The respective descriptive statistics related to the commutation distance for the variable student needs to be computed. For the students as a whole, the descriptive statistics related to commutation distance are pasted below.With regards to the first year undergraduate students, the descriptive statistics related to commutation distance are pasted below.With regards to the third year undergraduate students, the descriptive statistics related to commutation distance are pasted below
The bar chart with regards to the sensitivity of the UCC students in relation to carbon footprint is summarised in the following graph.From the above responses, it is apparent that the students at UCC do not tend to very sensitive with regards to carbon footprint with regards to commutation. In this regards, maximum responses comprise of those individuals who do not consider the carbon footprint. However, there are sizable number of students who tend to be quite sensitive about carbon environment while making the commutation choice. Also, there are students who sometimes tend to consider carbon footprint while making the commutation choices to the university.
The requisite pie charts are illustrated as follows.Based on the above pie-charts, it is apparent that a large proportion of students residing in rented accommodations tend to walk to the university. One of the possible reasons for the same is that these students would be renting near the university campus only so as to minimise the transportation cost and time involved. In sharp contrast, the mode of commutation for those who live with family is significantly more diverse with walking percentage a lot lower. This is not surprising as the respective houses of most students would not be in close vicinity to the university and hence walking would not be a viable choice.
The requisite histogram is as highlighted below.There does not seem to be any significant difference between the commutation distances between the students who participate or do not participate in UCC Clubs and Societies. The apparent difference between the two is on account of scale differences which once adjusted would result in similar distribution. Additionally, neither of the above distributions is normal considering the fact that right skew is present for both the distributions indicated above (Medhi, 2016).
The requisite steps in the given hypothesis testing are performed below.
Step 1: Defining the null hypothesis
H0: µBA=µBSc i.e. there is no significant difference in the average commuting distances between the B.A. students and B.Sc. students.
Step 2: State the alternative hypothesis
Ha: µBA≠µBSc i.e. there is significant difference in the average commuting distances between the B.A. students and B.Sc. students.
Step 3: The significance level for this test has been assumed as 5% which would be appropriate for the given hypothesis where higher accuracy would not be required.
Step 4: The relevant test statistic in the given case would be t statistics. This is because the given for the two samples i.e. commuting distance of B.A. students and commuting distance of B.Sc. students, the population standard deviation is not known. If the population standard deviation was known, then the relevant test statistics would have been z (Flick, 2015). Considering that there are two samples and both are independent of each other, hence a two sample independent t test would be conducted. Under this category, there are two options in the form of equal and unequal variance. Considering that the sample size of the two samples is not equal, hence unequal variance is assumed (Hair et. al., 2015). The value of the test statistic can be obtained from the excel output indicated as follows.
It is apparent from the above output that the t statistics has come out as 0.344.
Step 5: The critical values for the test need to be highlighted. The given test is a two tail test as the alternative hypothesis contains the “not equal to” sign. As a result, there would be two critical values, one at the lower end and the other at the upper end. From the above output, the t critical value at the upper end is 1.9655 and the corresponding value at the lower end would be -1.9655 (Hillier, 2016).
Step 6: The decision rule with regards to use of critical value is that if the test statistic lies within the interval defined by the critical values, then the null hypothesis would not be rejected and alternative hypothesis would not be accepted. In the given case, the computed t statistic is 0.344 which tends to lie between -1.9655 and 1.9655. As a result, the available evidence does not warrant null hypothesis rejection (Lieberman et. al., 2013). Hence, it can be concluded that there is no significant difference between the average commutating distance of B.A. and BSc students.
The research question that has been chosen for this analysis is as follows.
“Is there any relationship between the mode of transport used for commutating to the university and the level of students?”
The rationale for choosing the above research question was to critically analyse if the level of students tends to influence their choice of transport system. Thereby, the research question aims to explore the preferences of the two levels of students in the context of their commutation choices which then can be further analysed to explore the underlying reason along with the role of potentially other variables such as distance of commutation along with location.
The relevant statistical analysis would constitute of an inferential test as the underlying objective is to highlight the population parameter based on the given sample statistics. Hypothesis testing would be deployed and the appropriate test in this regards would be Chi-square test of independence. This test is appropriate as both the variables of interest have categorical measurement of the data and the underlying data type is non-numerical. Under the given situation, the chi square test statistics would be the most appropriate (Hastie, Tibshirani and Friedman, 2014).
The requisite hypotheses for this test are indicated below.
Null Hypothesis: There is no significant relationship between the commutation transport mode and level of course.
Alternative Hypothesis: There is significant relationship between the commutation transport mode and level of course.
The level of significance for the given hypothesis test has been assumed as 5%.
In order to compute the chi-square statistic, the first step is to indicate the actual frequency of the actual mode of transport divided in accordance with the level of student The next step is to use the above data for obtaining the expected frequencies of the usage of the various modes of commutation in accordance with the level. This is indicated below.Comparing the actual frequencies and expected frequencies, we can obtain the value of the test statistic i.e. chi-square which has been highlighted below.
The p value has been obtained considering the chi square test statistic (21.05) from the above computation along with the degrees of freedom (9-1)*(2-1) = 8. This p value has come out as 0.007 using Excel as an enabling tool.Comparing the p value obtained above with the level of significance (0.05), it is apparent that the available evidence is sufficient for causing rejection of the null hypothesis. As a result, the alternative hypothesis would be accepted (Hillier, 2016). The acceptance of alternative hypothesis indicates that the commutation mode and the level of students are inter-related.
The inter-relationship between the student level and the commutation means to the university ness to be further analysed considered the impact of distance from university which might be impacting the relationship that is observed in the given hypothesis testing. Also, any empirical support from existing literature needs to be explored with regards to offering explanation in this regards which would lend more clarity on the given relationship (Eriksson, and Kovalainen, 2015).
The objective of this section is to critically review the methodology and the underlying results obtained.
1) The questionnaire for the given survey worked real well. One of the key reasons for the same was that the questions were framed in an objective manner and the suitable options were presented to the respondents to choose from. This allowed the respondents to devote a lesser time to fill the survey and ensured that the responses were accurate owing to lack of underlying subjectivity in questions which is typical in open ended questions. With regards to improvements, it was noteworthy that very few comments were collects in any other comments. In the survey, mechanisms need to be incorporated so as to enhance the responses in this regards as it provides vital information that could be useful.
Another potential improvement is the questionnaire is to reduce the number of questions especially if there are questions which have limited relevance as the respondents highlighted concerns with regards to the length. Further, it would have been helpful if the respondents had been briefed before the survey with regards to the purpose of survey and also the various questions. This would potentially ensure in enhancing the accuracy of the data collected from the respondents. Besides, it would be a good idea to pass on the survey to the respondents and allow them some time to fill the same. This is especially critical in the context of certain information particularly the distance of commutation to the UCC which not everyone would be aware of with accuracy.
Another crucial aspect is the sample which has been used for acting as the respondent for the survey. This is critical considering the fact that if the sample is not representative of the population (i.e. students at UCC), then the reliability of the underlying results would be adversely impacted. Attempt had been made for the current survey to ensure that all the key attributes of the population were included in the given list of respondents which was randomly selected. However, going forward, it would make more sense to deploy stratified sampling which would result in a more representative sample and hence enhance the reliability of the trends observed.
2) One of the interesting aspects in relation to commutation trends is that for rented students, more than 75% tend to walk for commutating to the university. Hence, the key factor which tends to determine the choice of commutation mode is the distance from the university. Therefore, in order to reduce the carbon footprint, it is imperative that the UCC must invest in providing some hostel or residential facility either on the campus or near the campus. This should be especially lucrative to outstation students and also for those whose home is quite far from the campus.
Additionally, it may make sense for the UCC to encourage the students to use bicycles as a mode of commutation. This can be done by providing parking for the same inside the campus and not allowing any other vehicle inside the campus. Further, the university may also deliberating on plying buses if certain routes can be identified where maximum students can have assess and this would ensure that maximum students can travel together in the same mode of conveyance. The important aspect is to discourage individual car travel and hence car pooling forums should be available on the UCC student discussion so that the students can coordinate amongst themselves to reduce their transportation cost and also the carbon footprint in the process.
Out of the various choices suggested above, the most effective is likely to be one that involves providing residential facilities on or near the campus as it would be the most convenient and also ensure that the students would be able to save money.
References
Eriksson, P. and Kovalainen, A. (2015) Quantitative methods in business research. 3rd ed. London: Sage Publications.
Fehr, F. H. and Grossman, G. (2013). An introduction to sets, probability and hypothesis testing. 3rd ed. Ohio: Heath.
Flick, U. (2015) Introducing research methodology: A beginner’s guide to doing a research project. 4th ed. New York: Sage Publications.
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., and Page, M. J. (2015) Essentials of business research methods. 2nd ed. New York: Routledge.
Hastie, T., Tibshirani, R. and Friedman, J. (2014) The Elements of Statistical Learning. 4th ed. New York: Springer Publications.
Hillier, F. (2016) Introduction to Operations Research. 6th ed. New York: McGraw Hill Publications.
Lieberman, F. J., Nag, B., Hiller, F.S. and Basu, P. (2013) Introduction To Operations Research. 5th ed. New Delhi: Tata McGraw Hill Publishers.
Medhi, J. (2016) Statistical Methods: An Introductory Text. 4th ed. Sydney: New Age International.
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