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## Analysis

This paper is designed in understanding the demographic distribution of the students in Australia. Also, the report will evaluate students’ perception of environmental issues. Both nominal and ratio scale data will be collected. For instance, the height of the students (without shoes), number of hours slept, among others. These are vital as they help in giving a descriptive overview of the sample, which can help understanding the characteristics of the population. On the other hand, the nominal scale data are vital in understanding the most profound.

In this section, the analysis will be subdivided into two parts, in which the analysis of two states will be performed. A comparison will be carried out to assess how the student from VIC and NSW use different strategies to conserve the environment. The analysis will be performed by Excel Spreadsheet.

The distribution of gender is as illustrated in Table 1.

Table 1: Gender distribution

 Row Labels Count of Q2 Gender F 52.50% M 45.00% O 2.50% Grand Total 100.00%

The summary indicates that the proportion of female students is slightly higher than that of male students. There is a very low chance of getting a student with a gender of “other.”

Figure 1: Gender distribution

Figure 1 shows that there is a higher chance of randomly selecting a female student (52.50%) than the male (45.00%) and others (2.50%) (Keller, 2014).

The average height of student was computed and is as summarized below.

 Row Labels Average of Q3           Height (cm) StdDev of Q3           Height (cm) F 165.5714286 10.1517064 M 157.8888889 14.06660162 O 164 #DIV/0! Grand Total 162.075 12.40427449

The summary of descriptive statistics indicates that the female student’s height (165.57 cm) is slightly higher than that of male students (157.89 cm). However, the standard deviation of the male student is higher than that of female students suggesting that the male student’s height is not consistency like that of female students.

An assessment was carried out to determine time spent with family. The results are as summarized.

 Row Labels Average of Q9        Doing things with family F 10.76190476 M 14.55555556 O 2 Grand Total 12.25

The summary indicates that on average male students spent more time with their families (14 .56 hours) than the female students (10.76 hours). This can be illustrated in the chart below.

The chart shows that the male student spent the highest time with their families doing something, whereas female spent less time (Keller, 2014).

It was evaluated the time spent doing house chores by each gender, and the summary is as follows.

 Row Labels Average of Q9       House Chores F 4.333333333 M 3.722222222 O 2 Grand Total 4

On average, female students spent 4.33 hours doing house chore, which is higher compared to the male students who spent 3.72 hours.

Environment

I evaluated the proportion of students that responded on installing a water tank as a measure to conserve the environment. The summary of this by gender is as illustrated below.

 Count of Q13.       Installed a water tank Column Labels Row Labels No Yes Grand Total F 30.0% 22.5% 52.5% M 30.0% 15.0% 45.0% O 2.5% 0.0% 2.5% Grand Total 62.5% 37.5% 100.0%

## VIC State Data Analysis

The summary shows that the proportion of students that did not install water take for both male and female is equal. That is, they have equal percentages (30.0%). On the other hand, 22.5% of the female compared to 15.0% have installed a water tank. This shows that in VIC state, more female students install a water tank to conserve water.

A similar analysis was carried out to determine the proportion of students that power off the main switch as a measure of conserving the energy.

 Count of Q13      Powered off at main switch Column Labels Row Labels No Yes Grand Total F 30.00% 22.50% 52.50% M 25.00% 20.00% 45.00% O 0.00% 2.50% 2.50% Grand Total 55.00% 45.00% 100.00%

The summary shows that 30% of the female students do not power off the main switch to conserve the energy compared to 25% male students. On the other hand, 22.5% of the female power off the main switch compared to 20.0% of the male students (Keller, 2014).

A simple linear model was fitted to determine whether there is a relationship between the number of hours spent engaged in paid work per week and the amount of money received per week. The model summary and coefficient are as summarized below. The hypothesis tested is H­0: there is no relationship between the number of hours spent engaged in paid work per week and the amount of money received per week. Versus HA: there is a relationship between the number of hours spent engaged in paid work per week and the amount of money received per week.

 Summary Output Regression Statistics Multiple R 0.220571 R Square 0.048652 Adjusted R Square 0.023616 Standard Error 81.64305 Observations 40
 ANOVA df SS MS F Significance F Regression 1 12953.3 12953.3 1.94331 0.171408 Residual 38 253292.3 6665.587 Total 39 266245.6 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 36.68955 14.10106 2.6019 0.013143 8.143443 65.23566298 Q9.     engaged in paid work 4.722655 3.38778 1.394026 0.171408 -2.13555 11.58085777

The results show that we should fail to reject the null hypothesis (p = 0.171) (Montgomery, Peck, & Vining, 2012). This means that there is no significant association between the number of hours spent engaged in paid work per week and the amount of money received per week.

An assess met was carried out to determine the distribution of gender in the NSW sample data.

Table 2: Gender distribution

 Row Labels Count of Q2 Gender F 57.50% M 42.50% Grand Total 100.00%

The summary shows that the proportion of males is lower than that of the female students in VIC state. That is, there is a 42.50 % chance of randomly selecting a male student whereas there is a 57.50 % chance of randomly selecting a female student (Keller, 2014). This distribution is as illustrated below.

Figure 2: Gender distribution

The chart indicates that there is a higher number of female students than the male students.

Second, an assessment of descriptive statistics of the height of students by gender.

 Row Labels Average of Q3           Height (cm) StdDev of Q3           Height (cm) F 155.4347826 12.54383618 M 159.8823529 13.94579844 Grand Total 157.325 13.17220753

The summary indicates that the male students are taller (159.88 cm) than the female students who are 155.43 cm. The male students show larger deviation as they have a higher standard deviation.

## NSW Data Analysis

An analysis was carried out to evaluate the average time students spend with their families, and the summary of the results is as follows.

 Row Labels Average of Q9        Doing things with family F 12.30434783 M 11.17647059 Grand Total 11.825

On average, females spent 12.30 hours  ith their families than the male students who spent 11.18 hours (Keller, 2014).

The chart indicates that on average the female students spend more time with their family doing other things than the male students.

An assessment was carried to assess how gender spends time with their families.

 Row Labels Average of Q9       House Chores F 7 M 7.294117647 Grand Total 7.125

The results show that the female students spend slightly less time with doing house chores (7.0 hours) than male students who spend 7.29 hours.

The male student in NSW city spends more time doing house chores than the female students.

 Count of Q13.       Installed a water tank Column Labels Row Labels No Yes Grand Total F 27.50% 30.00% 57.50% M 22.50% 20.00% 42.50% Grand Total 50.00% 50.00% 100.00%

The summary indicates that females are more insensitive on installing a water tank (27.50%) than the male student (22.50%) (Heiberger & Holland, 2015). On the other hand, the female also more sensitive about installing a water tank as a measure of saving water.

This illustrates that females are more insensitive to water saving.

A simple linear regression model was fitted to determine whether there is an association between the number of hours spent engaged in paid work per week and the amount of money received per week. The results of the model are as follows and the hypothesis tested is H­0: there is no relationship between the number of hours spent engaged in paid work per week and the amount of money received per week. Versus HA: there is a relationship between the number of hours spent engaged in paid work per week and the amount of money received per week

 Summary Output Regression Statistics Multiple R 0.04607618 R Square 0.002123014 Adjusted R Square -0.024136906 Standard Error 92.75672975 Observations 40
 ANOVA df SS MS F Significance F Regression 1 695.5853 695.5853 0.080846 0.777698 Residual 38 326944.8 8603.811 Total 39 327640.4 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 44.81967015 16.88723 2.654057 0.011549 10.63326 79.00608 Q9.     engaged in paid work 0.528962188 1.860351 0.284335 0.777698 -3.23712 4.295046

The summary indicates that there is no sufficient evidence to reject the null hypothesis (p = 0.778) (Heiberger & Holland, 2015). This means that there is no association between the number of hours spent engaged in paid work per week and the amount of money received per week. In particular, the coefficient of determination can explain only 0.21% sources of variation, meaning 99.79% sources cannot be explained.

The summary of the distribution of gender in both states is similar, where the number of female students is higher than that of male students. On the other hand, the male students in NSW are taller than the female unlike in the VIC state where female students are taller. In both cities, male spend less time with their families, as compared to female students. Unlike in the VIC (4 hours) students in NSW spend more time doing family chores with an average of 7.125 hours. Also, the male students in NSW spent slightly more time than female student which is opposite in VIC state. In both cases, the fitted regression model was not significant. This means that the between the number of hours spent engaged in paid work per week in both cities are not good predictors of the amount of money received per week.

Conclusion

The results indicate that the between the number of hours spent engaged in paid work per week is not associated with the amount of money received per week. Therefore, this means that the number of hours engaged in paid work cannot be used as a determinant of income or money received by students. The research indicated that female students in both cities are more sensitive than the male students in Installing a water tank as a measure of conserving water. Therefore, there is a need to enlighten the male students on the need to conserve the water.

References

Barton, M., Yeatts, P. E., Henson, R. K., & Martin, S. B. (2016). Moving beyond univariate post-hoc testing in exercise science: A primer on descriptive discriminate analysis. Research quarterly for exercise and sport, 87(4), 365-375.

Heiberger, R. M., & Holland, B. (2015). Multiple Regression—Regression Diagnostics. Statistical Analysis and Data Display, 345-375.

Keller, G. (2014). Statistics for management and economics. Nelson Education.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis. 821. John Wiley & Sons.

Cite This Work

My Assignment Help. (2022). Essay: Demographic Distribution Of Students In Australia And Their Perception Of Environmental Issues.. Retrieved from https://myassignmenthelp.com/free-samples/corpfin7033-quantitative-methods/statistical-analysis-and-data-file-A99817.html.

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[Accessed 15 April 2024].

My Assignment Help. 'Essay: Demographic Distribution Of Students In Australia And Their Perception Of Environmental Issues.' (My Assignment Help, 2022) <https://myassignmenthelp.com/free-samples/corpfin7033-quantitative-methods/statistical-analysis-and-data-file-A99817.html> accessed 15 April 2024.

My Assignment Help. Essay: Demographic Distribution Of Students In Australia And Their Perception Of Environmental Issues. [Internet]. My Assignment Help. 2022 [cited 15 April 2024]. Available from: https://myassignmenthelp.com/free-samples/corpfin7033-quantitative-methods/statistical-analysis-and-data-file-A99817.html.

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