model building

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Model Building erview pes of Polynomial Models cond Order (Quadratic) Model Example teraction Example (cars and speed estimating number of accidents) -Interpretation of interaction with Excel ttendance Example (nominal & continuous i.v.’s) lticollinearity Assumption Analysis mework xt up -Introduction to Time Series Analysis

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Model Building. Overview Types of Polynomial Models Second Order (Quadratic) Model Example Interaction Example (cars and speed estimating number of accidents) Interpretation of interaction with Excel Attendance Example (nominal & continuous i.v.’s) Multicollinearity Assumption Analysis - PowerPoint PPT Presentation

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Page 1: Model Building

Model Building•Overview•Types of Polynomial Models•Second Order (Quadratic) Model Example•Interaction Example (cars and speed estimating number of accidents)

-Interpretation of interaction with Excel• Attendance Example (nominal & continuous i.v.’s)•Multicollinearity Assumption Analysis•Homework•Next up

-Introduction to Time Series Analysis

Page 2: Model Building

Model Building Overview

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2nd Order Quadratic Model Example, 15.5 page 579

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R-Squared for the quadratic model (0.8623) is greater than R-Squared for the SLR Model (0.7823) and the quadratic term contributes (i.e. there is a significant quadratic association between price and sales). Thus, the quadratic model is a better fit.

86.23% of the variation in price can be explained by the quadratic relationship between sales and price.

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14.47 Interaction Example, Horsepower, Weight, and Miles Per GallonModel to estimate MPG. Is MPG associated with HP, weight, or the interaction of HP and weight?

The Model:

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Interaction Example, Horsepower, Weight, and Miles Per GallonBasic Assumptions Check

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HPLow, 68 HPHigh, 105

WeightLow, 2194

WeightHigh, 3246

Interaction Example, Horsepower, Weight, and Miles Per GallonInterpreting the interaction

Page 8: Model Building

Multicollinearity Assumption

Some of the i.v.’s are highly correlatedIdea; there is redundancy in the i.v.’sResult; distortions in the model (beta coefficients far away from true values, high standard errors, and more)

Test; Generate a correlation matrix among the i.v.’sThrow out redundant i.v.(s)

Example: Estimating the Selling Price of Homes

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Example: Estimating the Selling Price of Homes

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NewModel

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Attendance Example

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Attendance Example

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Attendance Example, Analysis and Interpretation

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Homework (#6)

14.46 Estimating Sales based on Newspaper advertising, Radio advertising, and the interaction1.Perform a basic assumptions check.2.Perform a basic mulitcollinearity assumption check.3.Is the overall model useful? (overall F-Test)4.Perform a test to determine if the interaction is significant.5.Interpret the interaction, if significant, using a simple graph.

- What is the estimation model when amount of Radio advertising spend is low (25)?- What is the estimation model when amount of Radio advertising spend is high (65)?- To develop your graph use the following 2 by 2 table will help, where the interior of the table is

estimated sales!

Newspaper$Low, 25

Newspaper$High, 55

Radio$Low, 25

Radio$High, 65

15.7 Estimating county taxes1.Perform a basic assumptions check.2.Use the basic 2nd order model form.3.Do parts a. through i.