Descriptive Statistics Analysis Assignment

Descriptive Statistics Analysis Assignment

Scope and Descriptive Statistics

This statistics report seeks to ascertain whether Pastas R Us’s present expansion criteria may be enhanced. The current company wants to assess its loyalty card marketing plan’s efficacy, viability, and potential for expansion. In order to ascertain whether these elements are what the corporation had anticipated, the statistical report will conduct relevant studies. There are nine variables in the company’s current dataset, including the Annual Sales, which were found later. The statistical analysis used in this instance is based on the Past R Us database, which includes the mean, standard deviation, interquartile range (IQR), summary, and skew for each of the variables. Excel, a program suggested for data analysis, was used to analyze the data (Chavda et al., 2018). Sales per Person, Square Feet, Median Income, Bachelor’s Degree, Annual Sales, Loyalty Card, and Sales/SqFt are some of the other variables in the dataset. These variables are all the same size, or n = 74. Sales/SqFt, Sales Growth, Median Age, Bachelor’s Degree, Loyalty Card, and Annual Sales are the main variables that were examined in this case.

The completion of scatter plots and regression analyses of various variables, including combinations of variables, was made. The analyses’ findings show that a bachelor’s degree has a big impact on sales per square foot. The investigation also showed that Medium Income had no effect on sales per square foot, but Loyalty Cards appeared to have an impact on Sales increase over time, as seen in the scatter plots and regression equations below. Additionally, each of these variables’ standard deviation, skew, mean, summary, and IQR were calculated using the Excel program and the outcomes were tabulated as seen below:

	SqFt	Sales/Person	Sales Growth	Loyalty Card	Annual Sales Per Square Feet	Median Income	Median Age	Bach Degree	Annual Sales
Min	1251	6.54	-8.31	0.29	178.56	32929	24.7	14	499968
Q1	2400	6.825	3.98	1.8575	332.845	46953	32.525	20.25	877477.6
Median	2500	7	7.03	2.075	396.01	62757	35	26.5	1035749
Mean	2580.47297	7.044054054	7.414054054	2.026486486	420.30541	62807.7027	35.2013514	26.31081081	1059381
Q3	2735.25	7.1775	11.4225	2.325	483.5625	76194.25	37.525	30.75	1228867
Max	3799	7.97	28.81	3.38	987.12	114353	43.5	40	1746600
Std. Dev.	372.377178	0.295245747	6.579816378	0.548625882	136.30908	17782.88665	3.62977526	6.957254964	278522.3
IQR	335.25	0.3525	7.4425	0.4675	150.7175	29241.25	5	10.5	351389.4
Skew	0.52717084	0.903631503	0.493747471	-0.756891114	1.2358965	0.297838012	-0.16699582	0.140544196	0.361413

Analysis

Scatter Plots

Bach Deg v. Sales/SqFt

Sales per square foot and a bachelor’s degree are positively correlated, as shown by the scatter plot above. However, the weak association between the variables suggests a fragile positive relationship, meaning that a bachelor’s degree has a small impact on sales per square foot.

MedIncome v. Sales/SqFt

The scatter plot indicates that there is no relationship between the median income and sales per square foot. Therefore, median income does not affect sales per square foot.

MedAge v. Sales/SqFt

This scatter plot suggests a negative relationship between median age and sales per square foot. The relationship between the two variables is weak, indicating that median age has little effect on sales per square foot.

LoyaltyCard v. SalesGrowth

The scatter plot shows a weak, negative relationship between loyalty card and sales growth. This means that the loyalty card program has been negatively affecting the sales growth.

Recommendations and Implementation

It is clear from the analysis’s conclusions and the plots that have been shown that a bachelor’s degree greatly increases sales per square foot. The Sales/SqFt appears to be unaffected by Medium Income; however, the Loyalty Card Percentage appears to have an impact on the organization’s ability to increase sales. As a result of these data, it appears that the expansion requirements are better suited for the Bachelor’s Degree, which was found to positively affect sales. The Loyalty Card is the expansion criterion that needs to be assessed. The Pastas R Us Loyalty Card appears to have a detrimental effect on sales. Given that the results show a bad association between loyalty cards and sales growth, I advise Pastas R Us to rethink its marketing strategy and, if possible, do away with it entirely and adopt a new one.

The analysis’s results also show a negative relationship between sales per square foot and the median age. The company’s annual revenues are positively impacted by the median age, nevertheless. The youth actually patronize restaurants more frequently than older people, according to the sales per square foot, which are more centered on the median youth age (Orobia et al., 2020). Therefore, this may be said to be the company’s main goal. The youth are typically the primary target demographic for most enterprises. In this situation, Pastas R Us should keep its current target market in mind. The total sales made after a certain period are among the crucial data that should be gathered to help gauge the success of the suggestions and present plans. Additionally, the number of distinct or new customers in the establishment would indicate whether the suggested recommendations were successful. Another sign that the suggestions and the company’s current marketing tactics are effective is a rise in the number of clicks from visitors to the company website (Bala & Verma, 2018). The company could gather information about its clients in a variety of ways. One of these is the utilization of Google Analytics, which can assist in keeping an eye on website activity for the business. While changing the Loyalty Card program, the corporation should keep its current target market in mind.

References

Bala, M., & Verma, D. (2018, October 1). A Critical Review of Digital Marketing. Papers.ssrn.com. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3545505

Chavda, S., Bromley, T., Jarvis, P., Williams, S., Bishop, C., Turner, A. N., Lake, J. P., & Mundy, P. D. (2018). Force-Time Characteristics of the Countermovement Jump: Analyzing the Curve in Excel. Strength & Conditioning Journal, 40(2), 67–77. https://doi.org/10.1519/SSC.0000000000000353

Orobia, L. A., Tusiime, I., Mwesigwa, R., & Ssekiziyivu, B. (2020). Entrepreneurial framework conditions and business sustainability among the youth and women entrepreneurs. Asia Pacific Journal of Innovation and Entrepreneurship, 14(1), 60–75. https://doi.org/10.1108/apjie-07-2019-0059

Resources: Pastas R Us, Inc. Database & Microsoft Excel®, Wk 1: Descriptive Statistics Analysis Assignment
Scenario:

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Pastas R Us, Inc. is a fast-casual restaurant chain specializing in noodle-based dishes, soups, and salads. Since its inception, the business development team has favored opening new restaurants in areas (within a 3-mile radius) that satisfy the following demographic conditions:

Median age between 25 – 45 years old
Household median income above national average
At least 15% college educated adult population
Last year, the marketing department rolled out a Loyalty Card strategy to increase sales. Under this program, customers present their Loyalty Card when paying for their orders and receive some free food after making 10 purchases.
The company has collected data from its 74 restaurants to track important variables such as average sales per customer, year-on-year sales growth, sales per sq. ft., Loyalty Card usage as a percentage of sales, and others. A key metric of financial performance in the restaurant industry is annual sales per sq. ft. For example, if a 1200 sq. ft. restaurant recorded $2 million in sales last year, then it sold $1,667 per sq. ft.
Executive management wants to know whether the current expansion criteria can be improved. They want to evaluate the effectiveness of the Loyalty Card marketing strategy and identify feasible, actionable opportunities for improvement. As a member of the analytics department, you’ve been assigned the responsibility of conducting a thorough statistical analysis of the company’s available database to answer executive management’s questions.

Report:

Write a 750-word statistical report that includes the following sections:

Section 1: Scope and descriptive statistics
Section 2: Analysis
Section 3: Recommendations and Implementation

Section 1 – Scope and descriptive statistics

State the report’s objective.
Discuss the nature of the current database. What variables were analyzed?
Summarize your descriptive statistics findings from Excel. Use a table and insert appropriate graphs.

Section 2 – Analysis

Using Excel, create scatter plots and display the regression equations for the following pairs of variables:
“BachDeg%” versus “Sales/SqFt”
“MedIncome” versus “Sales/SqFt”
“MedAge” versus “Sales/SqFt”
“LoyaltyCard(%)” versus “SalesGrowth(%)”
In your report, include the scatter plots. For each scatter plot, designate the type of relationship observed (increasing/positive, decreasing/negative, or no relationship) and determine what you can conclude from these relationships.

Section 3: Recommendations and implementation

Based on your findings above, assess which expansion criteria seem to be more effective. Could any expansion criterion be changed or eliminated? If so, which one and why?
Based on your findings above, does it appear as if the Loyalty Card is positively correlated with sales growth? Would you recommend changing this marketing strategy?
Based on your previous findings, recommend marketing positioning that targets a specific demographic. (Hint: Are younger people patronizing the restaurants more than older people?)
Indicate what information should be collected to track and evaluate the effectiveness of your recommendations. How can this data be collected? (Hint: Would you use survey/samples or census?)