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BUS1BAN Business Analytics

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BUS1BAN Business Analytics

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Course Code: BUS1BAN
University: La Trobe University

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Country: Australia

Question:

Form a group of 3 students for the assignment. Only one assignment is to be submitted per group.  All group members must be from the same BAN class.  Make sure you form your group straight away once the assignment is uploaded and register your group members with your teacher by your last class in Week 3
Select a random sample of 80 for analysis. Randomly select the responses of 80 participants.  You will find the 645 responses in the Excel file called “Data for the Assignment”.   The Excel file provides further details about the sample selection process.
Submit your random sample of 80 as an Excel file through Turnit in on Moodle by end of Week 3… The sample you submit will be used to double check your calculations in the report.  This is where you will have to submit details of your group members as well.
Submit a written report (word file) through Turnit in on Moodle by end of Week 10. The report must respond to the specific questions asked in a report format.  Use the file called “Tuna Report Format” to insert your answers to the questions and then upload this file.  Ensure that the cover page includes names and ID numbers of each student in your group as well as the student contribution mark for each student along the signature of each student. 

In addition, submit a hard copy of your report (printed copy) to your teacher.

Answer:

Canning is a food preservation technique that makes use of cans to store processed food (Schafer, 2000). The food stored in cans are usually easy to cook food items which have an extended shelf life and can easily be stored in a variety of conditions.
Underfilling is a situation where the actual weight of the cans is lower than the weight labelled on the can. This way the consumers are deceived to purchase less products with no price reduction advantage.
This report is designed to evaluate the spread of the underfilling issue in tuna cans in Australia.
The data was collected from the Northern shopping centre by students of Busiban for a period of a month. In total 645 consumers participated in the survey from which a sample of 80 was selected randomly for analysis (Dillman, et al., 2009).

Data Analysis

Section A: Basic Analysis

Demand of the brand

The table below gives an illustration of the proportion of consumers purchasing brand A and B products.
Graphical display
From the pie chart its visible that the brand B is more popular than Brand A. The analysed data indicate that only 33.75% of the market proportion is under the control of company A with B in control of the other areas.

Tuna cans brand weight
A table summary

 

Brand A

Brand B

Minimum weight

90.2

88.3

Maximum weight

111.7

104.3

Range

21.5

16

Average weight

101.13

97.54

Standard deviation

4.636

3.696

Brand A products have an expected weight of 111.13 gm which is above the 100-gm standard mark while the brand B products have an expected weight of 97.54 which can be classified as underweight. The spread of Brand A products is larger than that of brand B products indicating that most of the products of type B are more centralised around the mean.
Distribution frequency
 
Graphical presentatio

From the cumulative relative frequency table, we can observe that 25.93% of the products from brand A are underfilled while up to 46.06% of the Brand B products are underfilled.

Underfilled or not
Table classification

Cross classification table by frequency

 

 

Underweight?

Total

 

 

Yes

No

Brand

A

7

20

27

B

26

27

53

Total

33

47

80

Cross classification table by brand total relative frequency

 

 

Underweight?

Total

 

 

Yes

No

Brand

A

25.93%

74.07%

 

B

49.06%

50.94%

 

Total

 

 

 

Graphical presentation

The underfilling issue is severe in brand B compared to brand A.

Section B: Intermediate level analysis

Potential NT Tuna demand
tabular summary of the NT Tuna demand
Scatter plot presentation
Interpretation of the graph

At 0% discount there are 11.19% of the consumers who are willing to shift their brand of preference. When the discount changes by a single percentage the percentage number of consumers willing to consume NT Tuna goes up by 1.17%. In addition, the R squared is obtained as 0.9818. This means that 98.18% of the number of consumers shifting consumption from their current brand to the NT Tuna are as a result of the discount being offered on the NT Tuna. The value of , this value is closer to 1 hence indicating a strong positive correlation between the discount offered and the number of consumers willing to buy NT Tuna.

Using the equation we can calculate the percentage discount which will guarantee NT Tuna 15% of the current market.

, making x the subject of the formula
         This means the producers of NT Tuna need to allow a discount of 11.8% if they are to secure a market proportion of 25%.

NT Tuna potential market capture by brand
Table summary
Scatter plot presentation

The slope of the line graph in A is higher than that of B, this means that the consumers of B are more loyal to their brand and a change in discount offered affects their shift with less intensity compared to that of A

IF the producers of the NT Tuna fail to provide any discount the product will be able to capture 17.16% of the brand B market share while at the same time losing 0.53% of their existing market to product A. By availing no discount NT Tuna is a ta risk of losing its consumers to the consumption of A.

Section C: Advanced scenarios

Random selection of a consumer

Graphical presentation of the confidence interval
There is no overlap between the probabilities hence it can be interpreted that the chance of the consumer being loyal to product A or B is clearly defined with no probability of consuming both the products at the same time.

Graphical presentation of the confidence interval for the mean
In test this validity we carry the one sample z-test

We begin by stating the hypothesis being tested;
The test is two tailed as the alternate hypothesis can either be higher or lower than 100.
Now we set up the data in excel and carry out the z-test using the data analysis tool. Below is the achieved output.
In this case the p value is obtained as 0.20697 which is greater than 0.05. hence, we can conclude that there is no sufficient evident to reject the null hypothesis.
The claim by brand A producers that the mean weight of their products is 100 gm can be stated to be valid.

First, we set the percentile table

From there, we test the hypothesis that 20% of the data is less than 98 gm using alpha at 5%.
The table shows that 46.1% of the data fall below the 98gm mark. We thereby reject the null hypothesis and conclude that more than 20% of the products of brand B are underweight.

Summary and discussion

In summary the most popular brand is B, the brand controls over 60% of the tuna market share. This is despite it being the most affected by the underfilling issue. Averagely the weight of the brand B tuna can is 97.54 gm which gives an insight of how extreme the underweighting issue have affected the brand. Considering that up to 46.06 % of the sampled brand B cans are found to be underweight, its surprising that their clients seems to be the most loyal.
The availability of discount has strong positive correlation with the consumers willingness to shift product brand preference. This makes it a vital strategy of capturing the market under the competitive market conditions.
The data collection technique applied is in line with the random sampling as all the consumers in The Northern market centre were given an equal opportunity of participating in the survey as well as being part of the sampled data (Mohammad & Dougherty, 2014).
The collection of the data from one location should be a concern as it may not be realistic to use the data to represent the whole of Melbourne/Australia. Market centre tends to serve majority of the people from a specific locality who may have preference for a given product.
To mitigate the sampling issues the data should be collected across a number of market centres spread across Australia.
References
Berinsky, A. J., 2008. Survey non-response. In W. Donsbach & M. W. Traugott (Eds.), The SAGE handbook of public opinion research (pp. 309–321), s.l.: Sage Publications.
Dillman, D., Smyth, J. & Christian, L. M., 2009. Internet, mail, and mixed-mode surveys: The tailored design method, San Francisco: Jossey-Bass.
Mohammad, E. & Dougherty, E., 2014. Effect of separate sampling on classification accuracy. Bioinformatics, 30 (2), p. 242–250.
Schafer, W., 2000. Home Canning Tomatoes.
Shields, P. M. & Tajalli, H., 2006. Intermediate Theory: The Missing Link in Successful Student Scholarship. Journal of Public Affairs Education. , 12 (3), p. 313–334.

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