuals vs Leverage-4-4-3-3-2-2-1-100112233440.010.010.020.020.030.030.040.040.050.050.060.060.070.070.080.080.090.090.10.10.110.110.120.120.130.130.140.140.150.150.160.16Residuals vs LeverageLeverageStandardized Residuals0.01513726089: -1.028623495138.2247465998372727.52341406390680.01627788216: 0.02191863077154.40973082822651602.38918517278740.0264606685: 0.6189837413298.899616685979531.27039768719870.05603564314: 1.892400258718.5573177458199379.58871841930440.01088784823: -1.32532620177.92718859315677762.86484723815240.04623464701: -0.6480891983579.4848906893878682.19646844865220.04812820117: -0.4147753098606.3537077874566654.4055281213740.0198022582: -0.6902177506204.41929352322362687.21456698378180.009865979278: 1.67201124863.42725477727822405.84012531711120.01556955021: 0.7112355095144.35876855872525520.28192459679520.03054979793: -1.04219606356.92281627638715729.14009597099660.009810539167: 0.858714399962.64058057965438502.715130488473050.02070460811: 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