Question

Asked Feb 21, 2019

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2.1 Sales personnel want to further develop the model to include other variables.

Merchandise Sales ($10,000s) |
Tickets Sold (1000s) |
Win or Loss (W or L) |
Event includes fan giveaway (Y or N) |

15 |
54 |
W |
N |

17 |
67 |
L |
Y |

10 |
53 |
L |
Y |

9 |
49 |
W |
Y |

16 |
59 |
W |
N |

14 |
58 |
L |
Y |

12 |
56 |
W |
N |

19 |
63 |
W |
Y |

18 |
65 |
L |
Y |

17 |
61 |
L |
Y |

- Should you keep either or both of the variables in the model and why?
- What is the regression equation (only include variables that should be in the model)?
- What is the merchandise sales forecast for 58,000 tickets sold at a winning event that does not include a fan giveaway?

Step 1

**Introduction to linear regression analysis:**

Regression analysis is a collection of statistical procedures used to estimate the relationship among variables.

Suppose one variable is thought to be dependent on one or more other variables. Then, statistical methods can be used to form a model of this dependent variable, often called the response variable, on the independent variables, known as regressors or predictors. This procedure is called regression analysis.

Step 2

**Indicator variable:**

An indicator or a dummy variable is created for using a qualitative predictor variable in a regression model, which assigns a unique number to indicate each value or category of the qualitative predictor variable. If there are *k* categories, then (*k* – 1) indicator variables are required.

Step 3

**The given data:**

In this study, the Merchandise Sales (in $10,000’s) is the response variable, which depends upon Tickets Sold (in 1,000s), Win or loss and Event includes fan giveaway.

Denote *y* as the Merchandise Sales (in $10,000’s), *x*1 as the Tickets Sold (in 1,0...

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