The Aussie University - MIS171: Data Analysis and Presentation Report

This report presents a comprehensive data analysis of student performance in the Bachelor of Commerce core units at The Aussie University. The analysis includes calculating average scores, identifying score distributions, and comparing the performance of different student groups (males vs. females, local vs. international). Statistical methods such as z-tests, ANOVA, and regression analysis are used to draw conclusions about the relationships between variables like attendance and grades. The report provides key findings, including the correlation between attendance and core unit 4 grades, and the statistical significance of differences in mean scores. The analysis is presented in a clear and concise manner, with references to the statistical methods used. The report is structured as an email to the Associate Dean, summarizing the key findings from the data analysis and providing supporting evidence.

Data Analysis and Presentation 1
DATA ANALYSIS AND PRESENTATION
By (Name)
The Name of the Class (Course)
Professor (Tutor)
The Name of the School (University)
The City and State where it is located
The Date

Data Analysis and Presentation 2
Data Analysis and Presentation
Email to the Associate Dean, Teaching and Learning
To: Claudia Brady
From: Sam Mannix
Subject: Analysis of the Bachelor of Commerce core units data
Dear Sam,
The following assessments could be made from the data provided:
Question 1
The average scores across all 8 core units were calculated for all 500 students and provided in a column to the
right of the existing data. The mean score for all students across all 8 core units was 71%. The median score for
all students with regard to average score across all 8 core units was 71.5%. Given that the mean and median are
almost similar, this indicates that the average score for students across all 8 core units is normally distributed
(Kemp, Hort & Hollowood 2018). The kurtosis and skewness for the same data are within -2 and 2; further
implying that the data is normally distributed. The inter-quartile range indicates that 8.375% of the different
between the third quartile and second quartile accounts for 50% of students’ (250 individuals) overall scores in
the 8 units. The standard deviation implies that majority of the average student scores across all 8 core units are
expected to differ from the mean by ± 5.69 % as indicated (Montgomery 2013).
Question 2
We can see that the mean scores for males and females with regard core unit 4 (Business analytics) are different
at 73.03% and 71.84% respectively. The other results are also different for all the other summary statistics e.g.

Data Analysis and Presentation 3
median, standard deviation, and inter-quartile range. After conducting a z-test, I am 95% confident that the
mean score for males and females with regard to core unit 4 is statistically different.
Question 3
35% of all students are local females, 14.4% of all students are international females, 30.6% are all students are
local males, 15.8% of all students are internal males, and the remaining 4.2% of all students are international
and local student who did not specify their gender. From the ANOVA result, I am 95% confident that the
proportion of local and internal students does not differ with regard to male and female gender (Gaston 2014).
Question 4
Through the use of regression analysis we are able to see that total attendance (independent variable) and core
unit 4 grades (dependent variables) have a weak positive correlation. In addition, 18.22% of the change in Core
unit grades can be explained by total attendance. Looking at the ANOVA results, I am 95% confident that there
is a relationship between that total attendance (independent variable) and core unit 4 grades (dependent
variables) (Fahrmeir et al. 2013).
Question 5
There is a weak positive correlation between core unit 4 grades and each of the three attendance variables;
tutorial attendance, lecture attendance, and total attendance. The regression is the same as in question 4;
therefore, I am 95% that there is a relationship between total attendance and core unit 4 grades.
The dashboard and assessment results are also provided.
Sincerely,
Sam Mannix

Data Analysis and Presentation 4
References
Fahrmeir, L., Kneib, T., Lang, S. & Marx, B. 2013. Regression: Models, Methods and Applications. 1st edn.
Berlin. GE: Springer Science & Business Media.
Gaston, L. 2014. Hypothesis Testing Made Simple. 1st edn. Leonard Gaston.
Kemp, S., Hort, J. & Hollowood, T. 2018. Descriptive Analysis in Sensory Evaluation. Hoboken, NJ: John
Wiley & Sons.
Montgomery, D. 2013. Introduction to Linear Regresssion Analysis. 5th edn. Hoboken, NJ: John Wiley & Sons.