Analyzing Bachelor Of Commerce Core Units Data In Essay.

The Assignment Requirements

The assignment requires that you analyse a data set, interpret, and draw conclusions from your analysis, and then convey your conclusions in a written email. The assignment must be completed individually. The assignment must be submitted by the due date electronically in Moodle. When submitting electronically, you must check that you have submitted the file correctly by following the instructions provided in Moodle. Please note that we will NOT accept any hard copies or assignments submitted via email.

Once you start working on the file you may save and go back as many times as you like; there is no need to complete it all in one go. Not following the instructions in this document may result in a delay in the release of your results and a penalty of marks deducted.

Aus University is an Australian university that offers various courses such as the Bachelor of Commerce. To complete the Bachelor of Commerce degree, the students must attain 24 credit points including 8 credit points of core units. Angelina Sully is the Associate Dean, Teaching and Learning at the Aus Business School. She has asked you, Brad Mit, to conduct exploratory and descriptive analysis to gain a better understanding of the performance of the Bachelor of Commerce students. You can see her email on the next page.

To: Brad Mit

From: Angelina Sully

Subject: Analysis of the Bachelor of Commerce core units data

Dear Brad,

The academic board is keen to understand the student cohort, how students have performed, and if their performance is related to their activities such as attendance in lectures and tutes, to ensure we are on the right track regarding our strategic goals.

As a result, can you investigate the following?

1. Average core score is the most important measure that the board is interested in. Can you provide me with an overall summaryof the average student score for the 8 core units? It would be easier for us if you could provide us with some graphs such as Histograms and Box plots please.
2. The board believes that performance in core unit 3 (Business Analytics) does not differ between genders. Can you provide a comparison of the core unit 3 scores across different genders?
3. The board wants to find out whether the proportion of students origin (local or international) differs between different genders. Can you provide some insights?
4. The board also wants to know if there is a relationship between the total number of attendances and core unit 3 scores. Can you provide some information?
5. Finally, I would also like to explore the relationship between gender and attendance of core units as well.

I look forward to your response and the Dashboard.

Sincerely,

Angelina Sully

Associate Dean, Teaching and Learning

This sheet provides information about the variables used in this assignment.

Sheet 2: Your Data

This sheet includes the data set that you are asked to analyse.

Use Q1 to Q5 sheets to perform the relevant analyses. When calculating any figures please include at least 2 decimal points. Please note, these sheets are not marked.

Use the Results sheet to report your answers.

The assignment consists of two parts:

When conducting the analysis, you will apply techniques from descriptive analytics and visualisations; hence you will use various tables, graphs, and summary measures. Sometimes we refer to this type of analysis as “exploratory analysis”. When exploring data, we often produce more results than we eventually use in the final report, but by investigating the data from a number of angles, we can develop a much better ‘feel’ for the data: a deeper understanding of the data.

Guidelines for Each Question

In order to prepare a reply to Angelina, you will need to examine and analyse the data set thoroughly. The following guidelines for each question should be considered carefully:

1. A summary of average scores: To answer this question you should firstly calculate the average student scores for the 8 core units and then analyse them. You should derive summary measures and check for unusual data along with producing any suitable graphs and tables.
2. Core unit 3 score by gender: Investigate any particular relationship(s) between core unit 3 scores and gender.
3. Origin by gender: Explore any particular relationship between student origin and gender.
4. Core unit 3 grade by attendance rate: Research any particular relationship between student grades in core unit 3 and their attendance rate in lectures and seminars.
5. Attendance and gender:Investigate for any particular relationship between students’ total attendance in core units and their gender.

The analysis you submit should be on the provided sheets (Q1 to Q5). Where possible, it is always useful to produce both numerical and graphical statistical summaries as sometimes, something is revealed in one that is not obvious in the other. Poorly presented, unorganised analysis or excessive output will be penalised.

It is usual, when writing any professional document, to caption any tabular or graphical output professionally.  For example, TABLE 1: Summary statistics for the variable Average Core Score.

You are required to reply by email, detailing essential information and conclusions from your data analysis. You are allowed no more than 2 pages to convey your written conclusions. Remember you should use font size 12 and leave a margin of 2.54 cm.

Keep the English simple and the explanations succinct. Avoid the use of technical statistical jargon. Your reader will not necessarily understand even simple statistical terms, thus your task is to convert your analysis into plain, simple, easy to understand language.

The email is to be written as a stand-alone document (assume that the Angelina Sully will only read your email). Thus, you should not have any references in the email to your analysis, nor should you include any charts and tables within it.

Use an email format for your reply. This means the email heading (e.g., To:, From:, Subject:) should be included, the recipient should be addressed at the beginning and the signature or name of the sender should be included at the end.

When composing your reply, make sure that you actually answer the questions asked. Cite (state)the summary statistics of importance (without referring to your analysis section). Sequentially number your answers in your email to match the email of the Associate Dean, Teaching and Learning. Include a simple introduction at the start of the email and a summary/conclusion at the end.

Marks will be deducted for the use of technical terms, irrelevant material, poor presentation /organisation/formatting and emails that are over two pages long.

When you have completed the email, it is a useful exercise to leave it for a day, return to it and re-read it as if you knew nothing about the analysis.  Does it flow easily?  Does it make sense?  Can someone without prior knowledge follow your written conclusions?  Often on re-reading, you become aware that you have made some points in a clumsy manner and find that you can re-phrase them much more clearly.

Your submission will comprise of two files: one Microsoft Word file (Part 2: Email) and one Microsoft Excel file (Analysis). This assessment is to be submitted ONLINE ONLY into the Assessment 1 Dropbox available on the MIS171/MISP171 in Deakin College Moodle.

Please do not rename the Excel file after you download it. The word file should have the same file name as the Excel file (MIS171_StudentID.doc or .docx). You must keep a backup copy of every assignment you submit until the marked assignment has been returned to you.  In the unlikely event that one of your assignments is misplaced, you will need to submit your backup copy.

All work you submit is checked by electronic or other means for the purposes of detecting collusion and/or plagiarism.

When you are required to submit an assignment through your Moodle site, you will receive an email to your Deakin email address confirming that it has been submitted. You should check that you can see your assignment in the Submissions view of the Assignment Dropbox folder after upload, and check for, and keep, the email receipt for the submission.

Notes

• Penalties for late submission:The following marking penalties will apply if you submit an assessment task after the due date without an approved extension: 5% will be deducted from available marks for each day up to five days, and work that is submitted more than five days after the due date will not be marked. You will receive 0% for the task. 'Day' means working day for paper submissions and calendar day for electronic submissions. The Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date.
• For more information about academic misconduct, special consideration, extensions, and assessment feedback, please refer to the unit outline and student portal.

		SUMMARY STATISTICS
CORE1		CORE2		CORE3		CORE4		CORE5		CORE6		CORE7		CORE8
Mean	62.53908	Mean	87.67936	Mean	85.08818	Mean	70.12625	Mean	76.02204	Mean	83.53307	Mean	60.87174	Mean	68.47695
Standard Error	0.749876	Standard Error	0.373296	Standard Error	0.453174	Standard Error	0.544763	Standard Error	0.710116	Standard Error	0.448614	Standard Error	0.481653	Standard Error	0.368674
Median	63	Median	90	Median	88	Median	70	Median	79	Median	86	Median	60	Median	68
Mode	50	Mode	97	Mode	96	Mode	62	Mode	82	Mode	95	Mode	60	Mode	67
Standard Deviation	16.75095	Standard Deviation	8.338797	Standard Deviation	10.12315	Standard Deviation	12.16908	Standard Deviation	15.86279	Standard Deviation	10.02128	Standard Deviation	10.7593	Standard Deviation	8.235549
Sample Variance	280.5944	Sample Variance	69.53554	Sample Variance	102.4782	Sample Variance	148.0864	Sample Variance	251.628	Sample Variance	100.4261	Sample Variance	115.7626	Sample Variance	67.82427
Kurtosis	-0.46269	Kurtosis	0.947642	Kurtosis	1.326087	Kurtosis	-0.54341	Kurtosis	0.47567	Kurtosis	1.373255	Kurtosis	-0.20581	Kurtosis	-0.00595
Skewness	-0.12534	Skewness	-1.14881	Skewness	-1.23307	Skewness	-0.03043	Skewness	-0.86489	Skewness	-1.19925	Skewness	-0.04725	Skewness	0.037547
Range	79	Range	41	Range	54	Range	62	Range	84	Range	58	Range	62	Range	49
Minimum	19	Minimum	56	Minimum	42	Minimum	36	Minimum	14	Minimum	37	Minimum	27	Minimum	42
Maximum	98	Maximum	97	Maximum	96	Maximum	98	Maximum	98	Maximum	95	Maximum	89	Maximum	91
Sum	31207	Sum	43752	Sum	42459	Sum	34993	Sum	37935	Sum	41683	Sum	30375	Sum	34170
Count	499	Count	499	Count	499	Count	499	Count	499	Count	499	Count	499	Count	499

min	19
max	147
bin	Frequencies	interval
20	3	0-20
40	48	21-40
60	168	41-60
80	199	61-80
100	81	81-100
120	0	101-120
140	0
160	1
total	500

It is symmetric

Graphical representation for core2

frequencies	interval
0	41-50
3	51-60
22	61-70
62	71-80
184	81-90
229	91-100
500

It is symmetric to the right.

For core 3

Bin	frequencies	interval
50	2	41-50
60	11	51-60
70	39	61-70
80	86	71-80
90	165	81-90
100	197	91-100

It is symmetric to the right.

For core 4

Bin	frequencies	interval
40	3	31-40
50	25	41-50
60	83	51-60
70	151	61-70
80	130	71-80
90	86	81-90
100	22	91-100

It is symmetric

For core 5

Bin	frequencies	Interval
20	1	10 to 19
30	3	20 to 29
40	10	30 to 39
50	28	40 to 49
60	42	50 to 59
70	77	60 to69
80	109	70 to 79
90	133	80 to 89
100	97	90 to 99

It is symmetric to the right

For core 6

Bin	Frequency	Interval
40	1	30-39
50	2	40-49
60	11	50-59
70	47	60-69
80	94	70-79
90	199	80-89
100	146	90-99

It is symmetric to the right

For core 7

Bin	Frequencies	Interval
30	2	21-30
40	11	31-40
50	73	41-50
60	164	51-60
70	153	61-70
80	82	71-80
90	15	81-90
100	0	91-100

We have to investigate any particular relationship(s) between core unit 3 scores and gender.

We use two sample unequal variances t- test.

Now we set up the hypothesis

: Core unit 3 scores and gender population mean are same.

: Core unit 3 scores and gender population mean are not same.

Using excel we get the following table

Mean	85.08818	130.5
Variance	102.4782	27144.5
Observations	499	2
Hypothesized Mean Difference	0
Degrees of freedom	1
t Stat	-0.3898
P(T<=t) one-tail	0.38169
t Critical one-tail	6.313752
P(T<=t) two-tail	0.763381
t Critical two-tail	12.7062

P-value =0.76

α- value = 0.05

Since p-value < α- value.

We clearly reject. Therefore there is a sufficient evidence at 5% level   of significance to conclude that Core unit 3 scores and gender population mean are not same. 

Origin	Female	Male	Other	Total
International	85	64	6	155
Local	162	175	8	345
Total	247	239	14	500

EXPT. FREQ
International	76.57	74.09	4.34
Local	170.43	164.91	9.66

O-E	8.43	-10.09	1.66
	-8.43	10.09	-1.66
(0-E)^2	71.0649	101.8081	2.7556
	71.0649	101.8081	2.75
(0-E)^2/E	0.928104	1.374114	0.634931
	0.416974	0.617356	0.284679

: There is a significant relationship between origin and gender.

: There is no significant relationship between origin and gender.

Chi-square	4.256157
Degrees of freedom	2
table value	0.1

Since calculated t > tab t

We Cleary reject our null hypothesis. There is a strong evidence at 5% level of significance that there is no significant relationship between origin and gender.

: Student grades in core unit 3 and their attendance rate in lectures and seminars population mean are same.

: Student grades in core unit 3 and their attendance rate in lectures and seminars population mean are not same.

Using excel we get the following table

	Attendance	Core 3
Mean	12.07014	85.08818
Variance	21.18985	102.4782
Observations	499	499
Hypothesized Mean Difference	0
Degrees of freedom	696
t Stat	-146.674
P(T<=t) one-tail	0
t Critical one-tail	1.647046
P(T<=t) two-tail	0
t Critical two-tail	1.963378

P-value =0

α- value = 0.05

Since p-value < α- value.

We clearly reject. Therefore there is a sufficient evidence at 5% level   of significance to conclude that Student grades in core unit 3 and their attendance rate in lectures and seminars population mean are not same.

: Students’ total attendance in core units and their gender population mean are same.

: Students’ total attendance in core units and their gender population mean are not same.

Using excel we get the following table

t-Test: Two-Sample Assuming Unequal Variances
Mean	12.07014028	130.5
Variance	21.18984958	27144.5
Observations	499	2
Hypothesized Mean Difference	0
Degrees of freedom	1
t Stat	-1.016563729
P(T<=t) one-tail	0.247385513
t Critical one-tail	6.313751515
P(T<=t) two-tail	0.494771025
t Critical two-tail	12.70620474

P-value =0.494

α- value = 0.05

Since p-value > α- value.

We may not reject. Therefore there is a sufficient evidence at 5% level   of significance to conclude that Students’ total attendance in core units and their gender population mean are same.

Conclusion:

From our analysis it is conclude that

• From core1 to core 7, we have seen that there are different types of graph like symmetric, positively skewed and negatively skewed.
• We investigate any particular relationship(s) between core unit 3 scores and gender. we get  there is a sufficient evidence at 5% level   of significance to conclude that Core unit 3 scores and gender population mean are not same.
• Explore any particular relationship between student origin and gender we get there is a strong evidence at 5% level of significance to conclude that there is no significant relationship between origin and gender.
• We investigate for any particular relationship between students’ total attendance in core units and their gender, we get there is a sufficient evidence at 5% level of significance to conclude that students total attendance in core units and their gender population mean are same.

efore there is a sufficient evidence at 5%