PACC6008 Business Decision Making

Table 1: Descriptive Statistics of GPA, Childrank & Smoker

From Table 1, we state that on average majority of the students in University A get a GPA score of about 3.24 and are second-born in their families. In addition, the analysis revealed that on average 1 student from University A smoke. Furthermore, the results reveals that majority of the students from the university are second-born in their families (represented by a mean of 2.30 which approximates to 2). The median of the three study variables are 3.3, 2, 1 for GPA scores, child rank and smoking status. Besides, the mode of the same variables are 3.4,1,1 which can be interpreted to mean the same as the means. In terms of skewness and kurtosis, the data for the three study variables i.e. GPA scores, Child rank and smoking status can be interpreted to skewed to the left for GPA scores of students (negatively skewed to the left of the mean which is 3) and skewed to the right for child rank and smoking status (Positively skewed to the left of the mean which is 3) (Lee, Lee, Chang, & Tai, 2016). The range of the three study variables which is the difference between the minimum and the maximum values (Lee, Lee, Chang, & Tai, 2016), can be summarized as 3 for student’s GPA scores, 6 for child rank and 1 for smoking status. In terms of the maximum and minimum values, students’ GPA score has a maximum of 4 and a minimum of 1 indicating that the highest scoring students in terms of exams done in 2018 had a GPA score of 4 while the least scoring had a GPA of 1. In terms of child rank position in the family, the maximum value is 7 and the minimum is 1 indicating that the highest recorded value in terms of child rank in a family is 7 and the lower is 1. In terms of smoking, the maximum value is 2 and the minimum is 1; symbolizing those students who smoke and those who do not smoke. In terms of participation rate, out of the interviewed students, 274 answered the question about GPA scores (6 never participated), 1ll of them answered the question on child rank and 276 answered the question on smoking status (4 did not participate.)

Given that the population of the students who were living in urban in 2017 was 0.15 of the total population of students in 2018(assuming the same population of 280 students), it can be concluded that 42 students were living in urban in 2017.

• The best test to conduct is  t-Test: Two-Sample Assuming Equal Variances analysis
• The null hypothesis of this part can be stated as, “There is a significant difference in population of students living in urban in 2017 and 2018.”  The alternative hypothesis can be as well stated as, “There is no significant difference in population of students living in urban in 2017 and 2018.”
• Test and report

From Table 2 above, the mean of the model is 1.96 for students of university A who lived in urban area in 2018 with a variance of 0.32 (to two decimal places) and 1.99 for students who lived in urban area in 20187 with a variance of 0.38 (to two decimal places). From the analysis also, it can be observed that t Critical two-tail value is 1.967382 which is more than the p-value (0.05), hence we accept the null hypothesis and reject the alternative hypothesis i.e. there is a difference in population of students living in urban in 2017 and 2018 and reject the alternative hypothesis that reads, “There is no significant difference in population of students living in urban in 2017 and 2018.”This can be interpreted to mean that the characteristics of the population of students in 2017 are not the same as their peers in 2018.

2.The population is 50 apartments each (confirm the population of apartments in both suburbs as you collected the data)

From the analysis, the average rent for apartments in Suburb 1 is $ 335.8 and $379.6 for apartments in suburb 2, at 95 confidence level. Thus, we can concluded that the average rent in Suburb 1 is lower than that in Suburb 2.

The hypothesized mean difference of this model is 450. The null hypothesis was earlier stated as, “the average rent in Suburb 1 (or Suburb 2) is not equal or higher than $450 per week.” From the analysis, the t Critical two-tail is 1.99 (to 2 decimal places) which is greater than the P-value, 0.05, indicating that this model is not statistically significant. Thus, we can reject the null hypothesis of this model accept the alternative hypothesis, “the average rent in Suburb 1 (or Suburb 2) is equal or lower than $450 per week”. Thus, we can conclude that the average rent in Suburb 1 and 2 is average rent in Suburb 1 (or Suburb 2) is not significantly higher than $450 per week.

References

Lee, C., Lee, J., Chang, J., & Tai, T. (2016). Professional Techniques Used in Excel and Excel VBA Techniques. Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses, 7(4), 763-800. doi:10.1007/978-3-319-38867-0_24

Lee, C., Lee, J., Chang, J., & Tai, T. (2016). Statistical Decision Theory. Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses, 9(2), 685-698. doi:10.1007/978-3-319-38867-0