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# Making Decisions Depending on Demand

Assignment 1

Making Decisions Based on Require and Predicting

July twenty two, 2013

Making use of the sample info: The Demand pertaining to Pizza, (shown below) Let me conduct a requirement analysis and forecast pertaining to pizza. Through this examination, I decide whether Domino's should set up a presence in the neighborhood depicted inside the sample data. The test data included one reliant variable (Y) Quantity demanded and 3 independent factors (X1) price of lasagna (X2) College tuition (X3) Selling price of Carbonated drinks and (4) Location you for city and 0 for otherwise. This info included 40 observations. Desk 1 . you Sample Data: The Demand intended for Pizza

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College| Y| X1| X2| X3| X4

1| 10| 100| 14| 100| 1

2| 12| 100| 16| 95| 1

3| 13| 90| 8| 110| 1

4| 14| 95| 7| 90| 1

5| 9| 110| 11| 100| 0

6| 8| 125| 5| 100| 0

7| 4| 125| 12| 125| 1

8| 3| 150| 10| 150| 0

9| 15| 80| 18| 100| 1

10| 12| 80| 12| 90| 1

11| 13| 90| 6| 80| 1

12| 14| 100| 5| 75| 1| X1 - Price of Pizza|

13| 12| 100| 12| 100| 1| X2 - Tuition|

14| 10| 110| 10| 125| 0| X3 - Price of carbonated drinks

15| 10| 125| 14| 130| 0| X4 - Location 1 for city 0 for otherwise| 16| 12| 110| 15| 80| 1| Y= Quantity Required

17| 11| 150| 16| 90| zero

18| 12| 100| 12| 95| 1

19| 10| 150| 12| 100| zero

20| 8| 150| 10| 90| 0

21| 9| 150| 13| 95| 0

22| 10| 125| 15| 100| 1

23| 11| 125| 16| 95| you

24| 12| 100| 17| 100| zero

25| 13| 75| 10| 100| one particular

26| 10| 100| 12| 110| one particular

27| 9| 110| 6| 125| 0

28| 8| 125| 10| 90| zero

29| 8| 150| 8| 80| 0

30| 5| 150| 10| 95| 0

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I utilized Excel to calculate around regression. The resulting table is shown below. I have highlighted the results I did previously form my own decision and recommendations.

Table 1 ) 2 - Pizza Regression| | | |

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Regression Statistics| | |

Multiple R| 0. 84649854| | |

R Square| 0. 716559778| | |

Adjusted L Square| zero. 671209343| | |

Standard Error| 1 . 640478718| | |

Observations| 30| | |

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ANOVA| | | |

| df| SS| MS| F

Regression| 4| 168. 087406| 42. 521852| 15. 800505

Residual| 25| 67. 27926063| installment payments on your 6911704|

Total| 29| 237. 3666667| В | В

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| Coefficients| Regular Error| capital t Stat| P-value

Intercept| dua puluh enam. 66685004| 3. 278084646| eight. 1348876| 1 . 73E-08| X1| -0. 087750505| 0. 018062008| -4. 858292| 5. 379E-05

X2| zero. 138204092| 0. 086646076| 1 ) 5950416| zero. 1232685

X3| -0. 075895058| 0. 019225627| -3. 947599| 0. 0005667

X4| -0. 544278595| 0. 884620096| -0. 615268| 0. 5439383

Evaluating the Pourcentage of Dedication

The initial result I examined was the R Rectangular or Agent of Willpower. The value expressed in the Regression Statistics proven in the stand above can be 0. 716559788. This indicates that roughly 72% of the deviation in the quantity of pizza demanded can be described in the factors used in this analysis. As 72% from the variation in the quantity of pizzas demanded may be explained inside the variables used, I established that various other results present in this examine would be attractive deciding whether or not to open a pizza organization. Examining P-Values

In order to check the statistical significance of the variables as well as the regression equation, I evaluated the P-Values for each variable in the regression study. The most statistically significant variable can be X1 or price of pizza, then the price of fizzy drinks. Both X1 and X3 have a p benefit of less than. 05. Because of this there is more than 95% self-confidence that these variables are what drive the demand for pizzas in this study. Based on our definition of statistical significance, a tiny p-value because observed right here means that all of us wouldn't see what we...

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