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Regresión Lineal Simple PlantaCapVol Rosario25567.65 Coyoacán4005676.48 Acueducto de Guadalupe871923.7 San Juan de Aragón5004446.58 Ciudad Deportiva2304099.68.

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Presentation on theme: "Regresión Lineal Simple PlantaCapVol Rosario25567.65 Coyoacán4005676.48 Acueducto de Guadalupe871923.7 San Juan de Aragón5004446.58 Ciudad Deportiva2304099.68."— Presentation transcript:

1 Regresión Lineal Simple PlantaCapVol Rosario Coyoacán Acueducto de Guadalupe San Juan de Aragón Ciudad Deportiva Iztacalco Cerro de la Estrella San Pedro Atocpan San Juan Ixtayopan San Andrés Mixquic Abasolo Heroico Colegio Militar Parres PEMEX Xicalco794.6 Reclusorio Sur San Luis Tlaxialtemalco Tlatelolco Bosque de las Lomas Campo Militar No Chapultepec RS1. Plantas de Tratamiento de agua, DF, 1998

2 anova(modelo) Analysis of Variance Table Response: Vol Df Sum Sq Mean Sq F value Pr(>F) Cap < 2.2e-16 *** Residuals Total Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 summary(modelo) Call: lm(formula = Vol ~ Cap) Residuals: Min1Q Median3QMax Coefficients: EstimateStd. Errort valuePr(>|t|) (Intercept) Cap <2e-16 *** --- Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: on 19 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: 6676 on 1 and 19 DF, p-value: < 2.2e-16

3 shapiro.test(modelo$residual) Shapiro-Wilk normality test data: modelo$residual W = , p-value =

4 Transformación de variable y<-sqrt(Vol) m2<-lm(y~Cap) Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) Cap e-11 *** Residuals Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Shapiro-Wilk normality test data: m2$residuals W = , p-value =

5 RS2. Modelo de Pinzones summary(m) Call: lm(formula = beak.length ~ mass, data = KenyaFinches) Residuals: Min1QMedian3QMax Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) <2e-16 *** mass <2e-16 *** --- Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: on 43 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: 574 on 1 and 43 DF, p-value: Schluter, D The evolution of finch communities on islands and continents: Kenya vs. Galapagos. Ecological Monographs 58:

6 anova(m) Analysis of Variance Table Response: beak.length DfSum SqMean SqFvalue Pr(>F) Mass < 2.2e-16 *** Residuals Total Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 shapiro.test(m$residuals) Shapiro-Wilk normality test data: m$residuals W = , p-value =

7 RS3. Reforestación en el DF m<-lm(Reforestacion~Superfice) summary(m) Call: lm(formula = Reforestacion ~ Superfice) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) *** Superfice Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: on 14 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 1 and 14 DF, p-value:

8 anova(m) Analysis of Variance Table Response: Reforestacion Df Sum SqMean SqF valuePr(>F) Superfice Residuals Total

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10 RS4. Dióxido de Carbono por uso vehicular anioco2uso Año base 1970=100 Redfern, A., Bunyan, M., and Lawrence, T. (eds) (2003). The Environment in Your Pocket, 7 th edn. London: UK Department for Environment, Food and Rural Affairs.

11 Call: lm(formula = co2 ~ uso) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) e-05 *** uso < 2e-16 *** --- Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: on 26 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 1 and 26 DF, p-value: < 2.2e-16 Analysis of Variance Table Response: co2 DfSum SqMean SqFvalue Pr(>F) Uso < 2.2e-16 *** Residuals Total Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

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