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predicaot2
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| > m4 <- lm(geo$preco ~ geo$area + geo$coef_ap + geo$acl_decl + geo$frente, data=geo) | |
| > | |
| > summary(m4) | |
| Call: | |
| lm(formula = geo$preco ~ geo$area + geo$coef_ap + geo$acl_decl + | |
| geo$frente, data = geo) | |
| Residuals: | |
| Min 1Q Median 3Q Max | |
| -15525 -5235 -1858 3628 52939 | |
| Coefficients: | |
| Estimate Std. Error t value Pr(>|t|) | |
| (Intercept) -41771.930 16298.496 -2.563 0.013501 * | |
| geo$area 17.276 6.424 2.689 0.009761 ** | |
| geo$coef_ap 8195.227 2238.198 3.662 0.000614 *** | |
| geo$acl_decl 55767.600 18004.224 3.097 0.003227 ** | |
| geo$frente 221.517 94.485 2.344 0.023152 * | |
| --- | |
| Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |
| Residual standard error: 11020 on 49 degrees of freedom | |
| Multiple R-squared: 0.5867, Adjusted R-squared: 0.5529 | |
| F-statistic: 17.39 on 4 and 49 DF, p-value: 6.111e-09 | |
| > summary(m4) | |
| Call: | |
| lm(formula = geo$preco ~ geo$area + geo$coef_ap + geo$acl_decl + | |
| geo$frente, data = geo) | |
| Residuals: | |
| Min 1Q Median 3Q Max | |
| -15525 -5235 -1858 3628 52939 | |
| Coefficients: | |
| Estimate Std. Error t value Pr(>|t|) | |
| (Intercept) -41771.930 16298.496 -2.563 0.013501 * | |
| geo$area 17.276 6.424 2.689 0.009761 ** | |
| geo$coef_ap 8195.227 2238.198 3.662 0.000614 *** | |
| geo$acl_decl 55767.600 18004.224 3.097 0.003227 ** | |
| geo$frente 221.517 94.485 2.344 0.023152 * | |
| --- | |
| Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |
| Residual standard error: 11020 on 49 degrees of freedom | |
| Multiple R-squared: 0.5867, Adjusted R-squared: 0.5529 | |
| F-statistic: 17.39 on 4 and 49 DF, p-value: 6.111e-09 | |
| > | |
| > summary(m4) | |
| Call: | |
| lm(formula = geo$preco ~ geo$area + geo$coef_ap + geo$acl_decl + | |
| geo$frente, data = geo) | |
| Residuals: | |
| Min 1Q Median 3Q Max | |
| -15525 -5235 -1858 3628 52939 | |
| Coefficients: | |
| Estimate Std. Error t value Pr(>|t|) | |
| (Intercept) -41771.930 16298.496 -2.563 0.013501 * | |
| geo$area 17.276 6.424 2.689 0.009761 ** | |
| geo$coef_ap 8195.227 2238.198 3.662 0.000614 *** | |
| geo$acl_decl 55767.600 18004.224 3.097 0.003227 ** | |
| geo$frente 221.517 94.485 2.344 0.023152 * | |
| --- | |
| Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |
| Residual standard error: 11020 on 49 degrees of freedom | |
| Multiple R-squared: 0.5867, Adjusted R-squared: 0.5529 | |
| F-statistic: 17.39 on 4 and 49 DF, p-value: 6.111e-09 | |
| > | |
| > residuos <- rstandard(m4) | |
| > | |
| > preditos <- fitted(m4) | |
| > | |
| > plot(residuos ~ preditos) | |
| > | |
| > qqnorm(residuos) | |
| > | |
| > qqline(residuos) | |
| > | |
| > shapiro.test(residuos) | |
| Shapiro-Wilk normality test | |
| data: residuos | |
| W = 0.82459, p-value = 1.653e-06 | |
| > | |
| > |
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| > coefap <- geo$coef_ap^2 | |
| > frente <- geo$frente^3 | |
| > acldecl <- 1/geo$acl_decl | |
| > m4 <- lm(log(geo$preco) ~ log(geo$area) + coefap + acldecl + frente, data=geo) | |
| > | |
| > summary(m4) | |
| Call: | |
| lm(formula = log(geo$preco) ~ log(geo$area) + coefap + acldecl + | |
| frente, data = geo) | |
| Residuals: | |
| Min 1Q Median 3Q Max | |
| -0.30260 -0.14413 -0.03326 0.09578 0.64391 | |
| Coefficients: | |
| Estimate Std. Error t value Pr(>|t|) | |
| (Intercept) 10.546673 0.660423 15.970 < 2e-16 *** | |
| log(geo$area) 0.227991 0.098045 2.325 0.0242 * | |
| coefap 0.041921 0.009218 4.547 3.58e-05 *** | |
| acldecl -1.534453 0.258429 -5.938 2.92e-07 *** | |
| frente 0.003400 0.001822 1.867 0.0679 . | |
| --- | |
| Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |
| Residual standard error: 0.2031 on 49 degrees of freedom | |
| Multiple R-squared: 0.709, Adjusted R-squared: 0.6852 | |
| F-statistic: 29.84 on 4 and 49 DF, p-value: 1.35e-12 | |
| > | |
| > residuos <- rstandard(m4) | |
| > | |
| > preditos <- fitted(m4) | |
| > | |
| > plot(residuos ~ preditos) | |
| > | |
| > qqnorm(residuos) | |
| > | |
| > qqline(residuos) | |
| > | |
| > shapiro.test(residuos) | |
| Shapiro-Wilk normality test | |
| data: residuos | |
| W = 0.9426, p-value = 0.01198 | |
| > | |
| > |
MODELO 2 TRANSFORMANDO AS VARIÁVEIS
Residuos vs Preditos (Sem Outliers)
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MODELO 1 SEM TRANSFORMAR VARIÁVEIS