Our website is a unique platform where students can share their papers in a matter of giving an example of the work to be done. If you find papers
matching your topic, you may use them only as an example of work. This is 100% legal. You may not submit downloaded papers as your own, that is cheating. Also you
should remember, that this work was alredy submitted once by a student who originally wrote it.
This assignment "Research Methods for Business" focuses on the evidence of an association between occupational status and the number of visits to the Gymnasium during the past four weeks and the evidence that the average customer satisfaction score is age-dependent. …
Download full paperFile format: .doc, available for editing
Research Methods for Business College: Question Is there any evidence of an association between occupational status and the number of visits to the Gymnasium during the past four weeks? Carry out an appropriate hypothesis test
In this regards, we will use correlation to test the relationship between the two groups.
According to the table below (Appendix 1), the P value is 0.05 and it is less than 0.05.This implies that we fail to reject the null hypothesis that there is an association between the two groups. The correlation is negative and weak (-0.177) we then conclude that the evidence that an association between occupational status and the number of visits to the Gymnasium do exist.
Correlations
Occupation
Visits
Occupation
Pearson Correlation
1
-.177*
Sig. (2-tailed)
.015
N
188
188
Visits
Pearson Correlation
-.177*
1
Sig. (2-tailed)
.015
N
188
188
*. Correlation is significant at the 0.05 level (2-tailed).
Question 2)
Is there any evidence that the average customer satisfaction score is age-dependent? Carry out an appropriate hypothesis test.
In this case, we will use regression analysis to see if the average customer satisfaction is dependent on the age. Basing on the analysis as shown below (Appendix 2), it is clearly evident that the customer satisfaction is not dependent on the age. This is shown by the p value (0.813) which is greater than 0.05.Hence in this case we reject the null hypothesis. Therefore, we conclude that the average customer satisfaction is not dependent on the age.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.017a
.000
-.005
1.066
a. Predictors: (Constant), Age
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
3.686
.175
21.035
.000
Age
.021
.090
.017
.237
.813
a. Dependent Variable: satsfaction level
Question 3)
The Gymnasium management have calculated that a monthly membership fee of at least £75 will be required to cover the investment and running costs of the upgraded Gymnasium. Test the null hypothesis that the mean willingness-to-pay for membership of the upgraded Gymnasium is at least £75, against the one-sided alternative hypothesis that the mean willingness to pay is less than £75.
The appropriate test for this question is one sample t test. According to the results as shown below (Appendix 3), the calculated t value is greater than the critical value. Therefore, we reject the null hypothesis and conclude that the mean willingness to pay is less than £75.The P value is less than 0.05 alpha. This means that the calculated t value is statistically significant.
One-Sample Test
Test Value = 75
t
df
Sig. (2-tailed)
Mean Difference
95% Confidence Interval of the Difference
Lower
Upper
Monetary
-4.440
187
.000
-5.947
-8.59
-3.30
Question 4)
Test whether there is any evidence of a difference between the willingness-to-pay for membership of the upgraded Gymnasium of male and female customers.
Independent sample t test is the appropriate test to test whether there is any evidence of a difference in the willingness-to- pay for membership between male and female customers. According to the results below (Appendix 4), it is clearly evident that there is a difference in the willingness of the paying of the membership between men and women. The p value is less than 0.05; this means that the calculated t value (4.824) is statistically significant. The calculated t value is greater than the critical value. This means that we reject the null hypothesis Therefore we conclude that there is enough evidence that there is a difference in the willingness-to- pay for membership between male and female customers.
Group Statistics
Genda
N
Mean
Std. Deviation
Std. Error Mean
Monetary
0
129
64.50
15.220
1.340
1
59
79.02
20.705
2.696
Independent Samples Test
Levenes Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
Monetary
Equal variances assumed
19.826
.000
-5.397
186
.000
-14.521
2.691
-19.829
-9.213
Equal variances not assumed
-4.824
87.782
.000
-14.521
3.010
-20.503
-8.538
Question 5)
Estimate a two-variable linear regression to describe the relationship between household weekly net income and willingness-to-pay for membership of the upgraded Gymnasium. According to this regression, what is the estimated willingness-to-pay of a customer with a household weekly net income of £300?
According to the analysis results as shown below (Appendix 5), the model is statistically significant since the p value is less than 0.05. The R squared is 0.368
The model equation is
Willingness-to-pay of a customer = (0.36) weekly net income +58.72
Using the above equation, we can estimate willingness-to-pay of a customer as
0.36x300+58.72
=166.72.
Therefore the estimate is 166.72.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.368a
.135
.131
17.122
a. Predictors: (Constant), Weekly Income
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
58.721
2.287
25.676
.000
Weekly Income
.036
.007
.368
5.393
.000
a. Dependent Variable: Monetary
Question 6)
Estimate a multiple regression in which willingness-to-pay for membership of the upgraded Gymnasium is explained by the following characteristics: gender (male/female), occupational status, and household weekly net income.
Basing on the analysis results shown below (Appendix 6), the model equation is as follows:
willingness-to-pay for membership=13.506( gender) – 1.796 (occupational status) + 0.026(weekly net income)+ 61.862
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.515a
.265
.253
15.866
a. Predictors: (Constant), Weekly Income, Gender, Occupation
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
16737.045
3
5579.015
22.164
.000b
Residual
46316.423
184
251.720
Total
63053.468
187
a. Dependent Variable: Monetary
b. Predictors: (Constant), Weekly Income, Gender, Occupation
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
61.862
4.862
12.722
.000
Gender
13.506
2.501
.342
5.400
.000
Occupation
-1.796
1.132
-.132
-1.587
.114
Weekly Income
.026
.008
.261
3.135
.002
a. Dependent Variable: Monetary
i)
Comment on the significance of each of these characteristics as determinants of willingness-to-pay.
According to the results output, it is observed that gender and weekly income have a positive influence on the willingness-to-pay for membership. Gender has a coefficient of 13.506 while weekly income has 0.26.In this case; gender has a bigger influence as compared to the weekly income. On the other hand, the occupation has a negative influence on the willingness-to-pay. The coefficient is -1.796.Both gender and weekly income coefficients are statistically significant because the p value is less than 0.05.On the other hand, the coefficient of occupation is not statistically significant, this is because its p value is greater than 0.05.In this case it does not suite the model and is required to be eliminated.
ii)
Comment briefly on the comparison between the multiple regression estimated in Q6 and the two-variable linear regression estimated in Q5.
For the multiple regression in Q6 has the weekly income coefficient which is lower than that in the linear regression in question 5.The probable reason for this could be because of co-linearity between the variables that resulted to the value being lowered.
iii)
What is the estimated willingness-to-pay of a male self-employed customer with a household weekly net income of £500?
Willingness-to-pay for membership=13.506( gender) – 1.796 (occupational status) + 0.026(weekly net income)+ 61.862
As seen above that the occupation does not fit in the equation, we must remove it from the model and the final model equation is
Willingness-to-pay for membership=13.506(gender) + 0.026(weekly net income)+ 61.862
Therefore the estimate of willing to pay by a male is
Willingness-to-pay for membership by a self employed male =13.506+0.026X500+61.862
=88.368
iv)
What is the estimated willingness-to-pay of a female employed customer with a household weekly net income of £400?
The following equation is used
Willingness-to-pay for membership=13.506(gender) + 0.026(weekly net income) + 61.862
Willingness-to-pay for membership=13.506+400X0.026+61.862
=85.768
Reference
Dey, I. (1993).Quantitative Data Analysis: A User-friendly Guide for Social Scientists. London, New York: Routledge.
Appendix 1
CORRELATIONS
/VARIABLES=q3 q5
/PRINT=TWOTAIL NOSIG
/MISSING=PAIRWISE.
Correlation
Correlations
Occupation
Visits
Occupation
Pearson Correlation
1
-.177*
Sig. (2-tailed)
.015
N
188
188
Visits
Pearson Correlation
-.177*
1
Sig. (2-tailed)
.015
N
188
188
*. Correlation is significant at the 0.05 level (2-tailed).
Appendix 2
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT q7
/METHOD=ENTER q2.
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
Ageb
.
Enter
a. Dependent Variable: satsfaction level
b. All requested variables entered.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.017a
.000
-.005
1.066
a. Predictors: (Constant), Age
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
.064
1
.064
.056
.813b
Residual
211.553
186
1.137
Total
211.617
187
a. Dependent Variable: satsfaction level
b. Predictors: (Constant), Age
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
3.686
.175
21.035
.000
Age
.021
.090
.017
.237
.813
a. Dependent Variable: satsfaction level
Appendix 3
T-TEST
/TESTVAL=75
/MISSING=ANALYSIS
/VARIABLES=q6
/CRITERIA=CI(.95).
One-Sample Statistics
N
Mean
Std. Deviation
Std. Error Mean
Monetary
188
69.05
18.363
1.339
One-Sample Test
Test Value = 75
t
df
Sig. (2-tailed)
Mean Difference
95% Confidence Interval of the Difference
Lower
Upper
Monetary
-4.440
187
.000
-5.947
-8.59
-3.30
Appendix 4
T-TEST GROUPS=q1(0 1)
/MISSING=ANALYSIS
/VARIABLES=q6
/CRITERIA=CI(.95).
Group Statistics
Genda
N
Mean
Std. Deviation
Std. Error Mean
Monetary
0
129
64.50
15.220
1.340
1
59
79.02
20.705
2.696
Independent Samples Test
Levenes Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
Monetary
Equal variances assumed
19.826
.000
-5.397
186
.000
-14.521
2.691
-19.829
-9.213
Equal variances not assumed
-4.824
87.782
.000
-14.521
3.010
-20.503
-8.538
Appendix 5
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT q6
/METHOD=ENTER q4.
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
Weekly Incomeb
.
Enter
a. Dependent Variable: Monetary
b. All requested variables entered.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.368a
.135
.131
17.122
a. Predictors: (Constant), Weekly Income
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
8525.723
1
8525.723
29.082
.000b
Residual
54527.745
186
293.160
Total
63053.468
187
a. Dependent Variable: Monetary
b. Predictors: (Constant), Weekly Income
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
58.721
2.287
25.676
.000
Weekly Income
.036
.007
.368
5.393
.000
a. Dependent Variable: Monetary
Appendix 6
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT q6
/METHOD=ENTER q1 q3 q4.
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
Weekly Income, Genda, Occupationb
.
Enter
a. Dependent Variable: Monetary
b. All requested variables entered.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.515a
.265
.253
15.866
a. Predictors: (Constant), Weekly Income, Genda, Occupation
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
16737.045
3
5579.015
22.164
.000b
Residual
46316.423
184
251.720
Total
63053.468
187
a. Dependent Variable: Monetary
b. Predictors: (Constant), Weekly Income, Genda, Occupation
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
61.862
4.862
12.722
.000
Genda
13.506
2.501
.342
5.400
.000
Occupation
-1.796
1.132
-.132
-1.587
.114
Weekly Income
.026
.008
.261
3.135
.002
a. Dependent Variable: Monetary
Read
More
Share:
CHECK THESE SAMPLES OF Research Methods for Business
The following book report "Research Methods for Business by Uma Sekaran" is focused on the processes of a research method in relation to business.... This trend has reduced this morale towards working and consequently the business has lost substantial profits.... This paper intends to show how the problem at hand can be taken through the processes of research discussed in the following sections.... Theoretical framework and hypothesis development This chapter provides an exclusive description of a research process through a theoretical framework and hypothesis development....
Journal of Small business and Enterprise Development.... Matlay and Westhead (2005).... - Hussain Javed, Millman Cindy and Matlay Harry.... (2006) ‘SME Financing in the U.... and in China: a comparative perspective.... 584-599
... ... .... Deakin et al , Fraser et al , Westhead , Trucker and....
(2000) Research Methods for Business Students.... (2000) Research Methods for Business, A Skill-Building Approach.... (2003) business Research: A practical guide for undergraduate and postgraduate students.... sing a mixture of approaches and techniques has advantages, as all methods have pros and cons.... Using both methods will help ensure that the research outcome is accurate and this “will lead to greater confidence being placed in your conclusions” (Saunders & Lewis & Thornhill, 2000, p....
This will allow the officers to fill out the surveys when they are free and have time to spare without disturbing their daily business, along with the fact that they could provide anonymous answers and give their honest opinions about the industry.... You also wish to secure some information about what they like and do not like about life in the subdivision.
...
In the paper 'Research Methods for Business and Management,' the author provides data analysis, which was collected in the United Kingdom in the three member states of the UK; England, Scotland, and Wales.... The survey was about different issues affecting people of the UK.... ... ...
3: - The research methods have been well justified.... 3: - The research methods have been well justified.... research questions Qn The report follows the required structure in outlining and arguing its hypothesis.... 7: - The actual research has followed the outline set in the research proposal.... The actual research has followed this order.... 8: - The research projects have been well presented....
A writer of the paper "Research Methods for Business Students" outlines that objective of this market research would also be to understand people's perception towards the suburb and ways in which suburb has changed energy consumption pattern of respective residents.... The objective of this market research would also be to understand people's perception towards the suburb and ways in which suburb has changed energy consumption pattern of respective residents....
6 Pages(1500 words)Assignment
sponsored ads
Save Your Time for More Important Things
Let us write or edit the assignment on your topic
"Research Methods for Business"
with a personal 20% discount.