Multivariate Data Analysis (Short computational exercise) - Essay Example

Essay, Other Multivariate Data Analysis (Short computational exercise) Question Answer From the results of the correlation analysis, there is a negative correlation analysis between the occupational status and the number of visits to the Gymnasium in the last 4 weeks. The coefficient of linear correlation is -0.077. This is a weak negative correlation based on the hypothesis that there is an association between the two variables.Fig 1: Results of Linear Correlation AnalysisQuestion 2: AnswerThe hypothesis is that there is an association between the age and the level of satisfaction.

From the linear correlation analysis, we run linear correlation between Q2 and Q7. The finding is that there is a strong negative correlation between the two variables, the age of the level of satisfaction. The coefficient of linear correlation was found to be -0.154.CorrelationsQ2Q7Q2Pearson Correlation1-.154*Sig. (2-tailed).021N225225Q7Pearson Correlation-.154*1Sig. (2-tailed).021N225225*. Correlation is significant at the 0.05 level (2-tailed).Fig 2: Linear Correlation ResultsQuestion 3: AnswerQ6FrequencyPercentValid PercentCumulative PercentValid504620.420.420.4522.9.921.3531.4.421.8541.4.422.2553113.813.836.0562.9.936.9571.4.437.3582.9.938.2592.9.939.160208.98.948.0611.4.448.4621.4.448.9631.4.449.36573.13.152.4671.4.452.9681.4.453.370146.26.259.6711.4.460.07583.63.663.6762.9.964.4771.4.464.98031.31.366.2831.4.466.78562.72.769.3861.4.469.8871.4.470.29041.81.872.0932.9.972.9941.4.473.39552.22.275.6991.4.476.01003113.813.889.8105135.85.895.611052.22.297.811531.31.399.11601.4.499.63201.4.4100.

0Total225100.0100.0Fig 3: Descriptive Statistics of the willingness to payFrom the frequency descriptions, the number that is willing to give at least £75 is 39.7%. The hypothesis therefore, scores 39.7% positive response. The remaining percentage (60.3%) is the score for the willingness to pay less than £75.Question 4: AnswerThe hypothesis is that there is no association between gender aspect and the willingness to pay. We run a linear correlation analysis on the two variables Q1 and Q6. The findings are as follows:CorrelationsQ1Q6Q1Pearson Correlation1.394**Sig. (2-tailed).

000N225225Q6Pearson Correlation.394**1Sig. (2-tailed).000N225225**. Correlation is significant at the 0.01 level (2-tailed).Fig 4: Gender influence on the willingness to payThere is a positive linear correlation coefficient of 0.394. This indicates that the gender group positively influences the willingness to pay is positively influenced by the gender. It therefore leads to a conclusion that females are willing to pay more than the males. This is against the hypothesis.Question 5: AnswerThe hypothesis is that the net weekly income positively influences the willingness to pay.

We run a linear correlation analysis using two variables CorrelationsQ4Q6Q4Pearson Correlation1.492**Sig. (2-tailed).000N225225Q6Pearson Correlation.492**1Sig. (2-tailed).000N225225**. Correlation is significant at the 0.01 level (2-tailed).Fig 5: Influence of Net Income on the Willingness to payThe coefficient of linear correlation between the two variables is 0.492. This is a strong positive linear correlation, indicating that the willingness to pay increases as the net income per week increases, as per the hypothesis.

Fig 6: Willingness to pay against Net IncomeThe gradient of the plotted line is 0.242. A person earning £300 will be willing to pay 300 * 0.242 = £72.Question 6: AnswerFig 7: Multivariate Linear Regression(i) The significance of gender (Q1) in the multiple regressions is 0. It shows that the regression model did not consider the gender factor.Fig 7: Gender and the Willingness to payFig 8: Occupational Status and willingness to payThe significance of the occupational Status (Q3) is 0.492. It was the most influential variable in the linear regression model.

Fig 9: Net weekly Income and the willingness to payThe net weekly income was of significance. The linear regression model did not make use of it.(ii) SimilarityThe multiple regressions and the two variable linear regression model give zero significance for the gender and net weekly income. DifferenceThe multiple regressions shows the three plots for net weekly income, occupational status and the gender factor in a single regression model. The two variable linear regression on the other hand shows a single plot between the dependent and independent variable.(iii). Net Weekly IncomeA person with weekly income of £500 is willingness £110.(iii). Net incomeiv.

A female earning weekly income of £400 is willingness £95.