Introduction to Biostatistics: Study Review and Regression Analysis using R

Running head: INTRODUCTION TO BIOSTATISTICS 1
Introduction to Biostatistics
Name:
Institution:

INTRODUCTION TO BIOSTATISTICS 2
Question 1: Study Review
This paper seeks to critically analyze a research study on transport activities connected with
youths in New Zealand (Ward, McGee, Freeman, Gendall, & Cameron, 2018).
Review against Items 10, 12-17 of the STROBE Checklist
In order to achieve the desired sample size, secondary schools were targeted in order to access
respondents with the age of interest in this case. 775 respondents participated in the study.
Data analysis involved descriptive analysis and inferential analysis. On descriptive analysis,
means, minimum, maximum and standard deviations were calculated and reported. On
inferential statistics, independent (unpaired) sample t-tests and chi- square tests were conducted.
The missing values in the captured data were included from the analysis. 82 percent of the study
participants provided complete responses to the survey questions, while the remaining 16 percent
had incomplete responses.
A pilot study was conducted on the same number of schools as those considered in the main
study. This study was not conducted in stages. The pilot study was key in improving the data
collection instruments. The response rate for the actual study was 71.5 percent (the 775
participants). Teenagers who participated in the class survey had a response rate of 77.2%, while
those who took the survey at home had a response rate of 65.6%. This response rate is high
enough, thus the results obtained were reliable. From the report, the number of females with
missing data was twice the number of males. This was because the male respondents chose to
participate in the survey in class, rather than answer the questions at home. Survey in class had a
higher response rate, since it was more convenient for the students. Answering the survey
questions a home was ineffective due to fatigue.

INTRODUCTION TO BIOSTATISTICS 3
From the study, 49 percent were male, while 51% were female. 7.9% were 15 years old, 49.2%
were 16 years, 40.7% were 17 years, and 2.2% were 18 years, while 0.1% of them were 19 years
old. In addition, 71.2% of the respondents were from urban areas and 28.8% from rural areas.
Moreover, 85.1% of the participants were European nationals, while the remaining 14.9% came
from other nations. In addition, 59.7% of the teenagers earned less than 50 dollars per month,
11.8% had an income of between 51 and 99 dollars, while .5% of them earned over 100 dollars
per month.
The chi- square tests conducted showed significant associations between gender and some of the
modes of transport. From the results, it was evident that more male than female students
preferred using bicycles, using skateboards or riding motorcycles. Additionally, more female
than male students preferred taking public or school buses or being passengers in cars. Further
analysis revealed that more male students had driving licenses as compared to their female
colleagues. In addition, t-test results revealed that more male students participated in sporting
activities, while the females were more active in social and cultural events.
Question 2: Regression Analysis using R
Introduction
This section involves linear regression analysis using the R software. The research question in
this case will be: Does the number of activities a student has attended in the past month predict
self-reported sedentary hours per week after correcting for gender? The independent variables in
this case will be will be activities attended and gender. The dependent variable will be sedentary
hours. The research hypothesis is as give below:

INTRODUCTION TO BIOSTATISTICS 4
H0: Number of activities attended in a month does not predict the number of self-reported
sedentary hours.
H1: Number of activities attended in a month predicts the number of self-reported sedentary
hours.
The obtained data has a total of 271 respondents. The effect of activities undertaken on sedentary
hours will be checked after controlling for gender.
The codes used in R software are given on the appendix.
Results
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
activities 271 2 13 7.07 2.291
sed 271 4.5 20.4 10.637 3.0521
The table above shows descriptive statistics for the two main variables of the study (activities
and sedentary hours).