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Module learning outcomes being assessed:

LO1. Apply basic probability and other quantitative techniques in a business context.

LO2. Analyse realistic management problems based on analysis of data sets and case materials.

LO3. Apply the techniques studied to interpret and communicate findings on data analysis.

The file “Survey_Data_Sem3.xls” contains the data of an anonymous survey.

Click on the red triangles, within the spreadsheet, to see details of each variable.

The purpose of this report is to:

• Describe the sample
• Investigate whether factors such as gender, type of student and age influence the type of type of accommodation students stay in during term time.

The report should contain quantitative information, summary statistics and appropriate charts and tables.

The marking scheme is as follows:

• Report Structure and Presentation

Structure, layout, use of spell checker, charts and tables in text

• Analysis

Description of the data.

Investigation of factors affecting choice of term time accommodation.

Use the survey data to conduct regression analysis to investigate the relationship between the time taken from the student’s accommodation to Coventry University and the time spent on the Internet per day.

1.Conduct linear regression analysis.

• Perform, using Excel, Data Analysis Pack, Regression function, the regression analysis.

2.Produce the scatterplot including a fitted regression line.

3.Write the regression equation using the results of the excel table.

4.Determine and interpret the slope

5.Determine and interpret the intercept.

6.Determine and interpret the R-squared value.

7.Give predictions for the time spent on the internet when the time taken from student accommodation to Coventry University is:

8.Are the variables in your regression model statistically significant to your model?

Analysis

A survey was conducted on students of Coventry University in 2017 inquiring about certain details about their accommodation, habits and scores during their term.

The survey data provides raw information to facilitate is insight building regarding student demographics and habits and assists in studying relationships between student habits and outcomes. Numerical summary, graphical representation and descriptive statistics alone can work to provide valuable insights.

This study focuses on how student age, domicile status and gender may relate to the type of accommodation that a student may prefer.

The data included domicile status, which is a categorical variable with three levels, indicating namely, whether the student is from the UK which was marked as “a” or from one of the members of the EU, marked as “b” or whether the student is an international student, marked as “c”. It also includes gender with three levels, namely male or “a”, female or “b” and prefers not to disclose or “c”. The age variable was also taken to be categorical, with three categories dividing age groups into 18 to 19 as “a”, 20 to 21 as “b” and over 21 as “c”. The variable accommodation type  was recorded as “a” for priory hall, “b” for self-catering halls of residence of the university, “c” for the university houses, “d” for privately rented accommodations, “e” for privately rented accommodation with meals included, “f” for parent’s home, “g” for own home and “h” for others

The data consists of 66.23% males, 29.8% females and the rest 3.97% refused to say. 15.23% were housed in priory hall, 15.89% were self-catering houses by University, 1.99% were in university house, 17.22% were in privately rented flats, 36.42% stayed in parent’s home, 6.62% stayed in own house and 6.62% in some other accommodation.

It is of interest to investigate how age, gender and domicile status plays a role in the kind of accommodation that a student of the university chooses.

It is seen that chance of males staying in their parent’s home is 32%. The chance of accommodation being self-catering university houses for males overall is 20% and that being priory hall is 17% .The probability is 16% for privately rented home. The situation for females in comparison is that 44.44% of the females stay in parent home which is the most popular option for the group. Next most probable option used by females is privately rented flats with 17.78% followed by priory halls with 13.33%. Among the student who failed to identify gender, probability of staying with parents was 50% followed by living on rent being 33.33% and living in own house is 16.67%.

## Data description

Among the 18 to 19 year olds of UK, the preference was seen to be equally probable for Priory hall and university self-catering halls with probability 26.47% followed by 17.65% residing in parent’s home and 14.71% in privately rented house. 100% of the 20 to 21 year olds of UK resided in parent’s home and 43.75% of those above 21 years resided there as well. Chance that a above 21 years old from UK resides in priory hall is 25% followed by 18.75% for the one preferring private flats. Among those students who are from the EU, the parent’s home was seen to be most preferred with 51.61% chance followed by 16.13% residing in priory hall. The preference was equally shared with 25% incidence each by self-catering halls, privately rented flats, parent’s home or some other options among the 20 to 21 year olds from EU. The chance of residence being parent’s home was 41.81% and privately rented flat was 23.53% among the over 21 year olds from EU. The chance that a 20 to 21 year old International is staying in parent’s home is 66.67% .Otherwise they are mainly seen to live in rented flats with chance of 16.67% or in some other accommodation. The chance that an International student aged more than 21 years lives in rented flat was again computed to be 41.67% followed by chance of staying with parents as 25% and staying at own home and staying in self catering university house being equally probable with 16.67%. The 18 to 19 year old international students though seem to be more diverse in accommodation choice with 25.57% percent staying with parents, 25% staying in rented flats and 3.57% staying in University houses even.

Conclusion

University houses are seen to be least popular with only those who are 18 to 19 year old and not UK residents choosing that option. Most students are seen to live with parents be it from any domicile category. The females seem to prefer privacy in accommodation more with higher chance of choosing parent’s home or private rented flat rather than accommodations which are shared over the males. UK students aged 20 to 21 are surely found  to live with parents although the chance drops when they turn 21. For International students aged below21 years, they are found to mostly prefer parent’s houses although the preference shifts to rented houses after they turn 21 years. EU students over 21 however seem to favor parent’s home.

The following table shows the output from the Regression analysis done using Excel. The equation for the model as per the coefficients was found to be:

Time spent on the internet =110.527 – 0.539 Time to University ( i.e., Commuting time)

 Coefficients Standard Error t Stat P-value Intercept 110.5278575 9.593198545 11.52148 2.31E-22 Time To University -0.539369708 0.191406158 -2.81793 0.00549

The slope of the regression equation is therefore equal to -0.539. This means that with each unit increase in the time taken to commute to university, the amount of time spent on the internet decreases by 0.539 units. That is these two are linearly but negatively related.

The intercept of the equation is 110.527. This means that when the time taken to commute to the university is zero, the expected amount to time spent by the student on the internet is 110.527 units.

The following table shows the regression statistics for the regression analysis done in Excel. The R squared value was found to be 0.05. This is the proportion of variation explained by the regression as compared to the total variation. In effect it is a measure of the goodness of fit of the equation, The low value suggests that time to commute to university alone is not sufficient to explain time spent on the internet in a linear fashion at least. There may be other factors that have not been considered or the relationship is not really linear in reality or both.

 Regression Statistics Multiple R 0.22493798 R Square 0.050597095 Adjusted R Square 0.044225263 Standard Error 74.59769367 Observations 151
1. The network diagram for the project is given below:
2. The required precedence diagram for this project is given as follows:
3. Thus, the critical path that has to be followed is listed as follows:
• A: Agree event outline
• B: Identify suitable marketing companies
• E: Agree detailed brief for event
• F: Selecting marketing company
• H: Agree content of marketing event
• I: Prepare content for marketing event
• K: Final briefing to staff briefing prior to launch
• L: Launch marketing campaign
1. The float associated with each activity is given in the following table:
 Activity EST LST TF A 0 0 0 B 5 5 0 C 8 12 4 D 5 7 2 E 8 8 0 F 14 14 0 G 12 14 2 H 16 16 0 I 18 18 0 J 14 22 8 K 24 24 0 L 26 26 0
1. The complete duration of the project = Duration of the activities in the Critical Path

= (5 + 3 + 6 + 2 + 2 + 6 + 2 + 0) days

= 26 days

Cite This Work

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[Accessed 25 February 2024].

My Assignment Help. 'Essay: Analysis Of Survey Data Of Coventry University Students.' (My Assignment Help, 2021) <https://myassignmenthelp.com/free-samples/107sam-business-and-management-statistics/faculty-of-business-and-law.html> accessed 25 February 2024.

My Assignment Help. Essay: Analysis Of Survey Data Of Coventry University Students. [Internet]. My Assignment Help. 2021 [cited 25 February 2024]. Available from: https://myassignmenthelp.com/free-samples/107sam-business-and-management-statistics/faculty-of-business-and-law.html.

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