Chat with us, powered by LiveChat As a?sales manager, you will?use statistical methods to support actionable business decisions?for Pastas R Us, Inc., a fast-casual restaurant chain specializing in noodl - EssayAbode

As a?sales manager, you will?use statistical methods to support actionable business decisions?for Pastas R Us, Inc., a fast-casual restaurant chain specializing in noodl

 

As a sales manager, you will use statistical methods to support actionable business decisions for Pastas R Us, Inc., a fast-casual restaurant chain specializing in noodle-based dishes, soups, and salads. In simpler terms, you are reviewing available information to determine if what your company is doing works. In this assessment, you use predictive and qualitative analysis skills to create a report for the executive team from available Pastas R Us, Inc. data about the effectiveness of the current expansion criteria, loyalty card program, and marketing position. 

Scenario

Since its inception, the Pastas R Us business development team has favored opening new restaurants in areas that satisfy the following demographic conditions within a 3-mile radius:

  • Median age is between 25–45 years old.
  • Household median income is above the national average.
  • At least 15% of the adult population is college educated.

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 1,200 sq. ft. restaurant recorded $2 million in sales last year, then it sold $1,667 per sq. ft. 

Preparation

Analyze the Pastas R Us charts file for your report, including the scatter plots and regression equations for the following pairs of variables:

  • "Sales/Sq.Ft. ($)” versus “Bach. Degrees (%)”
  • “Median Income ($)” versus “Sales/Sq.Ft. ($)”
  • “Median Age (Years)” versus “Sales/Sq.Ft. ($)”
  • “Loyalty Card (%)” versus “Sales Growth (%)”
Assessment Deliverable

Write a 700- to 1,050-word predictive and qualitative analysis report of Pastas R Us, Inc. that includes the following sections: scope and descriptive statistics, analysis, and recommendations and implementation. 

Section 1: Scope and descriptive statistics

  • State the report’s objective.
  • Discuss the nature of the current data. What variables were analyzed?
  • Summarize your descriptive statistical findings from Week 1.

Section 2: Analysis 

  • Interpret the scatter plots and designate the type of relationship (increasing/positive, decreasing/negative, or no relationship) observed in each one.
  • Determine what you can conclude from these relationships. You may include a copy of each chart in your report, but it is not required. 

Section 3: Recommendations and implementation

  • Based on the findings, assess which expansion criteria seem to be more effective. Could any expansion criterion be changed or eliminated? If so, which one(s) and why?
  • Based on the findings, does it appear as if the loyalty card is positively correlated with sales growth? Would you recommend any changes to this marketing strategy?
  • Based on the findings, recommend market positioning that targets a specific demographic. (Hint: Are younger people patronizing the restaurants more than older people?) 
  • Include how the local culture and communities are represented in your market position in your recommendations.
  • 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 surveys/samples or census?)

Format your references according to APA guidelines.

ChartDataSheet_

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This worksheet contains values required for MegaStat charts.
Residuals X data 3/19/2007 7:49.25
66 18 45177 34.4 31
69 16 51888 41.2 20
67 10 51379 40.3 24
70 4 66081 35.4 29
78 0 50999 31.5 18
62 28 41562 36.3 30
70 28 44196 35.1 14
84 29 50975 37.6 33
68 22 72808 34.9 28
60 42 79070 34.8 29
80 36 78497 36.2 39
64 32 41245 32.2 23
80 22 33003 30.9 22
88 78 90988 37.7 37
42 35 37950 34.3 24
68 32 45206 32.4 17
80 48 79312 32.1 37
84 32 37345 31.4 22
35 27 46226 30.4 36
84 24 70024 33.9 34
78 16 54982 35.6 26
80 39 54932 35.9 20
70 70 34097 33.6 20
76 33 46593 37.9 26
56 12 51893 40.6 21
65 32 88162 37.7 37
62 0 89016 36.4 34
66 20 114353 40.9 34
76 24 75366 35 30
92 36 48163 26.4 16
112 34 49956 37.1 28
66 15 45990 30.3 36
70 28 45723 31.3 18
60 15 43800 29.6 36
86 10 68711 32.9 18
76 0 65150 40.7 24
68 16 39329 29.3 22
64 0 63657 37.3 29
52 36 67099 39.8 25
78 26 75151 33.9 28
64 28 93876 35 40
82 32 79701 35 39
86 30 77115 35.9 30
92 16 52766 33 17
72 10 32929 30.9 22
90 24 87863 38.5 29
64 20 73752 40.5 19
80 20 85366 32.1 29
102 30 39180 34.8 18
70 26 56077 38 19
62 26 77449 37 34
68 20 56822 34.7 25
74 24 80470 36.4 30
84 14 55584 36.8 21
70 32 78001 32.2 30
96 32 75307 34.8 30
70 22 76375 36.7 28
76 32 61857 33.8 31
62 28 61312 34.2 16
92 23 72040 39 31
60 20 92414 34.9 40
54 15 92602 39.3 33
110 23 59599 35.6 28
78 0 72453 36 23
72 31 67925 41.1 16
74 29 42631 24.7 25
94 0 75652 40.5 25
80 16 39650 32.9 18
124 0 48033 30.3 15
46 20 67403 36.2 19
66 0 80597 32.4 27
63 28 60928 43.5 21
72 15 73762 41.6 29
76 24 64225 31.4 15
NormalPlot data 3/19/2007 7:49.03
-259.9497306439 -2.3669115357
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-123.016384493 -1.0070695657
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10.1026389027 0.0844037498
10.1556691582 0.1183004556
11.5424324103 0.152333674
13.4792557233 0.1865443062
15.0213966843 0.220974732
18.9663798116 0.2556692022
19.2122025279 0.2906742745
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23.7470781354 0.3980640685
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75.1828858929 0.8512709934
75.2802525469 0.9007655189
81.3206554357 0.952571595
81.6351025536 1.0070695657
105.576464442 1.0647357757
115.0253844293 1.1261791757
122.2233804168 1.1921973902
150.1178490106 1.2638662791
180.3934285167 1.3426905457
196.2671375845 1.4308738679
205.7993140008 1.5318456091
234.8563337617 1.6514108613
289.7338930992 1.8007082352
336.0904495556 2.0061237235
372.5195939607 2.3669115357
Residuals X data 3/19/2007 8:01.41
66 18 45177 34.4 31
69 16 51888 41.2 20
67 10 51379 40.3 24
70 4 66081 35.4 29
78 0 50999 31.5 18
62 28 41562 36.3 30
70 28 44196 35.1 14
84 29 50975 37.6 33
68 22 72808 34.9 28
60 42 79070 34.8 29
80 36 78497 36.2 39
64 32 41245 32.2 23
80 22 33003 30.9 22
88 78 90988