# Unmasking Insights: Biostatistical Analysis of COVID-19 Impact on University Students

Delve into a rigorous Biostatistics examination of COVID-19's impact on University of Sydney students. From refining the population of interest to uncovering patterns in COVID-19 distribution across employment levels, this analysis navigates intricate datasets. Rigorous data cleaning and variable analyses ensure robust conclusions, while a one-sample t-test reveals significant disparities in unpaid domestic work, shedding light on the broader implications of the pandemic on student life.

## Problem Description:

This assignment on Biostatistics focuses on the data analysis of a study concerning the spread and infection of COVID-19 among students at the University of Sydney. The dataset includes various variables related to demographics, health, and COVID-19 experiences. The goal is to apply statistical methods to gain insights into the data, identify patterns, and draw meaningful conclusions.

### Question 1:

1. Population of interest includes a group of aspects not limited to human subjects that have something in common (Bloomfield& Fisher,2019). This study focuses on COVID-19 spread and infection among students within the University of Sydney, thus, students of the University of Sydney are the population of interest for this study.
2. Non-sampling errors are various sources of errors that are not related to sampling and are usually present in all types of surveys. Thus, there is a possibility of non-sampling error in this data. In this data set, non-sampling error in this data set can occur from missing data or respondent error where respondents could possibly provide incorrect answers or tend to exaggerate or underplay events (Chen &Haziza, 2019).
3. In for this population of interest, non-sampling error can be minimized by increasing the sample size and randomizing selection to eliminate biases that may exist within the selected sample.

### Solutions:

Population of Interest and Non-Sampling Errors

Population of Interest:

• The population of interest for this study comprises students at the University of Sydney, emphasizing their experiences with COVID-19.

Non-Sampling Errors:

• Non-sampling errors, such as missing data and respondent errors, can influence the dataset. To minimize these errors, increasing the sample size and randomizing selection are suggested strategies.

Question 2: Data Cleaning and Variable Analysis

• Age:

• Omitted 12 missing and implausible age values, resulting in 94 valid observations.

Livewith:

• Removed 5 missing and implausible livewith values, leaving 91 valid observations.

Willing:

• Deleted 1 missing value in the "Willing" variable, resulting in 91 valid observations.

Question 3: Statistical Confidence Score Analysis

Figure 1: Histogram showing statistical confidence score for the participants

Histogram:

• Presents a histogram showing the statistical confidence scores of participants.
• Noteworthy points include an unimodal, normally distributed shape with a mean value of 13.0 (SD 7.23).

Question 4: Two-Way Contingency Table Analysis

Table 1:

 Meets recommended vegetable intake Does not meet vegetable intake Total Male (2) 0 25 25 Female (1) 1 64 65 Total 1 89 90

Table 2: Two-way contingency table for vege and sex

Vegetable Intake and Sex:

• Illustrates a two-way contingency table, emphasizing the gender distribution and adherence to vegetable intake recommendations.
• Majority of participants do not meet recommended vegetable intake; only 1 female meets the recommendations.

Question 5: COVID Distribution Among Employment Levels

Figure 2:

Figure 2: Bar plot showing distribution of covid among employment levels

Bar Plot:

• Displays the distribution of COVID cases among different employment levels.
• Notable patterns include full-time employed participants having the highest count in various COVID-related scenarios.

Question 6: Descriptive Statistics and Frequency Distribution

Table 1: Descriptive Statistics:

• Presents descriptive statistics for age and fruit consumption.
• Provides frequency distribution for degree, COVID status, and the year of contracting COVID.

Question 7:

Table 2: One sample t-test results

 Variable Statistic df P Housewk Student’s t -8.10 88.0 <.001

One Sample t-test for Domestic Work

Conducts a one-sample t-test to test the hypothesis regarding the amount of unpaid domestic work.

Test Hypotheses:

• H0: µ = 360
• H1: µ ≠ 360

Results indicate a significant difference, leading to the rejection of the null hypothesis.

This comprehensive analysis provides a detailed overview of the dataset, ensuring clarity and understanding of the applied statistical methods and their implications.