Research Method SPSS Report - Essay Example

ASB-2410 RESEARCH METHODS FOR BUSINESS ASSIGNMENT - PART 2 Question Answer In order to test this, we run a linear correlation analysis between the occupational status and the number of visits to the Gymnasium. The hypothesis is “As the occupational status rises, the number of visits to the gymnasium also increases”. The output of the analysis is shown below:The coefficient of linear relationship between Q3 and Q5 is - 0.026 by Kendall’s tau_b and -0.030 by Spearman’s Correlation. This means that there is a negative linear correlation between the two variables.

It implies that the rise in occupational status reduces the number of visits. Consequently, the employed people are able to visit the gymnasium more than those who are unemployed. This means that the hypothesis was not true. Question 2: AnswerThe hypothesis here is that the average customer satisfaction increases with the increase in age groups. We therefore run the linear correlation analysis using Q2 and Q7. The results are as shown below:The coefficient of linear relationship between Q2 and Q7 is - 0.

149 by Kendall’s tau_b and -0.172 by Spearman’s Correlation. This means that there is a negative linear correlation between the two variables. It implies that the increase in age groups reduces the level of customer satisfaction. Consequently, the people in the low age group are more satisfied compared to people in the older groups. The hypothesis is not true. Question 3: AnswerIn this hypothesis test, we conduct a one-sample t-test on Q6 OutputSince £75 is greater than the highest mean. On the other hand, the mean willingness of the people to contribute The frequencies of contributions from £75 and above are listed below:5.91.11.11.15.30.53.22.71.611.23.72.71.

6Total Percentages41.7 The One sample T test reveals the lowest mean to be 69.3281 while the highest mean is 74.8644. The mean willingness of the people to pay is 41.7 out of 186 = 22.42%.The willingness to pay less than £75 = 100 – 22.42 = 77.58%Question 4: AnswerIn this, we conduct a linear correlation analysis between the willingness to pay (Q6) and the gender group (Q2). The males are willing to pay less than their female counterparts are.There is no difference between the willingness to pay and the gender because there is a positive correlation coefficient of 0.

308 between the two variables Q2 and Q6. Question 5: AnswerThe results for linear regression between the household income and the willingness to pay are presented below: http://www.academia-research.com/filecache/instr/f/f/1039415_ff_500313392.xlsxThe linear regression gives a linear regression value of 0.042. It shows that the willingness to pay increases with the increase in the weekly income in the ratio of 0.042. For a person earning a net income of £300, he or she falls is willing to pay between 70 and 75 from the reading on the plotted output.

Question 6: AnswerWe run a multivariate linear regression with the willingness to pay as the dependent variation. The output is shown below:i. Question 6 Part (i): AnswerThe significance of Q6 (willingness to contribute) is 0.54, Q1 (Gender) = 0.778, Q3 (Occupational Status) = 0.00 and Q4 (Weekly Income) = 0.671. It means that the most significant variable that contributed to the willingness to pay is Gender, followed by Weekly income. The occupational status was not significant at all to the willingness to pay. ii. Question 6 Part (ii): AnswerThe multiple regressions shows that the significance of the weekly income to the willingness to pay is 0.

671while the bivariate linear regression shows a lower coefficient of value of 0.042. There is a wide variance between the two calculations.iii. Question 6 Part (iii): AnswerFrom the graph, a person earning £500 is willing to contribute £115iv. Question 6 Part (iv): AnswerFrom the graph, a person earning £400 is willing to contribute £93.