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# BUS501 Business Analytics And Statistics

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• Course Code: BUS501
• University: University of Sunshine Coast
• Country: Australia

## Question:

This assignment is based on fictional data.You are creating a business report for the CEO of Honeybee Fruit. It must be professional in presentation and contain insightful content for them to make business decisions.

Use the statistical analyses you have learned to answer all of the questions below

This first dataset is labelled “Fruit shop data product mix”:

• The variables include:
• Product class
• Product name
• Product category
• Total sales
• Cost of Goods (COGs)
• Net profit
• Location in the shop – front, left, outside front, rear, right
• Profit total

The second data set is labelled “Fruit shop data sales summary”:

• The variables include:
• Month (January, February, March, April, May, June, July, August, September, November, December)
• Season (Spring, summer, autumn, winter)
• Gross sales
• Net sales
• Cash total payments
• Credit total payments
• Total orders
• Average sale
• Staff cost
• Profit total

1. What are the best and worst selling products in terms of sales?
2. Is there a difference in payments methods? (Cash vs Credit)
3. Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?
4. Is there a difference in sales and gross profits between different months of the year?
5. Are their differences in sales performance between different seasons? (Summer, spring, autumn, winter)

### Introduction

Company sales is one of the key performances that shows that the business is either doing well or in some verge to collapse. The CEO of Honeybee Fruit is a concerned person and would want to understand how the company is performing. He is particularly interested in knowing which of the products are doing well and which ones are not doing well in the market. With this in mind, this study therefore sought to analyse the performance of Honeybee Fruit and make recommendations to the CEO regarding how best or worst the company is doing in terms of the profits made, sales, which months of the year the company made more profits, what about the seasons among others.

### Problem definition and business intelligence required

The main research questions that this study sought to answer include;

• What are the best and worst selling products in terms of sales?

This is the first research question that the study sought to answer. To answer this question, there is need to have an idea of how the products perform. Considering sales and product type, we will find the mean sales for each of the products and the rank based on the product with the highest average sales to the product with the lowest average sales. This means that descriptive statistics will be able to answer this research question.

• Is there a difference in payments methods? (Cash vs Credit)

Different payment methods are normally available for different organizations. This study sought to find out whether any of the payments methods brings in more revenue than the other and if yes, then which payment method is that? To answer the research question, a recommended test is the t-test which compares the average for two groups (Marden, 2000).

• Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?

This is another question that we sought to tackle in this study. To answer it, we needed to make a comparison between the average sales performances for all the locations and test the hypothesis. Since this question unlike the previous one has more than 2 factors, the t-test used above would not be ideal but rather we would use analysis of variance (ANOVA). ANOVA test is useful when we want to compare the average of more than 2 factors (Wilkinson, 1999)

• Is there a difference in sales and gross profits between different months of the year?

Just like the above research question, to answer the question as to whether the sales between the various months of the years is different or not, we have to use ANOVA test since there more than two factors for the independent variable (Derrick, Broad, Toher, & White, 2017).

• Are their differences in sales performance between different seasons? (Summer, spring, autumn, winter)

Again analysis of variance (ANOVA) test would be the most ideal test to be used since the number of season are four and this number is more than 2 factors hence the need to use ANOVA test.

### Results of the selected analytics methods and technical analysis

This section presents the results of the research questions discussed above. The section also reports on the hypothesis that was tested in each of the research question.

### What are the best and worst selling products in terms of sales?

As had been mentioned, answering this research question requires comparing the average sales for the different products and ranking them to see which of the products rank high (best performing) and those that rank low (worst performing). The results are given in table 1 and table 2 below;

In table 1, the top 5 products that had the highest average sales is presented. Water ranks the highest among all the products with an average sales revenue of \$1867.08. It is closely followed by sales from the fruits which had an average of \$1,048.67. Drinks closes the least of top 5 products that had highest sales with an average sales revenue of \$574.31.

Table 1: Top best-selling products in terms of sales

 Rank Product Class Average Sales 1 Water \$   1,867.08 2 Fruit \$   1,048.67 3 Vegetable \$       871.51 4 Dairy \$       619.12 5 Drinks \$       574.31

In table 2, the products were ranked from the bottom in order to check for the worst performing products (Mahdavi , 2012).  The number 1 product among the worst selling products as can be seen in table 2 below is the juicing with only an average sales revenue of \$5. Other products that did not perform well in terms of sales are the herbal teas, spices, snacks and salad greens.

Table 2: Bottom 5 worst selling products in terms of sales

 Rank Product Class Average Sales 1 Juicing \$       5.00 2 Herbal Teas \$    18.00 3 Spices \$    19.07 4 Snacks \$    20.50 5 Salad Greens \$    25.00

### Is there a difference in payments methods? (Cash vs Credit)?

This is another important research question that the CEO would want answered. To answer this, we needed to test the following hypothesis;

H0: The average sales revenue from the cash payment does not significantly differ from the sales revenue from the credit payments.

HA: The average sales revenue from the cash payment significantly differ from the sales revenue from the credit payments.

Table 3: Group Statistics

 Payment method N Mean Std. Deviation Std. Error Mean Total amount received Credit 366 584.81 228.87 11.96 Cash 366 404.29 153.65 8.03

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 Total amount received Equal variances assumed 42.885 .000 12.528 730 .000 180.52 14.41 152.23 208.81 Equal variances not assumed 12.528 638.5 .000 180.52 14.41 152.22 208.81

Looking at the above two tables, it can be seen that the average sales revenue received from cash payments averaged at 404.29 (SD = 153.65) while that from the credit payments averaged at 584.81 (SD = 228.87). The t-test results further showed that the p-value was 0.000 (a value smaller than 5% level of significance) hence resulting to the rejection of the null hypothesis (Nikoli?, Muresan, Feng, & Singer, 2012). Rejecting the null hypothesis implies that we come to a conclusion that the average sales revenue from the cash payment significantly differ from the sales revenue from the credit payments. The amount received from the credit payments is by far higher than what is received from the cash payments.

### Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?

The products were found to be located in five different locations and so to answer this research an ANOVA test was to be performed. The hypothesis tested was;

H0: Average sales of the product does not significantly differ depending on the location of the product.

HA: Average sales of the product significantly differ depending on the location of the product.

Table 5: Descriptive statistics

 N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Lower Bound Upper Bound Outside Front 12 3384.37 4719.35 1362.358 385.84 6382.90 Front 155 572.75 1430.66 114.913 345.74 799.76 Rear 180 536.07 1072.15 79.914 378.38 693.77 Right 311 239.89 553.00 31.358 178.19 301.59 Left 376 218.22 427.61 22.053 174.86 261.58 Total 1034 369.96 1014.72 31.556 308.04 431.88

Products located outside front had the highest sales revenue (M = 3384.37, SD = 4719.35) which were almost 6 times that of the second best sales revenue (M = 572.75, SD = 1430.66) which came from products located in front. Products on the left had the lowest average sales revenue at 218.22 (SD = 218.22).

Table 6: ANOVA

 Total Sales (\$) Sum of Squares df Mean Square F Sig. Between Groups 134299725 4 33574931.26 37.176 .000 Within Groups 929333381 1029 903142.26 Total 1063633106 1033

The p-value for the ANOVA test is 0.000 (a value smaller than 5% level of significance) hence resulting to the rejection of the null hypothesis. Rejecting the null hypothesis implies that we come to a conclusion that the average sales of the product significantly differ depending on the location of the product (Székely & Rizzo, 2017). Products located outside front had the highest sales revenue (M = 3384.37, SD = 4719.35) which were almost 6 times that of the second best sales revenue (M = 572.75, SD = 1430.66) which came from products located in front. Products on the left had the lowest average sales revenue at 218.22 (SD = 218.22).

From the above results, it is evident that the profits and the revenues are likely to be affected I the same manner the sales have been affected. This is because profits and revenues are generated based on the sales. So if sales are affected then most likely the profits and revenues will not be spared.

### Is there a difference in sales and gross profits between different months of the year?

There are 12 months in a year so to answer this research an ANOVA test was to be performed. The hypothesis tested was;

H0: Average sales of the product does not significantly differ depending on the month of the year.

HA: Average sales of the product significantly differ depending on the month of the year.

The results of the test are given below;

Table 7: Descriptive statistics

 N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Lower Bound Upper Bound January 31 979.65 368.350 66.158 844.54 1114.76 February 29 1066.47 237.830 44.164 976.00 1156.94 March 31 1063.69 379.880 68.228 924.35 1203.03 April 30 1078.77 314.570 57.432 961.31 1196.23 May 31 1054.09 336.527 60.442 930.65 1177.53 June 30 918.81 223.442 40.795 835.38 1002.25 July 31 1004.78 248.080 44.556 913.79 1095.78 August 31 1025.26 308.806 55.463 911.99 1138.54 September 30 1014.06 302.101 55.156 901.25 1126.86 October 31 1056.03 353.343 63.462 926.42 1185.64 November 30 1197.64 343.489 62.712 1069.38 1325.90 December 31 1082.74 413.527 74.272 931.05 1234.42 Total 366 1044.97 326.285 17.055 1011.43 1078.51

The month of November had the highest sales recorded (M = 1197.64, SD = 343.49) while the month with the lowest sales was the month of June (M = 918.81, SD = 223.44).

Table 8: ANOVA Table

 Sum of Squares df Mean Square F Sig. Between Groups 1508892.474 11 137172.043 1.300 .222 Within Groups 37349615.455 354 105507.388 Total 38858507.929 365

The p-value for the ANOVA test is 0.222 (a value bigger than 5% level of significance) hence resulting to the non-rejection of the null hypothesis. Failure to reject the null hypothesis implies that we come to a conclusion that the average sales of the products does not significantly differ depending on the month of the year.

Figure 1: Mean plot for gross sales versus month

Also tested in this section is whether the average gross profits differ among the months of the year. The tested hypothesis is given as follows

H0: Average gross profits of the product does not significantly differ depending on the month of the year.

HA: Average gross profits of the product significantly differ depending on the month of the year.

ANOVA test used at 5% level of significance. The results are as follows;

Table 9: Descriptive Statistics

 N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Lower Bound Upper Bound January 31 33.0187 41.52022 7.45725 17.7890 48.2484 February 29 23.4317 18.62369 3.45833 16.3477 30.5158 March 31 19.3377 16.22981 2.91496 13.3846 25.2909 April 30 19.6233 12.79108 2.33532 14.8471 24.3996 May 31 20.2316 20.39691 3.66339 12.7500 27.7133 June 30 19.3213 15.05624 2.74888 13.6992 24.9434 July 31 28.8258 17.17982 3.08559 22.5242 35.1274 August 31 34.4823 20.72196 3.72177 26.8814 42.0831 September 30 43.0557 35.75048 6.52711 29.7062 56.4051 October 31 46.2616 38.93676 6.99325 31.9795 60.5437 November 30 43.2477 49.52855 9.04263 24.7534 61.7419 December 31 37.2877 29.33486 5.26870 26.5276 48.0479 Total 366 30.7098 30.05661 1.57108 27.6202 33.7993

The month of October had the highest gross profits recorded (M = 46.26, SD = 38.94) while the month with the lowest sales was the month of March (M = 19.34, SD = 16.23).

Table 10: ANOVA Table

 Profit Total Sum of Squares df Mean Square F Sig. Between Groups 35370.948 11 3215.541 3.867 .000 Within Groups 294370.006 354 831.554 Total 329740.954 365

The p-value for the ANOVA test is 0.000 (a value smaller than 5% level of significance) hence resulting to the rejection of the null hypothesis. Rejecting the null hypothesis implies that we come to a conclusion that the average gross profits of the product significantly differ depending on the month of the year. Products sold in the month of October had the highest sales revenue (M = 46.26, SD = 38.94) while the month with the lowest sales was the month of March (M = 19.34, SD = 16.23).

Figure 2: Mean plot for gross profits versus month

### Are their differences in sales performance between different seasons?

There are 4 seasons in a year so to answer this research an ANOVA test was to be performed. The hypothesis tested was;

H0: Average sales of the product does not significantly differ depending on the season of the year.

HA: Average sales of the product significantly differ depending on the season of the year.

The results of the test are given below;

Table 12: Descriptive statistics

 N Mean Std. Deviation Summer 91 1042.44 349.18 Autumn 92 1065.37 341.39 Winter 92 983.65 264.13 Spring 91 1088.88 339.45 Total 366 1044.97 326.29

Spring had the highest gross sales recorded (M = 1088.88, SD = 339.45) while the season with the lowest sales was the winter (M = 983.65, SD = 264.13).

Table 13: ANOVA table

 Gross_Sales Sum of Squares df Mean Square F Sig. Between Groups 560240.410 3 186746.803 1.765 .153 Within Groups 38298267.520 362 105796.319 Total 38858507.929 365

The p-value for the ANOVA test is 0.153 (a value bigger than 5% level of significance) hence resulting to the non-rejection of the null hypothesis (Cohen, Cohen, West, & Aiken, 2002). Failure to reject the null hypothesis implies that we come to a conclusion that the average sales of the product does not significantly differ depending on the season of the year.

Figure 3: Mean plot for gross sales versus season of the year

### Discussion of the results and recommendations

This study sought to analyse the performance of the company focussing on the sales revenue and the gross profits. The study began by looking at the best and the worst selling products. To identify the best and the worst selling products, the average sales of each and every product was determined and the products ranked based on the average with the products having the highest sales revenue ranking first. Some of the best-selling products included water, fruits, vegetables, dairy products and the drinks. On the other hand, the worst performing products included salad greens, snacks, spices, herbal teas and juicing products. Some of the factors that were found to influence the performance of the business (either in terms of sales or the profits) include;

• Location of the product in the shop (sales)
• Month of the year (profits)
• Payment method

### Recommendations

Considering the above findings. The following recommendations are made to the company.

• The company to find out why the month of the year does not influence the sales but influences the profits. The reason could be there are high costs of goods in some months than the others hence the management needs to identify which months have high cost of goods and which ones have low cost of goods and know how to budget in order to maximize on profits.
• Work on proper way of ensuring the products are well displayed in locations where they can attract customers for subsequent close in sales.
• Ensure that there are flexible payment methods that would make the customers pay with ease.

## References

Derrick, B., Broad, A., Toher, D., & White, P. (2017). The impact of an extreme observation in a paired samples design. metodološki zvezki - Advances in Methodology and Statistics, 14(2), 1-17.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences. Psychology Press, 5(6), 31-39.

Mahdavi , D. B. (2012). The Misleading Value of Measured Correlation. Wilmott, 1(3), 64–73. doi:10.1002/wilm.10167

Marden, J. I. (2000). Hypothesis Testing: From p Values to Bayes Factors. Journal of the American Statistical Association, 95(452), 1316. doi:10.2307/2669779

Nikoli?, D., Muresan, R. C., Feng, W., & Singer, W. (2012). Scaled correlation analysis: a better way to compute a cross-correlogram. European Journal of Neuroscience, 5(4), 1–21. doi:10.1111/j.1460-9568.2011.07987.x

Székely, G. J., & Rizzo, B. N. (2017). Measuring and testing independence by correlation of distances. Annals of Statistics, 35(6), 2769–2794. doi:10.1214/009053607000000505

Wilkinson, L. (1999). Statistical Methods in Psychology Journals; Guidelines and Explanations. American Psychologist, 5(8), 594–604. doi:10.1037/0003-066X.54.8.594

### Cite This Work

My Assignment Help (2019) BUS501 Business Analytics And Statistics [Online]. Available from: https://myassignmenthelp.com/free-samples/bus501-business-analytics-and-statistics
[Accessed 14 August 2020].

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My Assignment Help. BUS501 Business Analytics And Statistics [Internet]. My Assignment Help. 2019 [cited 14 August 2020]. Available from: https://myassignmenthelp.com/free-samples/bus501-business-analytics-and-statistics.

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