Multi Linear Regression Homework

README: Using the multi-linear regression demo from class complete an analysis of this dataset trying to predict the median_house_value

#0.[-5pts]Load Libraries rename the file to include your name (lose 5 pts if you don’t do it)

library(ggplot2)

Warning message:
In normalizePath(quartoSrcFile, winslash = "/") :
  path[1]="": No such file or directory

library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

library(GGally)

Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2

hp <- read.csv("https://raw.githubusercontent.com/jacopomazzoni/DIDA/main/week12/cali_housing.csv")
summary(hp)

   longitude         latitude     housing_median_age  total_rooms    total_bedrooms     population   
 Min.   :-124.3   Min.   :32.54   Min.   : 1.00      Min.   :    2   Min.   :   1.0   Min.   :    3  
 1st Qu.:-121.8   1st Qu.:33.93   1st Qu.:18.00      1st Qu.: 1448   1st Qu.: 296.0   1st Qu.:  787  
 Median :-118.5   Median :34.26   Median :29.00      Median : 2127   Median : 435.0   Median : 1166  
 Mean   :-119.6   Mean   :35.63   Mean   :28.64      Mean   : 2636   Mean   : 537.9   Mean   : 1425  
 3rd Qu.:-118.0   3rd Qu.:37.71   3rd Qu.:37.00      3rd Qu.: 3148   3rd Qu.: 647.0   3rd Qu.: 1725  
 Max.   :-114.3   Max.   :41.95   Max.   :52.00      Max.   :39320   Max.   :6445.0   Max.   :35682  
                                                                     NA's   :207                     
   households     median_income     median_house_value ocean_proximity   
 Min.   :   1.0   Min.   : 0.4999   Min.   : 14999     Length:20640      
 1st Qu.: 280.0   1st Qu.: 2.5634   1st Qu.:119600     Class :character  
 Median : 409.0   Median : 3.5348   Median :179700     Mode  :character  
 Mean   : 499.5   Mean   : 3.8707   Mean   :206856                       
 3rd Qu.: 605.0   3rd Qu.: 4.7432   3rd Qu.:264725                       
 Max.   :6082.0   Max.   :15.0001   Max.   :500001                       
                                                                         

hp <- na.omit(hp)

ggpairs(hp,  columns = 3:9, progress = FALSE, lower=list(combo=wrap("facethist", binwidth=0.8)) )

#1.[10pts]Split it into a testing and training set, as before.

set.seed(210191)

#2.[5pts] Generate the model:

model <- 

Error: Incomplete expression: model <- 

#3.[5pts] Regularize the model:

#4.[10pts] Model Evaluation Calculate the predictions and residuals:

#5.[10pts] Pot Trends in Errors Plot the yhat and residual columns use ylim(-450000,450000)

#6.[5pts] Typical Error Size calculate the standard deviation for the residuals for this model

#7.[5pts] Check for Overfitting

#8.[5pts] Are we overfitting? yes or no, why?