Exercise 4.1 - Lenin Kamma

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library(readr)
library(ggm)

## Loading required package: igraph

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
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##     decompose, spectrum

## The following object is masked from 'package:base':
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##     union

## 
## Attaching package: 'ggm'

## The following object is masked from 'package:igraph':
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##     pa

library(Rcmdr)

## Loading required package: splines

## Loading required package: RcmdrMisc

## Loading required package: car

## Loading required package: carData

## Loading required package: sandwich

## Loading required package: effects

## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car

## lattice theme set by effectsTheme()
## See ?effectsTheme for details.

## The Commander GUI is launched only in interactive sessions

## 
## Attaching package: 'Rcmdr'

## The following object is masked from 'package:car':
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##     Confint

## The following object is masked from 'package:base':
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##     errorCondition

student_survey <- read_csv("C:/Users/Owner/Desktop/Lenin Files/Data Sciences/Assignments/student-survey.csv")

## Parsed with column specification:
## cols(
##   TimeReading = col_double(),
##   TimeTV = col_double(),
##   Happiness = col_double(),
##   Gender = col_double()
## )

View(student_survey)
cov(student_survey$TimeReading, student_survey$TimeTV)

## [1] -20.36364

cor(student_survey$TimeReading, student_survey$TimeTV, method = "pearson")

## [1] -0.8830677

cor(student_survey, use = "complete.obs", method = "pearson")

##             TimeReading       TimeTV  Happiness       Gender
## TimeReading  1.00000000 -0.883067681 -0.4348663 -0.089642146
## TimeTV      -0.88306768  1.000000000  0.6365560  0.006596673
## Happiness   -0.43486633  0.636555986  1.0000000  0.157011838
## Gender      -0.08964215  0.006596673  0.1570118  1.000000000

cor.test(student_survey$TimeReading, student_survey$Happiness, alternative = "less", method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  student_survey$TimeReading and student_survey$Happiness
## t = -1.4488, df = 9, p-value = 0.09067
## alternative hypothesis: true correlation is less than 0
## 95 percent confidence interval:
##  -1.0000000  0.1151482
## sample estimates:
##        cor 
## -0.4348663

cor.test(student_survey$TimeReading, student_survey$Happiness, alternative = "less", method = "pearson", conf.level = 0.99)

## 
##  Pearson's product-moment correlation
## 
## data:  student_survey$TimeReading and student_survey$Happiness
## t = -1.4488, df = 9, p-value = 0.09067
## alternative hypothesis: true correlation is less than 0
## 99 percent confidence interval:
##  -1.0000000  0.3422209
## sample estimates:
##        cor 
## -0.4348663

cor(student_survey)

##             TimeReading       TimeTV  Happiness       Gender
## TimeReading  1.00000000 -0.883067681 -0.4348663 -0.089642146
## TimeTV      -0.88306768  1.000000000  0.6365560  0.006596673
## Happiness   -0.43486633  0.636555986  1.0000000  0.157011838
## Gender      -0.08964215  0.006596673  0.1570118  1.000000000

cor(student_survey)^2

##             TimeReading       TimeTV  Happiness       Gender
## TimeReading 1.000000000 0.7798085292 0.18910873 0.0080357143
## TimeTV      0.779808529 1.0000000000 0.40520352 0.0000435161
## Happiness   0.189108726 0.4052035234 1.00000000 0.0246527174
## Gender      0.008035714 0.0000435161 0.02465272 1.0000000000

student_survey2 <- student_survey[, c("TimeReading","TimeTV","Happiness")]
pc_ss <- pcor(c("TimeReading", "TimeTV","Happiness"), var(student_survey2))
pc_ss

## [1] -0.872945

(pc_ss)^2

## [1] 0.762033

Exercise 4.1 - Lenin Kamma

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