{"id":101,"date":"2018-08-03T00:40:28","date_gmt":"2018-08-03T05:40:28","guid":{"rendered":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/?page_id=101"},"modified":"2018-08-28T13:53:31","modified_gmt":"2018-08-28T18:53:31","slug":"modelo-auto-con-r","status":"publish","type":"page","link":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/?page_id=101","title":{"rendered":"Modelo Auto con R"},"content":{"rendered":"

Autor:\u00a0 Guillermo Martinez Atilano<\/p>\n

El siguiente Modelo fue estimado con R.<\/p>\n

Utiliza la base de datos Auto.dta de Stata.<\/p>\n

2018-08-03 00:30:44<\/p>\n

> articulo <- readXL(“C:\/Users\/Atilano\/Desktop\/auto_es.xls”, rownames=FALSE, header=TRUE, na=””,
\n+\u00a0\u00a0 sheet=”Sheet1″, stringsAsFactors=TRUE)<\/p>\n

\"_MpgPeso\"<\/a><\/p>\n

La gr\u00e1fica de dispersi\u00f3n muestra una relaci\u00f3n negativa entre el peso de un autom\u00f3vil y las millas por gal\u00f3n. A memor peso aumenta el rendimiento y se obtienen mayor millaje.<\/p>\n

> RegModel.1 <- lm(mpg~peso, data=articulo)
\n> summary(RegModel.1)
\nCall:
\nlm(formula = mpg ~ peso, data = articulo)<\/p>\n

Residuals:
\nMin\u00a0\u00a0\u00a0\u00a0\u00a0 1Q\u00a0 Median\u00a0\u00a0\u00a0\u00a0\u00a0 3Q\u00a0\u00a0\u00a0\u00a0 Max
\n-6.9593 -1.9325 -0.3713\u00a0 0.8885 13.8174<\/p>\n

Coefficients:
\nEstimate Std. Error t value Pr(>|t|)
\n(Intercept) 39.4402835 \u00a01.6140031\u00a0\u00a0 24.44\u00a0\u00a0 <2e-16 ***
\npeso\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 -0.0060087\u00a0 0.0005179\u00a0 -11.60\u00a0\u00a0 <2e-16 ***
\n—
\nSignif. codes:\u00a0 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1<\/p>\n

Residual standard error: 3.439 on 72 degrees of freedom
\nMultiple R-squared:\u00a0 0.6515,\u00a0\u00a0\u00a0 Adjusted R-squared:\u00a0 0.6467
\nF-statistic: 134.6 on 1 and 72 DF,\u00a0 p-value: < 2.2e-16<\/p>\n

El resultado obtenido\u00a0<\/strong><\/p>\n

\"_MpgPeso_ajuster\"<\/a><\/p>\n

> stargazer(RegModel.1, header=FALSE, type = “text”, title=”Tabla 1. Modelos estimados”,
\n+ digits=2, single.row=FALSE, omit.stat=c(“LL”,”ser”,”f”))
\nTabla 1. Modelos estimados Millas por gal\u00f3n vs. peso del auto<\/p>\n

> articulo$mpg100 <- with(articulo, mpg\/100)<\/p>\n

> RegModel.2 <- lm(mpg100~peso, data=articulo)
\n> summary(RegModel.2)
\nCall:
\nlm(formula = mpg100 ~ peso, data = articulo)<\/p>\n

Residuals:
\nMin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1Q\u00a0\u00a0\u00a0 Median\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 3Q\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Max
\n-0.069593 -0.019325 -0.003713\u00a0 0.008885\u00a0 0.138174<\/p>\n

Coefficients:
\nEstimate\u00a0\u00a0 Std. Error t value Pr(>|t|)
\n(Intercept)\u00a0 0.394402835\u00a0 0.016140031\u00a0\u00a0 24.44\u00a0\u00a0 <2e-16 ***
\npeso\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 -0.000060087\u00a0 0.000005179\u00a0 -11.60\u00a0\u00a0 <2e-16 ***
\n—
\nSignif. codes:\u00a0 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1<\/p>\n

Residual standard error: 0.03439 on 72 degrees of freedom
\nMultiple R-squared:\u00a0 0.6515,\u00a0\u00a0\u00a0 Adjusted R-squared:\u00a0 0.6467
\nF-statistic: 134.6 on 1 and 72 DF,\u00a0 p-value: < 2.2e-16<\/p>\n

___________________________________________________________________________<\/p>\n

\"auto\"<\/a><\/p>\n

> articulo$mpg100 <- with(articulo, 100\/mpg)<\/p>\n

> RegModel.3 <- lm(mpg100~peso, data=articulo)
\n> summary(RegModel.3)
\nCall:
\nlm(formula = mpg100 ~ peso, data = articulo)<\/p>\n

Residuals:
\nMin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1Q\u00a0\u00a0 Median\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 3Q\u00a0\u00a0\u00a0\u00a0\u00a0 Max
\n-2.04508 -0.37487\u00a0 0.00488\u00a0 0.40990\u00a0 1.56000<\/p>\n

Coefficients:
\nEstimate Std. Error t value Pr(>|t|)
\n(Intercept) 0.7707669\u00a0 0.3142571\u00a0\u00a0 2.453\u00a0\u00a0 0.0166 *
\npeso\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.0014070\u00a0 0.0001008\u00a0 13.954\u00a0\u00a0 <2e-16 ***
\n—
\nSignif. codes:\u00a0 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1<\/p>\n

Residual standard error: 0.6696 on 72 degrees of freedom
\nMultiple R-squared:\u00a0\u00a0 0.73, Adjusted R-squared:\u00a0 0.7263
\nF-statistic: 194.7 on 1 and 72 DF,\u00a0 p-value: < 2.2e-16<\/p>\n

___________________________________________________________________________<\/p>\n

 <\/p>\n

> RegModel.4 <- lm(mpg100~peso+precio, data=articulo)
\n> summary(RegModel.4)
\nCall:
\nlm(formula = mpg100 ~ peso + precio, data = articulo)<\/p>\n

Residuals:
\nMin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1Q\u00a0\u00a0 Median\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 3Q\u00a0\u00a0\u00a0\u00a0\u00a0 Max
\n-2.55540 -0.27285\u00a0 0.02426\u00a0 0.40080\u00a0 1.32384<\/p>\n

Coefficients:
\nEstimate Std. Error t value Pr(>|t|)
\n(Intercept) 0.77124601 0.30497650\u00a0\u00a0 2.529\u00a0\u00a0 0.0137 *
\npeso\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.00126102 0.00011614 \u00a010.858\u00a0\u00a0 <2e-16 ***
\nprecio\u00a0\u00a0\u00a0\u00a0\u00a0 0.00007144 0.00003060\u00a0\u00a0 2.334\u00a0\u00a0 0.0224 *
\n—
\nSignif. codes:\u00a0 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1<\/p>\n

Residual standard error: 0.6498 on 71 degrees of freedom
\nMultiple R-squared:\u00a0 0.7493,\u00a0\u00a0\u00a0 Adjusted R-squared:\u00a0 0.7422
\nF-statistic: 106.1 on 2 and 71 DF,\u00a0 p-value: < 2.2e-16<\/p>\n

> stargazer(RegModel.1, header=FALSE, type = “text”,
\n+\u00a0\u00a0 title=”Tabla 1. Modelos estimados”,
\n+ digits=2, single.row=FALSE, omit.stat=c(“LL”,”ser”,”f”))<\/p>\n

Tabla 1. Modelos estimados
\n========================================
\nVariable dependiente :\u00a0 mpg
\n—————————————-
\npeso\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 -0.01***
\n(0.001)<\/p>\n

Constante\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a039.44***
\n(1.61)<\/p>\n

—————————————-
\nObservationes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a074
\nR2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.65
\nAdjusted R2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.65
\n========================================
\nNote:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 *p<0.1; **p<0.05; ***p<0.01<\/p>\n

___________________________________________________________________________<\/p>\n

> stargazer(RegModel.4, header=FALSE, type = “text”,
\n+\u00a0\u00a0 title=”Tabla 1. Modelos estimados”,
\n+ digits=2, single.row=FALSE, omit.stat=c(“LL”,”ser”,”f”))<\/p>\n

Tabla 2. Modelos estimados\u00a0 : 100\/mpg\u00a0 \u00a0 vs.\u00a0 peso.\u00a0 (se interpreta por cada 100 millas cuantos galones se consumen)
\n========================================
\nDependent variable:
\n—————————
\nmpg100
\n—————————————-
\npeso\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.001***
\n(0.0001)<\/p>\n

precio\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.0001**
\n(0.0000)<\/p>\n

Constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.77**
\n(0.30)<\/p>\n

—————————————-
\nObservations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a074
\nR2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.75
\nAdjusted R2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.74
\n========================================
\nNote:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 *p<0.1; **p<0.05; ***p<0.01<\/p>\n

___________________________________________________________________________<\/p>\n

 <\/p>\n

> stargazer(RegModel.3, RegModel.4, header=FALSE, type = “text”,
\n+\u00a0\u00a0 title=”Tabla 1. Modelos estimados”,
\n+ digits=2, single.row=FALSE, omit.stat=c(“LL”,”ser”,”f”))<\/p>\n

Tabla 3. Modelos estimados son 2. con una variable y con dos variables explicativas.
\n=========================================
\nDependent variable:
\n—————————-
\nmpg100
\n(1)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (2)
\n—————————————–
\npeso\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.001***\u00a0\u00a0\u00a0\u00a0\u00a0 0.001***
\n(0.0001)\u00a0\u00a0\u00a0\u00a0\u00a0 (0.0001)<\/p>\n

precio\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.0001**
\n(0.0000)<\/p>\n

Constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.77**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.77**
\n(0.31)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (0.30)<\/p>\n

——————————————————————–
\nObservations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 74
\nR2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.73\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.75
\nAdjusted R2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.73\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.74
\n=========================================
\nNote:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 *p<0.1; **p<0.05; ***p<0.01<\/p>\n","protected":false},"excerpt":{"rendered":"

Autor:\u00a0 Guillermo Martinez Atilano El siguiente Modelo fue estimado con R. Utiliza la base de datos Auto.dta de Stata. 2018-08-03 00:30:44 > articulo <- readXL(“C:\/Users\/Atilano\/Desktop\/auto_es.xls”, rownames=FALSE, header=TRUE, na=””, +\u00a0\u00a0 sheet=”Sheet1″, stringsAsFactors=TRUE) La gr\u00e1fica de dispersi\u00f3n muestra una relaci\u00f3n negativa entre el peso de un autom\u00f3vil y las millas por gal\u00f3n. A memor peso aumenta el … Seguir leyendo Modelo Auto con R<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-101","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/pages\/101","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=101"}],"version-history":[{"count":5,"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/pages\/101\/revisions"}],"predecessor-version":[{"id":122,"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/pages\/101\/revisions\/122"}],"wp:attachment":[{"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=101"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}