Competency One Assessment - Data Analysis And Business Analytics

Data Analysis And Business Analytics Competency One– Assessment Part 1: Descriptive Statistic Analysis Does it look symmetric? No Would you prefer the IQR instead of the standard deviation to describe this variable’s dispersion? Why or why not? I would actually choose the standard deviation over the IQR when describing this variable dispersion because it is easier to use all the variables instead of the two utilized for IQR.

Data Analysis And Business Analytics Is the distribution symmetric? If not, what is the Skew? No, the distribution is not symmetric. The mean, 420.31 is more notable than the median, 396.01 resulting in it skewing to the right. Are they outliers? If so, which one(s)? Yes, there are outliners. They are 948.56 – 1058.56. What is the SQFT area of the outliner? Is the outlier(s) smaller or larger than the average restaurant in the database? What can you conclude from this observation? The SDFT area of the outliner is 1251. The outliner is smaller than the average restaurant in the database. I can conclude that the number of sales is notable larger compared to the other restaurants. What measure of central tendency is more appropriate to describe Sale/SQFT? Why? The median would be more appropriate to describe Sales/SQFT to increase the chances of being more symmetrically more data would be needed. Part 2: Report Section 1 – Scope and Descriptive Statistics Pastas R Us specialties consisted of soup, salads, and pasta-based entrees. The Pastas R Us report is aimed to gather statistics pertaining to

Data Analysis And Business Analytics spends around $7 within the restaurant. The average age of the consumers visiting the restaurants is 35 years old, and the average household income per year is approximately $62,808. Additionally, 26% of consumers live within 3 miles of the restaurant. Figure 1 Square Feet Per Person Averag e Spendin g Sales Growth Over Previo us Year (%) Loyalt y Card % of Net Sales Annual Sales Per Sq Ft Median HH Income (3 Miles) Media n Age (3 Miles) % w/ Bachelor 's Degree (3 Miles) Annual Sales Averag e 2580.47 3 7.044 7.414 2.026 420.30 5 62807.70 3 35.20 1 26.311 1084586.73 9 Stand. D 374.919 0.297 6.625 0.552 137.24 0 17904.27 3 3.655 7.005 51453.709 Skew 0.527 0.885 0.484 -0.741 1.211 0.292 -0.164 0.138 0.638 Min 1251 6.540 -8.310 0.290 178.56 0 32929 24.70 14 223378.560 Media n 2500 7.000 7.030 2.075 396.01 0 62757 35.00 26.500 990025.000 Max 3799 7.970 28.810 3.380 987.12 0 114353 43.50 40 3750068.88 0 Q1 2400 6.825 3.980 1.858 332.84 5 46953 32.52 5 20.250 798828.000 Q3 2735.25 7.178 11.423 2.325 483.56 3 76194.25 37.52 5 30.750 1322664.32 8 Inter Q 335.25 0.353 7.443 0.468 150.71 8 29241.25 5 10.500 50528.042 Section 2 – Analysis

Data Analysis And Business Analytics 10 15 20 25 30 35 40 45 0.00 200.00 400.00 600.00 800.00 1,000.00 1,200.00 f(x) = 6.7 x + 244.03 R² = 0.12 % w/ Bachelor's Degree vs Sales SqFt Bach Degree % Sales/SqFt Figure 2 A positive correlation exists between bachelor’s degree % and Sales/SqFt as displayed in figure 2. Although the impact of Sales/SqFt on the percentage of bachelor’s degrees may not be significant, there is still a noticeable positive correlation. The trend line added to the scatter plot illustrates the data pattern, indicating a positive trend. 200 00 300 00 400 00 500 00 600 00 700 00 800 00 900 00 100 00 0 110 000 120 00 0 0.00 200.00 400.00 600.00 800.00 1,000.00 1,200.00 f(x) = − 0 x + 431.11 R² = 0 Median Income Vs Sales/SqFt Sales/SqFt Figure 2.1

Data Analysis And Business Analytics - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 -20.00 -10.00 0.00 10.00 20.00 30.00 40.00 f(x) = − 3.56 x + 14.63 R² = 0.09 Loyalty Card % vs Sales Growth Loyalty Card % Sales Growth % Figure 2.4 The final comparison emphasized in Figure 2.4 is Loyalty Card (%) versus Sales Growth (%). One can see that there is a negative correlation between Loyalty Card (%) versus Sales Growth (%). The percentage of loyalty cards does impact on the percentage of sales growth. This suggests that the loyalty card does not contribute to an increase in sales growth across the restaurants, it decreases sales growth. The chart illustrates a weak negative pattern; therefore, this scatter plot reflects a moderate negative association. Section 3: Recommendations & Implementation There was a variety of data that was analyzed to see the most effective position for Pasta R Us to expand. In my opinion, the more impactful set of data that was analyzed was the college-educated adult by the annual sales per SqFt. The data highlighted showed a positive

Data Analysis And Business Analytics connection between annual sales and the college-educated adult, making this my recommended focus on how Pasta R Us expansion makes the most sense. Additionally, I don’t believe the loyalty card program is worth continuous investment unless redone as a whole. The data highlighted a negative connection when compared to sales per SqFt. When reviewing the data in the scatter plots, Pasta R Us consumers who are college-educated adults and about 30 years old produce a higher number of sales. I recommend Pasta R Us utilize the data and readjust their target market to those in that age bracket and have a college education, expanding closer to secondary education campuses to increase profits.