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BUS501 Business Analytics And Statistics
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BUS501 Business Analytics And Statistics
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Course Code: BUS501
University: University Of The Sunshine Coast
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Country: Australia
Question:
Task instructions:
Using what you have learned in the lectures and the tutorials answer the main research questions below. Then consider the dataset and answer any other research questions you can identify that are meaningful to the CEO.
As guide – answer these main research questions:
1.Which product categories are making the most profit?
2.Which product category costs the most (COGS)?
Is there a difference in payments methods?
4.Are there any differences in the user groups on all of the customer attitudes? (6 outcomes)
5.Are there any differences in gender on all of the customer attitudes? (6 outcomes)
6.Are the customer attributes for female/male/light users/heavy user. Using neutral or average
Problem definition and business intelligence required :
oList each of the research questions and list what analytics methods you have chosen to answer each question and why. Use references to academic definitions of the statistics, such as textbooks and journal articles.
For example, if you used an ANOVA – list the research question, explain why an ANOVA was most appropriate by defining what an ANOVA is and does (use references to statistical textbooks and websites).
Results of the selected analytics methods and technical analysis:
oUse each research question as a heading
oPresent the relevant descriptive statistics through data visualization/ graphical displays (Tables, graphs, pie charts etc.).
oPresent the main analysis results and explain them.
oHigher marks for including extra research questions not defined in this document and creating nice tables/displays opposed to copy and pasting directly from SPSS output.
Discussion of the results and recommendations:
oBased on the analyses what conclusions about the business can be summarized/drawn and what recommendations can be offered to the CEO.
Answer:
Introduction
The business world has in the recent past been dogged by myriad of problems ranging from global inflation to high level competition. Industries have been growing big due to new entrants in the market. This has posed great challenges in in terms of competition. Due to this, various businesses have embraced various methods to keep their businesses afloat. This include having to introduce online platforms where customers can view and by goods online. Some companies have reverted to doing research about the market trends so as to understand the trends in the market and adapt.
Problem definition
Things have not been different for Retail Surge Company, an online company dealing in clothes and footwear for boys and girls and men and women. Retail Surge also deals in customisation, accessories and sports equipment. Due to low sales and dwindling profit margins, the company decided to conduct a market research by conducting data from customers with an aim of analysing it and drawing important information to guide their business.
The product category is made the most profit
To give information about the product category that made the most profit at Retail Surge, the appropriate bar graphs were employed to visually present the product and the amount of each.
Product category that cost the most
To give information about the product category that cost most, at Retail Surge, the appropriate bar graphs were employed to visually present the product and the amount of each.
Is there a difference in payment methods?
Since is a test for difference in means, an inferential statistics was employed. Independent t-test was employed since the test was being conducted between two variables. Test for difference between two independent variables use independent t-test. The two variables were the two methods of payments which were PayPal and credit card.
Are there any differences in the user groups on all of the customer attitudes? (6 outcomes)
Since is a test for difference in means, an inferential statistics was employed. Analysis of variance (ANOVA) was employed since the test was being conducted among more 3 variables. Test for difference between three independent variables uses ANOVA. The three variables were light users, medium users and heavy users.
Are there any differences in gender on all of the customer attitudes?
Since is a test for difference in means, an inferential statistics was employed. Independent t-test was employed since the test was being conducted between two variables. Test for difference between two independent variables use independent t-test. The two variables were the male and the females.
Analysis and results
Which product categories are making the most profit?
Figure 1
Figure 1 above is a bar chart showing the mean total profit of different products sold by Retail Surge. The product which fetched the highest profit was the customised products ($ 25). The second best product in terms of mean profit was men’s shoes ($15.5). The product that fetched the least profit was boys clothing.
Which product category costs the most (COGS)?
Figure 2
Figure 2 above is a bar chart showing the mean cost of goods at Retail Surge. The product which had the highest mean cost was the customised products ($9.9). The second costly product was girls’ shoes ($8). The product that was less costly was boys’ clothing.
Is there a difference in payments methods?
Figure 3
The box-plots above show that 95% of the time, the mean total purchases paid by PayPal was between around 3.15 while the mean total purchases paid by credit card was between 3.3 and 4.0.
Are there any differences in the user groups on all of the customer attitudes? (6 outcomes)
Descriptives
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Minimum
Maximum
Lower Bound
Upper Bound
Knowledge of the company
Light Users
104
2.85
1.711
.168
2.51
3.18
1
6
Medium Users
204
4.90
1.664
.116
4.67
5.13
2
7
Heavy Users
284
6.45
.601
.036
6.38
6.52
5
7
Total
592
5.28
1.840
.076
5.14
5.43
1
7
Satisfaction with the company
Light Users
104
2.54
1.157
.113
2.31
2.76
1
6
Medium Users
204
5.39
1.033
.072
5.25
5.53
2
7
Heavy Users
284
6.07
.776
.046
5.98
6.16
2
7
Total
592
5.22
1.585
.065
5.09
5.34
1
7
Preference for Nike
Light Users
104
2.46
1.507
.148
2.17
2.75
1
6
Medium Users
204
2.82
1.572
.110
2.61
3.04
1
5
Heavy Users
284
4.45
1.654
.098
4.26
4.64
1
7
Total
592
3.54
1.826
.075
3.39
3.69
1
7
Purchase Intention for Nike
Light Users
100
4.16
1.835
.184
3.80
4.52
1
7
Medium Users
204
4.04
1.893
.133
3.78
4.30
2
7
Heavy Users
284
5.01
1.195
.071
4.87
5.15
3
7
Total
588
4.53
1.648
.068
4.40
4.66
1
7
Would recommend company to a friend
Light Users
104
3.46
1.131
.111
3.24
3.68
2
6
Medium Users
204
4.92
.494
.035
4.85
4.98
4
6
Heavy Users
284
6.35
.477
.028
6.29
6.40
6
7
Total
592
5.35
1.260
.052
5.25
5.45
2
7
Loyalty for Nike
Light Users
104
3.92
1.499
.147
3.63
4.21
2
6
Medium Users
204
4.14
1.563
.109
3.92
4.35
2
6
Heavy Users
284
3.92
1.575
.093
3.73
4.10
2
7
Total
592
3.99
1.559
.064
3.87
4.12
2
7
Test of Homogeneity of Variances
Levene Statistic
df1
df2
Sig.
Knowledge of the company
Based on Mean
137.679
2
589
.000
Based on Median
43.167
2
589
.000
Based on Median and with adjusted df
43.167
2
366.541
.000
Based on trimmed mean
130.226
2
589
.000
Satisfacition with the company
Based on Mean
34.012
2
589
.000
Based on Median
19.318
2
589
.000
Based on Median and with adjusted df
19.318
2
543.743
.000
Based on trimmed mean
28.470
2
589
.000
Preference for Nike
Based on Mean
3.007
2
589
.050
Based on Median
2.032
2
589
.132
Based on Median and with adjusted df
2.032
2
534.316
.132
Based on trimmed mean
3.259
2
589
.039
Purchase Intention for Nike
Based on Mean
51.499
2
585
.000
Based on Median
48.655
2
585
.000
Based on Median and with adjusted df
48.655
2
555.807
.000
Based on trimmed mean
49.574
2
585
.000
Would recommend company to a friend
Based on Mean
155.697
2
589
.000
Based on Median
75.229
2
589
.000
Based on Median and with adjusted df
75.229
2
489.408
.000
Based on trimmed mean
152.051
2
589
.000
Loyalty for Nike
Based on Mean
.438
2
589
.645
Based on Median
1.134
2
589
.322
Based on Median and with adjusted df
1.134
2
466.890
.323
Based on trimmed mean
.333
2
589
.717
Table 1
Homogeneity of variance test was employed to test whether there was equality of variance in customer attitudes. The null hypothesis in this test maintains equality of variance (Derrick, Toher, & White, 2017). Three attitudes; satisfaction with the company, purchase intention for Nike and knowledge with the company had p-values equal to 0.00 which is less than level of significance 0.05. This means that null hypothesis is violated in all the three attitudes thus it is concluded that at least one item has a different mean. On the other hand, under loyalty for Nike, the p-values (0.64) was greater than level of significance 0.05. The alternative hypothesis is accepted thus it is concluded that at least one variance is different (Leigh, 2008).
ANOVA
Sum of Squares
df
Mean Square
F
Sig.
Knowledge of the company
Between Groups
1034.437
2
517.218
315.401
.000
Within Groups
965.888
589
1.640
Total
2000.324
591
Satisfaction with the company
Between Groups
959.259
2
479.630
538.032
.000
Within Groups
525.065
589
.891
Total
1484.324
591
Preference for Nike
Between Groups
461.224
2
230.612
89.966
.000
Within Groups
1509.803
589
2.563
Total
1971.027
591
Purchase Intention for Nike
Between Groups
129.379
2
64.690
25.830
.000
Within Groups
1465.070
585
2.504
Total
1594.449
587
Would recommend company to a friend
Between Groups
692.399
2
346.199
829.181
.000
Within Groups
245.919
589
.418
Total
938.318
591
Loyalty for Nike
Between Groups
6.460
2
3.230
1.331
.265
Within Groups
1429.513
589
2.427
Total
1435.973
591
Table 2
Test for the difference in means of user groups between different customer attitudes
Attitude 1: Knowledge of the company
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, not all the means are equal
Attitude 2: Satisfaction with the company
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, not all the means are equal (Howell, 2007).
Attitude 3: Preference for Nike
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, not all the means are equal
Attitude 4: Purchase intention for Nike
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, not all the means are equal (Hinkelmann & Kempthorne, 2010).
Attitude 5: Would recommend company to a friend?
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, not all the means are equal
Attitude 6: Loyalty for Nike
The test found the p-value (0.27) to be greater than the level of significance (0.05). The conclusion then is, all the means are equal (Gelman, 2005).
Multiple Comparisons
Bonferroni
Dependent Variable
(I) Webiste User Group
(J) Webiste User Group
Mean Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Knowledge of the company
Light Users
Medium Users
-2.056*
.154
.000
-2.43
-1.69
Heavy Users
-3.605*
.147
.000
-3.96
-3.25
Medium Users
Light Users
2.056*
.154
.000
1.69
2.43
Heavy Users
-1.549*
.118
.000
-1.83
-1.27
Heavy Users
Light Users
3.605*
.147
.000
3.25
3.96
Medium Users
1.549*
.118
.000
1.27
1.83
Satisfaction with the company
Light Users
Medium Users
-2.854*
.114
.000
-3.13
-2.58
Heavy Users
-3.532*
.108
.000
-3.79
-3.27
Medium Users
Light Users
2.854*
.114
.000
2.58
3.13
Heavy Users
-.678*
.087
.000
-.89
-.47
Heavy Users
Light Users
3.532*
.108
.000
3.27
3.79
Medium Users
.678*
.087
.000
.47
.89
Preference for Nike
Light Users
Medium Users
-.362
.193
.183
-.83
.10
Heavy Users
-1.989*
.184
.000
-2.43
-1.55
Medium Users
Light Users
.362
.193
.183
-.10
.83
Heavy Users
-1.627*
.147
.000
-1.98
-1.27
Heavy Users
Light Users
1.989*
.184
.000
1.55
2.43
Medium Users
1.627*
.147
.000
1.27
1.98
Purchase Intention for Nike
Light Users
Medium Users
.121
.193
1.000
-.34
.58
Heavy Users
-.854*
.184
.000
-1.30
-.41
Medium Users
Light Users
-.121
.193
1.000
-.58
.34
Heavy Users
-.975*
.145
.000
-1.32
-.63
Heavy Users
Light Users
.854*
.184
.000
.41
1.30
Medium Users
.975*
.145
.000
.63
1.32
Would recommend company to a friend
Light Users
Medium Users
-1.455*
.078
.000
-1.64
-1.27
Heavy Users
-2.887*
.074
.000
-3.06
-2.71
Medium Users
Light Users
1.455*
.078
.000
1.27
1.64
Heavy Users
-1.432*
.059
.000
-1.57
-1.29
Heavy Users
Light Users
2.887*
.074
.000
2.71
3.06
Medium Users
1.432*
.059
.000
1.29
1.57
Loyalty for Nike
Light Users
Medium Users
-.214
.188
.763
-.66
.24
Heavy Users
.008
.179
1.000
-.42
.44
Medium Users
Light Users
.214
.188
.763
-.24
.66
Heavy Users
.222
.143
.364
-.12
.57
Heavy Users
Light Users
-.008
.179
1.000
-.44
.42
Medium Users
-.222
.143
.364
-.57
.12
*. The mean difference is significant at the 0.05 level.
Table 3
Difference in mean for light, medium and heavy users
Hypothesis
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, not all the means are equal
However for Nike products, the hypothesis and conclusion was as below;
The test found the p-value (0.27) to be greater than the level of significance (0.05). The conclusion then is, all the means are equal.
Are there any differences in gender on all of the customer attitudes? (6 outcomes)
Group Statistics
Gender
N
Mean
Std. Deviation
Std. Error Mean
Knowledge of the company
Female
388
5.02
1.961
.100
Male
204
5.78
1.463
.102
Satisfaction with the company
Female
388
5.18
1.595
.081
Male
204
5.29
1.567
.110
Preference for Nike
Female
388
3.19
1.876
.095
Male
204
4.22
1.516
.106
Purchase Intention for Nike
Female
388
4.67
1.619
.082
Male
200
4.26
1.675
.118
Loyalty for Nike
Female
388
3.44
1.588
.081
Male
204
5.04
.768
.054
Would recommend company to a friend
Female
388
5.40
1.255
.064
Male
204
5.25
1.267
.089
Table 4
Independent Samples Test
Levene’s 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
Knowledge of the company
Equal variances assumed
56.606
.000
-4.892
590
.000
-.764
.156
-1.070
-.457
Equal variances not assumed
-5.347
522.857
.000
-.764
.143
-1.044
-.483
Satisfaction with the company
Equal variances assumed
.024
.877
-.867
590
.386
-.119
.137
-.388
.150
Equal variances not assumed
-.872
419.112
.384
-.119
.136
-.387
.149
Preference for Nike
Equal variances assumed
14.901
.000
-6.765
590
.000
-1.030
.152
-1.329
-.731
Equal variances not assumed
-7.223
493.730
.000
-1.030
.143
-1.310
-.750
Purchase Intention for Nike
Equal variances assumed
2.724
.099
2.876
586
.004
.410
.143
.130
.690
Equal variances not assumed
2.845
390.123
.005
.410
.144
.127
.694
Loyalty for Nike
Equal variances assumed
246.135
.000
-13.543
590
.000
-1.596
.118
-1.827
-1.364
Equal variances not assumed
-16.471
586.589
.000
-1.596
.097
-1.786
-1.406
Would recommend company to a friend
Equal variances assumed
.157
.692
1.442
590
.150
.157
.109
-.057
.371
Equal variances not assumed
1.437
409.319
.151
.157
.109
-.058
.372
Table 5
Test for equality of variance in attitude (knowledge of the company) between male and female
Hypothesis
H0: Variance1 = Variance2
Versus
H1: Variance1 ≠ Variance2
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, there is a significant difference in the two variances.
Test for equality of variance in attitude (satisfaction with the company) between male and female
Hypothesis
H0: Variance1 = Variance2
Versus
H1: Variance1 ≠ Variance2
The test found the p-value (0.27) to be greater than the level of significance (0.05). The conclusion then is, all the means in attitude levels between the males and females are equal.
Test for equality of variance in attitude (preference for Nike) between male and female
Hypothesis
H0: Variance1 = Variance2
Versus
H1: Variance1 ≠ Variance2
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, there is a significant difference in the two variances.
Test for equality of variance in attitude (purchase intention for Nike) between male and female
Hypothesis
H0: Variance1 = Variance2
Versus
H1: Variance1 ≠ Variance2
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, there is a significant difference in the two variances.
Test for equality of variance in attitude (loyalty for Nike) between male and female
Hypothesis
H0: Variance1 = Variance2
Versus
H1: Variance1 ≠ Variance2
The test found the p-value (0.00) to be less than the level of significance (0.05). The conclusion then is, there is a significant difference in the two variances.
Test for equality of variance in customer attitude (would recommend company to a friend) between male and female
Hypothesis
H0: Variance1 = Variance2
Versus
H1: Variance1 ≠ Variance2
The p-value calculated (0.15) is great compared to the alpha value (0.05). This means that null hypothesis is accepted thus equality of variance in attitude levels between the males and females.
One sample t-test for the mean satisfaction level (3.5)
Hypothesis
H0: µ = 3.5
Versus
H1: µ ≠ 3.5
Test results table is as shown below
One-Sample Statistics
N
Mean
Std. Deviation
Std. Error Mean
Satisfaction with the company
592
5.22
1.585
.065
Table 6
One-Sample Test
Test Value = 3.5
t
df
Sig. (2-tailed)
Mean Difference
95% Confidence Interval of the Difference
Lower
Upper
Satisfaction with the company
26.349
591
.000
1.716
1.59
1.84
Table 7
Since the p-value calculated (0.00) is less compared to the alpha value (0.05). This means that null hypothesis is not accepted thus the mean satisfaction level is not 3.5.
Discussion and recommendation
The research team found many revelations from the data analysis results. The product which fetched the highest profit was the customised products ($ 25). The second best product in terms of mean profit was men’s shoes ($15.5). The product that fetched the least profit was boys clothing. To add on, the product which had the highest mean cost was the customised products ($9.9). The second costly product was girls’ shoes ($8). The product that was less costly was boys’ clothing. The research also found that there was no significant difference in the attitude levels between males and females.
Reference
Derrick, B., Toher, D., & White, P. (2017). How to compare the mean of two samples that include paired observations and independent observations. Quantitative methods for Psychology, 13(2), 120 – 126.
Gelman, A. (2005). Analysis of variance? Why it is more important than ever. The anals of Statistics, 33, 1 – 53.
Hinkelmann, K., & Kempthorne, O. (2010). Design and analysis of experiments (5 ed., Vol. 8).
Howell, D. C. (2007). Statistical methods for Psychology (3 ed., Vol. 5).
Leigh, E. S. (2008). Consumer rites. Selling of American Holidays, 6(3), 106 – 191.
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Australia Maidstone Management Business management with marketing University of New South Wales Masters in Business Administration
BSBHRM513 Manage Workforce Planning
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20
Course Code: BSBHRM513
University: Tafe NSW
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Country: Australia
Answer:
Task 1
1.0 Data on staff turnover and demographics
That includes the staffing information of JKL industries for the fiscal year of 2014-15, it can be said that the company is having problems related to employee turnover. For the role of Senior Manager in Sydney, the organization needs 4 managers; however, one manager is exiting. It will make one empty position which might hurt the decision making process. On the other hand, In Brisba…
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MKT2031 Issues In Small Business And Entrepreneurship
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5
Course Code: MKT2031
University: University Of Northampton
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Country: United Kingdom
Answer:
Entrepreneurial ventures
Entrepreneurship is the capacity and willingness to develop, manage, and put in order operations of any business venture with an intention to make profits despite the risks that may be involved in such venture. Small and large businesses have a vital role to play in the overall performance of the economy. It is, therefore, necessary to consider the difference between entrepreneurial ventures, individual, and c…
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Turkey Istanbul Management University of Employee Masters in Business Administration
MN506 System Management
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7
Course Code: MN506
University: Melbourne Institute Of Technology
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Country: Australia
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Introduction
An operating system (OS) is defined as a system software that is installed in the systems for the management of the hardware along with the other software resources. Every computer system and mobile device requires an operating system for functioning and execution of operations. There is a great use of mobile devices such as tablets and Smartphones that has increased. One of the widely used and implemented operating syste…
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Australia Cheltenham Computer Science Litigation and Dispute Management University of New South Wales Information Technology
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