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The minimum requirement is a neat, functional, interactive dashboard. Following questions will guide you designing an interactive dashboard.
1. What are the most appropriate visualisations for the dashboard?
2. What about choices of colour?
3. How can I use hierarchy and dimension?
4. How can I make the dashboard interactive?

## Email from the General Manager

Australia’s leading supermarket chains is “Supermart”. It was established 30 years age. There are 150 stores are present in the chain. After originating from a family-based chain of general stores, “Supermart” has stores in many states across Australia. For making the online purchase, the company introduced an online channel for stores to the consumers in some chosen suburbs of Australia. Individual store management has vast-ranging operating capability about day-to-day operations of their stores. The Head office of the company in Melbourne prepares a planning and direction to widen the business.

The research report compares the market position of the company with the competitors. The general manager of “Supermart” has asked to present a preferable data analysis and visual dashboard. The exploratory and descriptive analysis is carried out here that helped to gain a better understanding of performance of the “Supermart” (Van Der Aalst, La Rosa and Santoro 2016).

Email from the General Manager

To: Grace Wong

From: Stephen Hennigsson

Subject: Analysis of the Store data

Dear Grace,

As per the interest of board, I have analysed the last year financial data to make sure that we are keeping pace with our main competitors. The data analysis graphically and theoretically provides the claim and hypothesis regarding the asked question. The investigation highlights the overall scenario of the market value and the statistics of the previous year for business purpose.

Provide Summary of Gross profit.

The overall summary statistics of gross profit refers that the average gross profit of 150 samples is 0.9335 \$m. The middle most value of the gross profit is calculated as 0.8835 \$m. The scatter ness in terms of standard deviation is found to be 0.6755 \$m. The positive value of skewness interprets that the distribution of gross salary is positively skewed. The highest gross profit of a sample in the dataset is 2.872 \$m and the lowest gross profit of a sample in the dataset is 0.018 \$m in the dataset. The range of gross profit of all the 150 samples is 2.854 units (Johnson 2013). The estimated average gross profit of the all the samples lies in the interval of 1.0424 \$m and 0.8245 \$m (Quinn and Keough 2002).

Provide a gross profit comparison for our stores that open on Sunday and the stores that do not?

The board is willing to compare the gross profit of the stores that are open in the Sundays and closed (not opened) in the Sundays. It is found that out of 150 samples, 57 stores are closed on Sundays while 93 are kept open in Sundays.

## Data Analysis

For testing equality of averages of gross profit of stores that are either open or not open on Sundays, the two-sample t-test for equal and unequal variances are executed (Taeger and Kuhnt 2014).

Null hypothesis (H0): The average gross profit of the shops that are open on Sundays is greater than the average gross profit of the shops that are closed on Sundays.

Alternative hypothesis (HA): The average gross profit of the shops that are open on Sundays is equal the average gross profit of the shops that are closed on Sundays.

Observation: The average gross profit of the stores that are open in the Sundays is 0.997 and the average gross profit of the stores that are closed in the Sundays is 0.830.

Interpretation: As 0.1245>0.05 and 0.11422>0.05, therefore, null hypothesis is rejected with 5% level of significance. Therefore, the average gross profit of the shops that are open on Sundays is greater than the average gross profit of the shops that are closed on Sundays.

Conclusion: It is 95% evident that there exists significant difference between the average profit between the average prices of the shops that are open and closed on Sundays.

Is there any evidence of the fact that Mall stores are leading the way than other location of stores when the question of setting up online stores comes?

It is hypothecated that Mall stores are leading the way of setting up online stores. The assertion is investigated below.

The number of “Mall” stores that are eager to set up an online store is 43 out of 62. The calculated percentage of the “Mall” stores that are eager to set up an online store is 69.4% (Pituch, Stevens and Whittaker 2013).

The number of “Country” or “Strip” stores that are keen to set up an online store is 62 out of 88. The calculated percentage of the “Country” or “Mall” stores that are eager to set up an online store is 70.5%.

To compare the proportions of both the cases, Two-samples proportional Z-test is applied.

Null hypothesis (H0): The percentage of malls that have online settings and the percentage of country or strip that have online settings are not significantly different.

Alternative hypothesis (HA): The percentage of malls that have online settings and the percentage of country or strip that have online settings are significantly different.

Observation: The percentage of malls that have online settings is 69.4% and the percentage of country or mall that have online settings is 70.5%.

Interpretation: As 0.8854>0.05, therefore, null hypothesis is accepted with 5% level of significance. Therefore, the percentage of malls that have online settings and the percentage of country or strip that have online settings are not significantly different (Palocsay, Markham and Markham 2010).

Conclusion: It is 95% evident that there does not exist significant difference between the percentage of malls that have online settings and the percentage of country or strip that have online settings.

The shops and stores of Australia generate medium amount of breakdown wastages mostly (81) followed by high amount of breakdown wastages (46) (Wegner 2010). The least number of shops and stores of Australia create low breakdown wastages (23).

How the states compare when it comes the question of high level of wastage in their stores?

The total number of stores in Australia is 46. Out of them, 11 are located in Queensland and 11 are located in Tasmania (Kraus 2014). Only 1 shop each in Australian Capital Territory and Northern Territory occur high amount of breakdown wastages. Besides, Western Australia (2) and Tasmania (3) do not possess a significant number of stores that create high amount of breakdown wastages.

Do the listed factors in the assignment file provide any explanation in the variation of sales between stores?

It is a fact of verification that which factors among “Advertise expenses”, “Number of Staff”, “Number of car spaces” and “Number of trading hours” influence the variation of sales between stores. It is also required to find that among the mentioned four factors which factor is most important to explain variation of sales between stores.

The dependent factor of the multiple regression model is “Sales\$m” and the four independent factors are accounted as “Adv.\$’000”, “No. Staff”, “Car Spaces” and “HrsTrading”.

The hypotheses of the model:

Null hypothesis (H0): The independent variables do not have linear significant association with the dependent variable.

Alternative hypothesis (HA): The independent variables have linear significant association with the dependent variable.

As 0.0<0.05, therefore, the null hypothesis is rejected at 5% level of significance. The null hypothesis of the insignificant linear association between explanatory (independent) and response (dependent) variable is rejected with 95% probability. Finally, it could be interpreted that the all the explanatory factors are linearly and significantly associated with the response variable.

The slopes between advertise amount and variation of Sales, number of staffs and variation of sales as well as car spaces and variation of sales are positive. Hence, the linear relevance between amount of sales and all the dependent variables is direct. That is for one unit increase in car spaces, number of staffs and advertisement amount, the amount of sales increases by 0.009673 unit, 0.049954 unit and 0.034506 unit respectively. Similarly, for one unit decrease of these amounts, the amount of sales decreases by 0.009673 unit, 0.049954 unit and 0.034506 unit respectively (Catalina, Iordache and Caracaleanu 2013).

Conversely, the slope between “HrsTrading” and “Sales\$m” is negative. Therefore, the linear association between amount of sales and trading hours is reciprocal. That is for one unit increase of trading hours, the amount of sales decreases by 0.00354 unit and vice versa.

The factors having p-value less than 0.05 are accounted as significant factors. The significant factors have linear significant association between dependent variable and independent variables. As per the model, two factors are found to be significant that are Advertising expense and number of staffs. Other two variables are found insignificant (Keith 2014).

The p-value of the predictor variable “Advertising expense” (3.61E-19) is lesser than the p-value of the predictor variable “Number of staffs” (0.01524). Hence, the significance of advertising expense is lower than the number of staffs to the amount of sales. It could be interpreted that the most statistically significant factor to influence the amount of sales is “Advertising expense”.

The data analysis helps to extract the interpretation that the overall gross profit of the 150 stores of the company is widely spread. The average gross profit is less than 1 \$million. The maximum gross profit of any store of the company is \$2.872 \$m and minimum gross profit of any store of the company is 0.018 \$m. The estimated average gross profit of “Supermart” is in the interval of 0.82 \$m to 1.04 \$m.

The average gross profit is lower for the stores that are open on Sundays than the stores that are closed on Sundays. Therefore, to stay in competition, our company must keep the stores open on Sundays.

It was also found that to set up online stores for improving business, the “Mall” stores are not significantly different from “Country” or “Strip” stores. Almost one third of the overall breakdown of wastages among all the stores are high that is second highest in frequency. The minimum number of stores have low breakdown wastages and maximum number of stores have high breakdown wastages.

Queensland and South Australia has maximum number of stores (11) that generate high breakdown of wastage. Northern territory and Australian capital territory has least number of stores (1) that generate high breakdown of wastage.

The variation of amount of sales is found to be mostly explained by the two factors “Advertising expenses” and “Number of staffs”. Rest of the two factors “Number of car spaces” and “Number of trading hours” are found irrelevant to explain the variation of sales among the stores. To increase the sales, our company should concentrate on increasing the amount of advertising expenses and number of staffs.

The interactive Dashboard would also help you to explore the Sales and Gross profit performance from different dimensions. The averages of Sales and Gross profit in this analysis are taken into consideration.

References:

Alexander, M. and Walkenbach, J., 2013. Excel Dashboards and Reports (Vol. 17). John Wiley & Sons.

Catalina, T., Iordache, V. and Caracaleanu, B., 2013. Multiple regression model for fast prediction of the heating energy demand. Energy and Buildings, 57, pp.302-312.

De Winter, J.C., 2013. Using the Student's t-test with extremely small sample sizes. Practical Assessment, Research & Evaluation, 18(10).

Dierenfeld, H. and Merceron, A., 2012. Learning analytics with excel pivot tables.

Jelen, B. and Alexander, M., 2010. Pivot Table Data Crunching: Microsoft Excel 2010. Pearson Education.

Johnson, V.E., 2013. Revised standards for statistical evidence. Proceedings of the National Academy of Sciences, 110(48), pp.19313-19317.

Keith, T.Z., 2014. Multiple regression and beyond: An introduction to multiple regression and structural equation modeling. Routledge.

Kraus, D., 2014. Consolidated data analysis and presentation using an open-source add-in for the Microsoft Excel® spreadsheet software. Medical Writing, 23(1), pp.25-28.

Palocsay, S.W., Markham, I.S. and Markham, S.E., 2010. Utilizing and teaching data tools in Excel for exploratory analysis. Journal of Business Research, 63(2), pp.191-206.

Pituch, K.A., Stevens, J.P. and Whittaker, T.A., 2013. Intermediate statistics: A modern approach. Routledge.

Quinn, G. and Keough, M., 2002. Statistical hypothesis testing. Experimental design and data analysis for biologists, 2002, pp.32-39.

Robbins, N.B., 2012. Creating more effective graphs. Wiley.

Taeger, D. and Kuhnt, S., 2014. Statistical hypothesis testing. Statistical Hypothesis Testing with SAS and R, pp.3-16.

Van Der Aalst, W.M., La Rosa, M. and Santoro, F.M., 2016. Business process management.

Wegner, T., 2010. Applied business statistics: Methods and Excel-based applications. Juta and Company Ltd.

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