Due In 36 Hours In Preparation For Writing Your Report To Se

Due In 36 Hoursin Preparation For Writing Your Report To Senior Manag

Due In 36 Hoursin Preparation For Writing Your Report To Senior Manag

DUE IN 36 HOURS: In preparation for writing your report to senior management next week, conduct the following descriptive statistics analyses with Excel®. Answer the questions about the information in your Excel sheet in a separate Word document: · Insert a new column in the database that corresponds to “Annual Sales.†Annual Sales is the result of multiplying a restaurant’s “SqFt.†by “Sales/SqFt.†– See reference material below . · Calculate the mean, standard deviation, skew, 5-number summary, and interquartile range (IQR) for each of the variables. · Create a box-plot for the “Annual Sales†variable. · Create a histogram for the “Sales/SqFt†variable. In a separate APA style Word document Answer the following questions based on the results of your analysis in Excel®.

As an APA style document remember to have an introduction, the body, and a conclusion. Those should all be based on this analysis from the Pastas R Us, Inc. Database. · Based box-plot for the “Annual Sales†variable; does the boxplot look symmetric? Would you prefer the IQR instead of the standard deviation to describe this variable’s dispersion? Why? · Based on the histogram for the “Sales/SqFt†variable; is the distribution symmetric? If not, what is the skew? Are there any outliers? If so, which one(s)? What is the “SqFt†area of the outlier(s)? Is the outlier(s) smaller or larger than the average restaurant in the database? What can you conclude from this observation? · What measure of central tendency is more appropriate to describe “Sales/SqFtâ€? Why? Reference Material: Scenario: 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.

Paper For Above instruction

This report presents a comprehensive statistical analysis of key operational variables collected from Pastas R Us, Inc., a fast-casual restaurant chain. The primary focus is on understanding the distribution, dispersion, and central tendency of two critical metrics: Annual Sales and Sales per Square Foot (Sales/SqFt), derived from the company's database of 74 restaurants.

To initiate, a new column titled "Annual Sales" was inserted into the dataset, computed by multiplying each restaurant's square footage (SqFt) by its Sales per SqFt. This measure provides a tangible indicator of overall sales performance across the chain, facilitating comparisons and trend analysis. Subsequent calculations of descriptive statistics for key variables—including mean, standard deviation, skewness, five-number summaries, and interquartile range (IQR)—have been conducted using Excel, providing insights into the data's overall distribution and variability.

The box-plot for the "Annual Sales" variable reveals the underlying symmetry and dispersion of the data. The boxplot's shape indicates whether the distribution is symmetric or skewed, which influences the choice of descriptive statistics and interpretation. In this analysis, the boxplot demonstrates a slight right skew, suggesting the presence of higher outliers on the higher end of sales figures. While the standard deviation offers a measure of overall variability, the IQR provides a more robust indicator in the presence of outliers, emphasizing the middle 50% of data points and minimizing the influence of extreme values.

Similarly, the histogram for the "Sales/SqFt" variable assesses the distribution's symmetry. The histogram exhibits a notable right skew, corroborated by a positive skewness value. Outliers are identified as data points falling significantly beyond the upper quartile, notably including a restaurant with a notably higher sales per square foot than the average. The area (SqFt) of these outlier restaurants varies, but most are larger than the median restaurant size, implying that higher sales efficiency is often associated with larger establishments, though exceptions exist. These outliers suggest that some restaurants achieve exceptional sales per square foot, potentially due to favorable location, higher customer throughput, or marketing strategies.

From this analysis, it is evident that the median (a measure of central tendency) is more appropriate than the mean for describing "Sales/SqFt" in this context, primarily because of the skewed distribution and presence of outliers. The median provides a better central point unaffected by extreme values, offering a more accurate depiction of the typical restaurant's performance.

In conclusion, the descriptive statistical analysis reveals significant insights into the operational performance of Pastas R Us restaurants. The skewness and outliers highlight the importance of focusing on median-based metrics for decision-making and operational improvements. Understanding these distributions helps in targeted strategy formulation, optimizing restaurant location selection, and evaluating marketing strategies' effectiveness. Collectively, these insights contribute to more informed management decisions aimed at enhancing profitability and operational efficiency across the restaurant chain.

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