Graded: 1.8, 1.10, 1.28, 1.36, 1.48, 1.50, 1.56, 1.70 (use the library(openintro); data(heartTr) to load the data)
#1.7 Fisher's irises: - a) 150
# b) Continuous numerical variable sepal length, sepal width, petal length and petal width.
# c) Ordinal categorical variable with level setosa, versicolor and verginica
#1.8 Smoking habits of UK residents: -
#a) Case
#b) 1691
#c) age: Discrete Numerical
# grossIncome: Continuous Numerical
# amtWeekend: Discrete Numerical
# amtWeekdays: Discrete Numerical
# Sex: Categorical nominal
# Martial: Categorical Ordinal
# smoke: Categorical Ordinal
#1.9 Air pollution and birth outcomes, scope of inference:
#a) Birth and Sample : 143,196 birth during period of 1989 to 1993
#b) As this is an observational data , so no causal relationship can be determined.
#1.10 Cheaters, scope of inference.
#a) Population of Interest : Cheaters
# Sample : 160 children's within age group of 5 and 15.
#b) No this study cannot be generalized , no causal relation ship can be established as this is an Observational study.
#1.23 Haters are gonna hate, study confirms
#a) 200 randomly selected men n women
#b) Response variable :- Reaction towards the imaginary oven
#c) Explanatory variable : Attitude
#d) yes
#e) This is an observational study , as the study observers the behavior of person based on some criteria.
#f) No Causal relationship can be inferred as this is an Observational study
#g) yes, as sample taken is random.
#1.28 Reading the paper.
#a) Looking at the article, I think there is a clear relationship between the smokers and people who are having dementia. And as the people who smoke more the more the risk of having dementia /Alzheimer / Vascular dementia. But this study is an Observational study which puts a question mark on any causal relation ship between smokers and Dementia. But still there is some sort of relation between the 2.
#b) This is also an Observational study, hence any causal relation between variable (Bullying & Sleep disorders) cannot be contemplated. But looking at the study one can infer that there seems to be a relation between Bullying and Sleep disorders.
#1.33 Light, noise, and exam performance
#a) Experimental Study
#b) Noise (no noise, construction noise, and human chatter noise), Light (fluorescent overhead lighting, yellow overhead lighting, no overhead lighting (only desk lamps))
#c) Researches wanted to show that Noise and Light level have different effect on male and females thus they had equal representation of both sexes.
#1.36 Exercise and mental health.
#a) Experimental Study
#b) Treatment group is the one which does exercise twice in a week
# Control group is the one which will remain as they are now.
#c) Exercise
#d) No
#e) yes, the study establishes causal exercise between exercise and mental health. Yes study can be generalized as we have taken random samples from stratified samples.
#f) May be the study can we based on sex and also can be divided into a) 5 times exercise a week b) 2-3 times a week and c) no exercise groups to be more generalized and clear of the outcomes.
statScores <- c(57, 66, 69, 71, 72, 73, 74, 77, 78, 78, 79, 79, 81, 81, 82, 83, 83, 88, 89, 94)
statScores
## [1] 57 66 69 71 72 73 74 77 78 78 79 79 81 81 82 83 83 88 89 94
boxplot(statScores)
summary(statScores)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 57.00 72.75 78.50 77.70 82.25 94.00
#install.packages("devtools")
#library(devtools)
#install_github("OpenIntroOrg/openintro-r-package", subdir = "openintro")
library(openintro)
## Please visit openintro.org for free statistics materials
##
## Attaching package: 'openintro'
## The following objects are masked from 'package:datasets':
##
## cars, chickwts, trees
data(heartTr)
NROW(heartTr)
## [1] 103
controlDied <- subset(heartTr, heartTr$transplant =='control' & heartTr$survived =='dead' )
dim(controlDied)
## [1] 30 8
NROW(controlDied)
## [1] 30
treatmentDied <- subset(heartTr, heartTr$transplant =='treatment' & heartTr$survived =='dead' )
dim(treatmentDied)
## [1] 45 8
nrow(treatmentDied)
## [1] 45
#proportion of control dead
(NROW(controlDied)/nrow(heartTr))
## [1] 0.2912621
#proportion of treatment dead
(NROW(treatmentDied)/nrow(heartTr))
## [1] 0.4368932
treatmentgrp <- subset(heartTr, heartTr$transplant=='treatment')
nrow(treatmentgrp)
## [1] 69