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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

Table


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

Table


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

Table


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

table


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

Table


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%

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My Assignment Help. (2019). Analyzing Bachelor Of Commerce Core Units Data In Essay.. Retrieved from https://myassignmenthelp.com/free-samples/mis171-business-analytics.

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