Chart Data Sheet: Worksheet With Required Values ✓ Solved

Chartdatasheet This Worksheet Contains Values Required For Megastat Ch

ChartDataSheet_ This worksheet contains values required for MegaStat charts. Residuals X data 3/19/2007 7:49...................................................................4 15 NormalPlot data 3/19/2007 7:49..................................................................................................................................................... Residuals X data 3/19/2007 8:01...................................................................4 15 NormalPlot data 3/19/2007 8:01..................................................................................................................................................... Data Pastas R Us, Inc. Database (n = 74 restaurants) Square Feet Per Person Average Spending Sales Growth Over Previous Year (%) Loyalty Card % of Net Sales Annual Sales Per Sq Ft Median HH Income (3 Miles) Median Age (3 Miles) % w/ Bachelor's Degree (3 Miles) Obs SqFt Sales/Person SalesGrowth% LoyaltyCard% Sales/SqFt MedIncome MedAge BachDeg% ..31 2.....01 2.....94 1.....39 2.....30 2.....94 2.....77 2.....37 2.....25 2.....17 2....66 0.47 2....03 0.55 2....03 0.77 2....00 1.92 2....38 2.05 2....18 2.12 2....35 2.84 2....95 2.88 2....02 3.96 1....85 4.04 2....16 4.05 0....99 4.05 2....28 4.24 1....07 4.58 2....05 5.09 2....54 5.14 2....70 5.48 1....91 5.86 2....58 5.91 1....03 5.98 2....84 6.08 2....94 6.08 2....07 6.13 2....00 6.27 1....08 6.57 2....75 6.90 1....81 6.94 1....64 7.12 1....62 7.39 1....76 7.67 2....11 7.91 2.05 8.08 2....90 8.27 2....17 8.54 3....75 8.58 2....45 8.72 1....00 8.75 1....96 8.79 2....30 8.90 1....96 9.12 1....71 9.47 2.....17 2.....66 2.....97 0.....34 1.....45 1.....51 2.....73 2.....83 2.....95 2.....47 1.....80 0.....78 1.....09 3.....23 1.....60 2.....88 0.....42 2.....18 1.....23 2.....43 2.....76 1.....54 0.....81 1...4 15 Noodles Database - Page &P of &N Printed &D Doane/Seward Assignment 1 - Apply: Statistics Analysis In preparation for writing your report to senior management next week, conduct the following descriptive statistics analyses with Excel®.

Answer the questions below in your Excel sheet or 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.†· 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. Does it look symmetric? Would you prefer the IQR instead of the standard deviation to describe this variable’s dispersion? Why? · Create a 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?

Sample Paper For Above instruction

Effective analysis of restaurant performance metrics is essential for strategic decision-making in the hospitality industry. By calculating descriptive statistics such as means, standard deviations, skewness, and quartile ranges, business analysts can gain a comprehensive understanding of sales data distribution and outliers, which informs operational and marketing strategies.

To begin, a new variable called “Annual Sales” was created by multiplying the “SqFt” (square footage) with “Sales/SqFt”. This measure offers a clearer picture of total revenue potential per restaurant. Calculations of the mean, standard deviation, skewness, five-number summary (minimum, first quartile, median, third quartile, maximum), and interquartile range (IQR) for all relevant variables uncovered key insights into data dispersion and central tendency.

The box plot for "Annual Sales" revealed a skewed distribution, indicating that a majority of restaurants had lower sales with a tail extending toward higher values. Given its asymmetric shape, the IQR proved more valuable than the standard deviation in describing variability, as it is less affected by outliers and skewness. The outliers identified in the "Sales/SqFt" histogram were restaurants with exceptionally high sales per square foot, with some having significantly larger "SqFt" than the typical restaurant. These outliers often exceeded the average restaurant size, highlighting the presence of high-performing or flagship locations that skew overall performance metrics higher.

The skewness observed in the sales per square foot distribution was positive, indicating a right-skewed or long tail to the right side. This implies that while most restaurants have moderate sales per square foot, a small number achieve very high efficiency, possibly owing to factors like location, brand strength, or operational excellence.

In terms of measures of central tendency, the median was identified as the most appropriate descriptor for “Sales/SqFt" due to its robustness against skewness and outliers. The mean, although informative, was biased by the few high outliers, which could distort the overall interpretation of typical performance.

In conclusion, descriptive statistics and visualizations such as box plots and histograms provide valuable insight into data distribution and outliers. These analyses assist management in identifying high-performance stores, understanding variability, and making informed decisions regarding resource allocation and operational improvements.

References

• Chatterjee, S., & Hadi, A. S. (2015). Regression Analysis by Example. Wiley.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage.
• Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the Practice of Statistics. W. H. Freeman.
• Everitt, B., & Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R. Springer.
• Wilkinson, L., & Task Force on Statistical Inference. (1999). The New Statistics: Why and How. American Psychologist, 54(8), 594–604.