library(readr)
data <- read_csv("HousePriceData.csv")## Rows: 905 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Parking, City_Category
## dbl (8): Observation, Dist_Taxi, Dist_Market, Dist_Hospital, Carpet, Builtup...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(data)## spc_tbl_ [905 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Observation : num [1:905] 1 2 3 4 5 6 7 8 9 10 ...
## $ Dist_Taxi : num [1:905] 9796 8294 11001 8301 10510 ...
## $ Dist_Market : num [1:905] 5250 8186 14399 11188 12629 ...
## $ Dist_Hospital: num [1:905] 10703 12694 16991 12289 13921 ...
## $ Carpet : num [1:905] 1659 1461 1340 1451 1770 ...
## $ Builtup : num [1:905] 1961 1752 1609 1748 2111 ...
## $ Parking : chr [1:905] "Open" "Not Provided" "Not Provided" "Covered" ...
## $ City_Category: chr [1:905] "CAT B" "CAT B" "CAT A" "CAT B" ...
## $ Rainfall : num [1:905] 530 210 720 620 450 760 1030 1020 680 1130 ...
## $ House_Price : num [1:905] 6649000 3982000 5401000 5373000 4662000 ...
## - attr(*, "spec")=
## .. cols(
## .. Observation = col_double(),
## .. Dist_Taxi = col_double(),
## .. Dist_Market = col_double(),
## .. Dist_Hospital = col_double(),
## .. Carpet = col_double(),
## .. Builtup = col_double(),
## .. Parking = col_character(),
## .. City_Category = col_character(),
## .. Rainfall = col_double(),
## .. House_Price = col_double()
## .. )
## - attr(*, "problems")=<externalptr>summary(data)## Observation Dist_Taxi Dist_Market Dist_Hospital
## Min. : 1.0 Min. : 146 Min. : 1666 Min. : 3227
## 1st Qu.:237.0 1st Qu.: 6477 1st Qu.: 9367 1st Qu.:11302
## Median :469.0 Median : 8228 Median :11149 Median :13189
## Mean :468.4 Mean : 8235 Mean :11022 Mean :13091
## 3rd Qu.:700.0 3rd Qu.: 9939 3rd Qu.:12675 3rd Qu.:14855
## Max. :932.0 Max. :20662 Max. :20945 Max. :23294
##
## Carpet Builtup Parking City_Category
## Min. : 775 Min. : 932 Length:905 Length:905
## 1st Qu.: 1317 1st Qu.: 1579 Class :character Class :character
## Median : 1478 Median : 1774 Mode :character Mode :character
## Mean : 1511 Mean : 1794
## 3rd Qu.: 1654 3rd Qu.: 1985
## Max. :24300 Max. :12730
## NA's :7
## Rainfall House_Price
## Min. :-110.0 Min. : 1492000
## 1st Qu.: 600.0 1st Qu.: 4623000
## Median : 780.0 Median : 5860000
## Mean : 786.9 Mean : 6083992
## 3rd Qu.: 970.0 3rd Qu.: 7200000
## Max. :1560.0 Max. :150000000
## head(data)## # A tibble: 6 × 10
## Observation Dist_Taxi Dist_Market Dist_Hospital Carpet Builtup Parking
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 9796 5250 10703 1659 1961 Open
## 2 2 8294 8186 12694 1461 1752 Not Provided
## 3 3 11001 14399 16991 1340 1609 Not Provided
## 4 4 8301 11188 12289 1451 1748 Covered
## 5 5 10510 12629 13921 1770 2111 Not Provided
## 6 6 6665 5142 9972 1442 1733 Open
## # ℹ 3 more variables: City_Category <chr>, Rainfall <dbl>, House_Price <dbl>colnames(data)## [1] "Observation" "Dist_Taxi" "Dist_Market" "Dist_Hospital"
## [5] "Carpet" "Builtup" "Parking" "City_Category"
## [9] "Rainfall" "House_Price"regresion <- lm(House_Price~ Dist_Taxi+ Dist_Market+Dist_Hospital+ Carpet+Builtup+factor(Parking)+ factor(City_Category)+Rainfall, data=data)
summary(regresion)##
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital +
## Carpet + Builtup + factor(Parking) + factor(City_Category) +
## Rainfall, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3586934 -837542 -65314 784513 4577689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.568e+06 3.688e+05 15.097 < 2e-16 ***
## Dist_Taxi 2.834e+01 2.694e+01 1.052 0.2931
## Dist_Market 1.237e+01 2.089e+01 0.592 0.5538
## Dist_Hospital 5.071e+01 3.021e+01 1.679 0.0936 .
## Carpet 9.907e+03 1.428e+02 69.398 < 2e-16 ***
## Builtup -7.575e+03 2.412e+02 -31.403 < 2e-16 ***
## factor(Parking)No Parking -6.170e+05 1.393e+05 -4.429 1.06e-05 ***
## factor(Parking)Not Provided -5.077e+05 1.239e+05 -4.096 4.58e-05 ***
## factor(Parking)Open -2.597e+05 1.131e+05 -2.297 0.0218 *
## factor(City_Category)CAT B -1.883e+06 9.641e+04 -19.529 < 2e-16 ***
## factor(City_Category)CAT C -2.902e+06 1.062e+05 -27.321 < 2e-16 ***
## Rainfall -9.984e+01 1.548e+02 -0.645 0.5191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1228000 on 886 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.9429, Adjusted R-squared: 0.9422
## F-statistic: 1329 on 11 and 886 DF, p-value: < 2.2e-16Poder explicativo del modelo = 94%
Modelo altamente significativo estadísticamente. Variables como Carpet y
Dist_Hospital son significativas estadísticamente y tienen un impacto
positivo en el precio de la casa. Mientras tanto, todos los factores de
Parking y City_Category muestran también altos niveles de significancia,
pero finalmente un impacto negativo en el precio de las casas.