Regresión Lineal WALMART
Eduardo Camacho - A01026437
2024-02-19
Instalar paquetes y llamar librerías
library(tidyverse)#file.choose()Importar Base de Datos
df <- read.csv("C:\\Users\\Eduardo\\Downloads\\Walmart_Store_sales.csv")Entender la Base de Datos
df$Date <- as.Date(df$Date, format="%d-%m-%Y")
summary(df)## Store Date Weekly_Sales Holiday_Flag
## Min. : 1 Min. :2010-02-05 Min. : 209986 Min. :0.00000
## 1st Qu.:12 1st Qu.:2010-10-08 1st Qu.: 553350 1st Qu.:0.00000
## Median :23 Median :2011-06-17 Median : 960746 Median :0.00000
## Mean :23 Mean :2011-06-17 Mean :1046965 Mean :0.06993
## 3rd Qu.:34 3rd Qu.:2012-02-24 3rd Qu.:1420159 3rd Qu.:0.00000
## Max. :45 Max. :2012-10-26 Max. :3818686 Max. :1.00000
## Temperature Fuel_Price CPI Unemployment
## Min. : -2.06 Min. :2.472 Min. :126.1 Min. : 3.879
## 1st Qu.: 47.46 1st Qu.:2.933 1st Qu.:131.7 1st Qu.: 6.891
## Median : 62.67 Median :3.445 Median :182.6 Median : 7.874
## Mean : 60.66 Mean :3.359 Mean :171.6 Mean : 7.999
## 3rd Qu.: 74.94 3rd Qu.:3.735 3rd Qu.:212.7 3rd Qu.: 8.622
## Max. :100.14 Max. :4.468 Max. :227.2 Max. :14.313str(df)## 'data.frame': 6435 obs. of 8 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2010-02-05" "2010-02-12" ...
## $ Weekly_Sales: num 1643691 1641957 1611968 1409728 1554807 ...
## $ Holiday_Flag: int 0 1 0 0 0 0 0 0 0 0 ...
## $ Temperature : num 42.3 38.5 39.9 46.6 46.5 ...
## $ Fuel_Price : num 2.57 2.55 2.51 2.56 2.62 ...
## $ CPI : num 211 211 211 211 211 ...
## $ Unemployment: num 8.11 8.11 8.11 8.11 8.11 ...Agregar Variables a Base
df$Year <- format(df$Date, "%Y")
df$Year <- as.integer(df$Year)
df$Month <- format(df$Date, "%m")
df$Month <- as.integer(df$Month)
df$WeekYear <- format(df$Date, "%W") #Iniciando en lunes
df$WeekYear <- as.integer(df$WeekYear)
df$WeekDay <- format(df$Date, "%u") #Iniciando en lunes
df$WeekDay <- as.integer(df$WeekDay)
df$Day <- format(df$Date, "%d")
df$Day <- as.integer(df$Day)
str(df)## 'data.frame': 6435 obs. of 13 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2010-02-05" "2010-02-12" ...
## $ Weekly_Sales: num 1643691 1641957 1611968 1409728 1554807 ...
## $ Holiday_Flag: int 0 1 0 0 0 0 0 0 0 0 ...
## $ Temperature : num 42.3 38.5 39.9 46.6 46.5 ...
## $ Fuel_Price : num 2.57 2.55 2.51 2.56 2.62 ...
## $ CPI : num 211 211 211 211 211 ...
## $ Unemployment: num 8.11 8.11 8.11 8.11 8.11 ...
## $ Year : int 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
## $ Month : int 2 2 2 2 3 3 3 3 4 4 ...
## $ WeekYear : int 5 6 7 8 9 10 11 12 13 14 ...
## $ WeekDay : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 5 12 19 26 5 12 19 26 2 9 ...Generar Regresión Lineal
regresion <- lm(Weekly_Sales ~., data=df)
summary(regresion)##
## Call:
## lm(formula = Weekly_Sales ~ ., data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1094800 -382464 -42860 375406 2587123
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.384e+09 9.127e+09 -0.261 0.7940
## Store -1.538e+04 5.202e+02 -29.576 < 2e-16 ***
## Date -3.399e+03 1.266e+04 -0.268 0.7883
## Holiday_Flag 4.773e+04 2.706e+04 1.763 0.0779 .
## Temperature -1.817e+03 4.053e+02 -4.484 7.47e-06 ***
## Fuel_Price 6.124e+04 2.876e+04 2.130 0.0332 *
## CPI -2.109e+03 1.928e+02 -10.941 < 2e-16 ***
## Unemployment -2.209e+04 3.967e+03 -5.569 2.67e-08 ***
## Year 1.212e+06 4.633e+06 0.262 0.7937
## Month 1.177e+05 3.858e+05 0.305 0.7604
## WeekYear NA NA NA NA
## WeekDay NA NA NA NA
## Day 2.171e+03 1.269e+04 0.171 0.8642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 520900 on 6424 degrees of freedom
## Multiple R-squared: 0.1495, Adjusted R-squared: 0.1482
## F-statistic: 113 on 10 and 6424 DF, p-value: < 2.2e-16Ajustar Regresión
df_ajustada <- df %>% select(-Store, -Date, - Year:-Day)
regresion_ajustada <- lm(Weekly_Sales ~., data=df_ajustada)
summary(regresion_ajustada)##
## Call:
## lm(formula = Weekly_Sales ~ ., data = df_ajustada)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1022429 -478555 -117266 397246 2800620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1726523.4 79763.5 21.646 < 2e-16 ***
## Holiday_Flag 74891.7 27639.3 2.710 0.00675 **
## Temperature -724.2 400.5 -1.808 0.07060 .
## Fuel_Price -10167.9 15762.8 -0.645 0.51891
## CPI -1598.9 195.1 -8.194 3.02e-16 ***
## Unemployment -41552.3 3972.7 -10.460 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 557400 on 6429 degrees of freedom
## Multiple R-squared: 0.02544, Adjusted R-squared: 0.02469
## F-statistic: 33.57 on 5 and 6429 DF, p-value: < 2.2e-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