Economics 104: Project 1Fall 2022, UCLADue Date: Oct 12, 2022 by 11:59 PM (PST)For this project, you will work any dataset you like, however, it must contain at least 5 different predictors...

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2022-10-12
1. Provide a descriptive analysis of your variables. This should include histograms and fitted distributions, correlation plot, boxplots, scatterplots, and statistical summaries (e.g., the five-number summary). All figures must include comments.
library(readr)
exams <- read_csv("exams.csv")
## Rows: 1000 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): gender, race/ethnicity, parent_education_level, lunch, test_prep_co...
## dbl (1): math
##
## 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.
View(exams)
First look of data
# descriptive measures
skimr::skim(exams)
Data summary
    Name
    exams
    Number of rows
    1000
    Number of columns
    6
    _______________________
    
    Column type frequency:
    
    character
    5
    numeric
    1
    ________________________
    
    Group variables
    None
Variable type: character
    skim_variable
    n_missing
    complete_rate
    min
    max
    empty
    n_unique
    whitespace
    gender
    0
    1
    4
    6
    0
    2
    0
    race/ethnicity
    0
    1
    7
    7
    0
    5
    0
    parent_education_level
    0
    1
    11
    18
    0
    6
    0
    lunch
    0
    1
    8
    12
    0
    2
    0
    test_prep_course
    0
    1
    4
    9
    0
    2
    0
Variable type: numeric
    skim_variable
    n_missing
    complete_rate
    mean
    sd
    p0
    p25
    p50
    p75
    p100
    hist
    math
    0
    1
    66.09
    15.16
    0
    57
    66
    77
    100
    ▁▁▅▇▃
#histogram of math score
hist(exams$math, main="Histogram of math score")
#Boxplot of math score
boxplot(exams$math, main="Boxplot of Math score")
# Removing Outliers
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
# using 1.5*IQR where IQR = Q3-Q1
exams<-exams%>%
filter(math>=30)
#After removing outliers
boxplot(exams$math)
hist(exams$math)
Categorical variables distribution
library(ggplot2)
# gender distribution
ggplot(data=exams, aes(x=gender)) +
geom_bar() +
labs (title = "Gender Distribution", x = "Gender", y = "Total Count")+ theme_classic()
#Race/ ethnicity distribution
ggplot(data=exams, aes(x=exams$`race/ethnicity`)) +
geom_bar() +
labs (title = "race/ethnicity Distribution", x = "race/ethnicity", y = "Total Count")+ theme_classic()
#parent Education level distribution
ggplot(data=exams, aes(x=parent_education_level)) +
geom_bar() +
labs (title = "parent_education_level Distribution", x = "parent_education_level", y = "Total Count")+ theme_classic()
#Lunch distribution
ggplot(data=exams, aes(x=lunch)) +
geom_bar() +
labs (title = "lunch Distribution", x = "lunch", y = "Total Count")+ theme_classic()
#test preparation course
ggplot(data=exams, aes(x=test_prep_course)) +
geom_bar() +
labs (title = "test_prep_course Distribution", x = "test_prep_course", y = "Total Count")+ theme_classic()
1. Estimate a multiple linear regression model that includes all the main effects only (i.e., no interactions nor higher order terms). We will use this model as a baseline. Comment on the statistical and economic significance of your estimates. Also, make sure to provide an interpretation of your estimates.
baseline_mod<-lm(math~.,data=exams)
stargazer::stargazer(baseline_mod,type = "text")
##
## ===================================================================
## Dependent variable:
## ---------------------------
## math
## -------------------------------------------------------------------
## gendermale 4.322***
## (0.808)
##
## `race/ethnicity`group B 2.685
## (1.639)
##
## `race/ethnicity`group C 2.746*
## (1.532)
##
## `race/ethnicity`group D 5.413***
## (1.563)
##
## `race/ethnicity`group E 10.033***
## (1.729)
##
## parent_education_levelbachelor's degree 2.074
## ...