The country manager of a retail chain (which has 150 stores) is finalizing plans

Question

The country manager of a retail chain (which has 150 stores) is finalizing plans for sales promotion strategies. Data pertaining to stores such as store location, sales turnover, store size, staf(FTE), and profit margin are stored. The manager wants to find out what can explain profit margin and how to manage stores more efficiently and effectively.

You will use regression analysis of retail stores data to assist the manager in developing promotion strategies.

Part 1a. Purpose of the report: To describe what you are going to do (1 paragraph)

1b. Research Question: What is your main question to find out from this analysis?

1c. Hypotheses: Develop hypotheses to give answers for the research question. (H0 & H1)

After performing REGRESSION ANALYSIS

PART 2:

a. Explain variables (DV and IVs)

b. Explain method like Enter, Stepwise, ...

Part 3: Results

a. Descriptive Analysis

b. R2

c. Model

d. Variables

PART 4: Interpretation of results (Explain what results say)

1.8

Store Sales Turnover Store Size FTE (staff size) Profit Margin New York 5.1 3500 1.4 0.2 Los Angeles 4.9 3000 1.4 0.2 Chicago 4.7 3200 1.3 0.2 Houston 4.6 3100 1.5 0.2 Philadelphia 5 3600 1.4 0.2 Phoenix 5.4 3900 1.7 0.4 San Antonio 4.6 3400 1.4 0.3 San Diego 5 3400 1.5 0.2 Dallas 4.4 2900 1.4 0.2 San Jose 4.9 3100 1.5 0.1 Jacksonville 5.4 3700 1.5 0.2 Indianapolis 4.8 3400 1.6 0.2 San Francisco 4.8 3000 1.4 0.1 Austin 4.3 3000 1.1 0.1 Columbus 5.8 4000 1.2 0.2 Fort Worth 5.7 4400 1.5 0.4 Charlotte 5.4 3900 1.3 0.4 Detroit 5.1 3500 1.4 0.3 El Paso 5.7 3800 1.7 0.3 Memphis 5.1 3800 1.5 0.3 Baltimore 5.4 3400 1.7 0.2 Boston 5.1 3700 1.5 0.4 Seattle 4.6 3600 1 0.2 Washington 5.1 3300 1.7 0.5 Nashville 4.8 3400 1.9 0.2 Denver 5 3000 1.6 0.2 Louisville 5 3400 1.6 0.4 Milwaukee 5.2 3500 1.5 0.2 Portland 5.2 3400 1.4 0.2 Las Vegas 4.7 3200 1.6 0.2 Oklahoma City 4.8 3100 1.6 0.2 Albuquerque 5.4 3400 1.5 0.4 Tucson 5.2 4100 1.5 0.1 Fresno 5.5 4200 1.4 0.2 Sacramento 4.9 3100 1.5 0.1 Long Beach 5 3200 1.2 0.2 Kansas City 5.5 3500 1.3 0.2 Mesa 4.9 3100 1.5 0.1 Virginia Beach 4.4 3000 1.3 0.2 Atlanta 5.1 3400 1.5 0.2 Colorado Springs 5 3500 1.3 0.3 Omaha 4.5 2300 1.3 0.3 Raleigh 4.4 3200 1.3 0.2 Miami 5 3500 1.6 0.6 Cleveland 5.1 3800 1.9 0.4 Tulsa 4.8 3000 1.4 0.3 Oakland 5.1 3800 1.6 0.2 Minneapolis 4.6 3200 1.4 0.2 Wichita 5.3 3700 1.5 0.2 Arlington 5 3300 1.4 0.2 Bakersfield 7 3200 4.7 1.4 New Orleans 6.4 3200 4.5 1.5 Honolulu 6.9 3100 4.9 1.5 Anaheim 5.5 2300 4 1.3 Tampa 6.5 2800 4.6 1.5 Aurora 5.7 2800 4.5 1.3 Santa Ana 6.3 3300 4.7 1.6 Saint Louis 4.9 2400 3.3 1 Pittsburgh 6.6 2900 4.6 1.3 Corpus Christi 5.2 2700 3.9 1.4 Riverside 5 2000 3.5 1 Cincinnati 5.9 3000 4.2 1.5 Lexington 6 2200 4 1 Anchorage 6.1 2900 4.7 1.4 Stockton 5.6 2900 3.6 1.3 Toledo 6.7 3100 4.4 1.4 Saint Paul 5.6 3000 4.5 1.5 Newark 5.8 2700 4.1 1 Greensboro 6.2 2200 4.5 1.5 Buffalo 5.6 2500 3.9 1.1 Plano 5.9 3200 4.8 1.8 Lincoln 6.1 2800 4 1.3 Henderson 6.3 2500 4.9 1.5 Fort Wayne 6.1 2800 4.7 1.2 Jersey City 6.4 2900 4.3 1.3 Saint Petersburg 6.6 3000 4.4 1.4 Chula Vista 6.8 2800 4.8 1.4 Norfolk 6.7 3000 5 1.7 Orlando 6 2900 4.5 1.5 Chandler 5.7 2600 3.5 1 Laredo 5.5 2400 3.8 1.1 Madison 5.5 2400 3.7 1 Winston-Salem 5.8 2700 3.9 1.2 Lubbock 6 2700 5.1 1.6 Baton Rouge 5.4 3000 4.5 1.5 Durham 6 3400 4.5 1.6 Garland 6.7 3100 4.7 1.5 Glendale 6.3 2300 4.4 1.3 Reno 5.6 3000 4.1 1.3 Hialeah 5.5 2500 4 1.3 Chesapeake 5.5 2600 4.4 1.2 Scottsdale 6.1 3000 4.6 1.4 North Las Vegas 5.8 2600 4 1.2 Irving 5 2300 3.3 1 Fremont 5.6 2700 4.2 1.3 Irvine 5.7 3000 4.2 1.2 Birmingham 5.7 2900 4.2 1.3 Rochester 6.2 2900 4.3 1.3 San Bernardino 5.1 2500 3 1.1 Spokane 5.7 2800 4.1 1.3 Gilbert 6.3 3300 6 2.5 Arlington 5.8 2700 5.1 1.9 Montgomery 7.1 3000 5.9 2.1 Boise 6.3 2900 5.6 1.8 Richmond 6.5 3000 5.8 2.2 Des Moines 7.6 3000 6.6 2.1 Modesto 4.9 2500 4.5 1.7 Fayetteville 7.3 2900 6.3 1.8 Shreveport 6.7 2500 5.8 1.8 Akron 7.2 3600 6.1 2.5 Tacoma 6.5 3200 5.1 2 Oxnard 6.4 2700 5.3 1.9 Aurora 6.8 3000 5.5 2.1 Fontana 5.7 2500 5 2 Yonkers 5.8 2800 5.1 2.4 Augusta 6.4 3200 5.3 2.3 Mobile 6.5 3000 5.5 1.8 Little Rock 7.7 3800 6.7 2.2 Moreno Valley 7.7 2600 6.9 2.3 Glendale 6 2200 5 1.5 Amarillo 6.9 3200 5.7 2.3 Huntington Beach 5.6 2800 4.9 2 Columbus 7.7 2800 6.7 2 Grand Rapids 6.3 2700 4.9 1.8 Salt Lake City 6.7 3300 5.7 2.1 Tallahassee 7.2 3200 6 1.8 Worcester 6.2 2800 4.8 1.8 Newport News 6.1 3000 4.9 1.8 Huntsville 6.4 2800 5.6 2.1 Knoxville 7.2 3000 5.8 1.6 Providence 7.4 2800 6.1 1.9 Santa Clarita 7.9 3800 6.4 2 Grand Prairie 6.4 2800 5.6 2.2 Brownsville 6.3 2800 5.1 1.5 Jackson 6.1 2600 5.6 1.4 Overland Park 7.7 3000 6.1 2.3 Garden Grove 6.3 3400 5.6 2.4 Santa Rosa 6.4 3100 5.5 1.8 Chattanooga 6 3000 4.8 1.8 Oceanside 6.9 3100 5.4 2.1 Fort Lauderdale 6.7 3100 5.6 2.4 Rancho Cucamonga 6.9 3100 5.1 2.3 Port Saint Lucie 5.8 2700 5.1 1.9 Ontario 6.8 3200 5.9 2.3 Vancouver 6.7 3300 5.7 2.5 Tempe 6.7 3000 5.2 2.3 Springfield 6.3 2500 5 1.9 Lancaster 6.5 3000 5.2 2 Eugene 6.2 3400 5.4 2.3 Pembroke Pines 5.9 3000 5.1

1.8

Explanation / Answer

1a. Purpose of the report:

We have data of 150 stores to finalize plans for sales promotion strategies. Data pertaining to stores such as store location, sales turnover, store size, staf(FTE), and profit margin are stored. We need to find out what can explain profit margin.

1b. Research Question: What is your main question to find out from this analysis?

We need to identify which independent variables are affecting the profit margin and on this basis we can take actions on those factors to increase profit margin.

1c. Hypotheses: Develop hypotheses to give answers for the research question. (H0 & H1)

H0: Profit Margin, Store Size and Staff Size does not affect Sales Turnover.

H1: Atleast one of Profit Margin, Store Size and Staff Size affects Sales Turnover.

2a. Explain variables (DV and IVs)

Here Dependent Variable is Sales Turnover and Independent Variables are Profit Margin, Store Size and Staff Size.

2b. Explain method like Enter, Stepwise, ...

Part 3: Results

a. Descriptive Analysis:

Residuals:

Min 1Q Median 3Q Max

-0.82564 -0.21924 0.01471 0.19843 0.84677

Coefficients:

Estimate Std. Error t value Pr(>|t|)   

(Intercept) 1.845e+00 2.504e-01 7.368 1.18e-11 ***

Store.Size 6.549e-04 6.667e-05 9.823 < 2e-16 ***

FTE..staff.size. 7.111e-01 5.661e-02 12.560 < 2e-16 ***

Profit.Margin -5.626e-01 1.271e-01 -4.426 1.87e-05 ***

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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3139 on 146 degrees of freedom

Multiple R-squared: 0.8592, Adjusted R-squared: 0.8563

F-statistic: 297 on 3 and 146 DF, p-value: < 2.2e-16

b. R2=0.8563

c. Model: Sales_Turnover= Beta1*Store_Size+Beta2*Staff_Size+Beta3*Profit_Margin+error

d. Variables: IVs:Store_Size, Staff_Size, Profit_Margin DV: Sales_Turnover

PART 4: Interpretation of results (Explain what results say)

The Variables are insignificant, i.e, We accept null hypothesis.

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