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# Locating New Pam And Susan’s Stores

1233 words - 5 pages

Multiple Regression Project

Locating New Pam and Susan’s Stores

Introduction

Pam and Susan’s department stores are in the process of opening a new business unit. There are two locations that are being considered for the new store and decision is based upon estimates of sales for both of them. My job is to use data gathered from each store as well census data in store’s trading zones to predict sales at both of the sites that are being consider for their newest store.

Data
We have data out of 250 stores. The data include demographics, economics, sales of the stores, compositions of those sales as well as sales behavior per households. There are 31 variables being consider for ...view middle of the document...

59996 |
%inc10-14 0.61405 |
%inc0-10 0.61505 |
%freezer -0.63945 |
%dryers -0.65733 |
%owners -0.68985 |
%nocars 0.70094 |

To obtain the regressing output table for sales, I used those 10 variables, sales, comptype1, comptype2 and comptype7. Seven models were created in order to obtain the model in which all-remaining variables are statistically significant and the final equation to predict sales is as follow:
Sales (in \$ 1000s)= 16,020.78118 + 149.15175 * %spanishsp – 44.16538 * %dryers – 112.48017 * %freezer – 79.84655 * %sch0-8 + 9,393.82229 * comtype1 + 3,802.26442 * comtype2 – 3,123.24462 * comtype7

Regression Statistics |
R | 0.86485 | | |
R-square | 0.74797 | | |
Adjusted R-square | 0.74068 | | |
S | 2,776.51435 | | |
N | 250 | | |
| | | |
| Coefficient | Standard Error | t Stat | p-level |
Intercept | 16,020.78118 | 1,157.29255 | 13.84333 | 0. |
%spanishsp | 149.15175 | 55.74166 | 2.67577 | 0.00796 |
%dryers | -44.16538 | 16.10174 | -2.74289 | 0.00655 |
%freezer | -112.48017 | 39.36158 | -2.85761 | 0.00464 |
%sch0-8 | -79.84655 | 30.30416 | -2.63484 | 0.00896 |
comtype1 | 9,393.82229 | 862.77706 | 10.88789 | 0. |
comtype2 | 3,802.26442 | 518.76015 | 7.32952 | 3.4437E-12 |
comtype7 | -3,123.24462 | 562.54923 | -5.55195 | 7.41169E-8 |

Question 1
After reviewing the regression equation and statistics, there is a high % of Spanish Speaking population, low % of people with dryers and freezers and sales are high in locations with a lower competitive type and with high population. Higher sales are in densely populated areas with relatively little direct competition and lower sales are in stores located along the sides of major roads as we can see on comtype 7.

Question 2
The planning department created a competitive type classification for potential sites that range from 1 to 7. Only three comptypes were considered for the regression models as per instructed for the project along with sales and the 10 variables with the highest correlation with sales.
comtype1 | 9,393.82229 |
comtype2 | 3,802.26442 |
comtype7 | -3,123.24462 |

All comptypes are highly significant according to the t-test of significance of regression coefficient. Expected sales decrease as the value of the competitive type increase.

Question 3
| Coefficient | A | B | PREDICT A | PREDICT B |
Intercept | 16,020.78118 | 1 | 1 | 16020.78118 | 16020.78118 |
%spanishsp | 149.15175 | 10.8 | 6.6 | 1610.8389 | 984.40155 |
%dryers | -44.16538 | 9 | 12.2 | -397.48842 | -538.817636 |
%freezer | -112.48017 | 6.1 | 5 | -686.129037 | -562.40085 |
%sch0-8 | -79.84655 | 37.4 | 40.1 | -2986.26097 | -3201.846655 |
comtype1 | 9,393.82229 | 1 | 0 | 9393.82229 | 0 |
comtype2 | 3,802.26442 | 0 | 0 | 0 | 0 |
comtype7 | -3,123.24462 | 0 | 0 | 0 | 0 |
...

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