A regression model to predict the price of diamonds Included the following predictor variables: the weight of the stone (in carats where 1 carat = 0.2 gram), the color rating (D, E, F, G, H, or I), and the clarity rating (IF, VVS1, VVS2, VS1, or VS2). (a) Identify the quantitative predictor variable(s). (You may select more than one answer. Click the box with a check mark for the correct answer and double click to empty the box for the wrong answer.) Weight ? Color Rating ? Clarity Rating (b) How many Indicator variables would be included in the model in order to prevent the least squares estimation from falling? Indicator Variables

Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015
1st Edition
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:HOUGHTON MIFFLIN HARCOURT
Chapter4: Writing Linear Equations
Section: Chapter Questions
Problem 14CR
Question

Please no written by hand solution

 

A regression model to predict the price of diamonds Included the following predictor variables: the weight of the stone (in carats
where 1 carat = 0.2 gram), the color rating (D, E, F, G, H, or I), and the clarity rating (IF, VVS1, VVS2, VS1, or VS2).
(a) Identify the quantitative predictor variable(s). (You may select more than one answer. Click the box with a check mark for the
correct answer and double click to empty the box for the wrong answer.)
Weight
? Color Rating
? Clarity Rating
(b) How many Indicator variables would be included in the model in order to prevent the least squares estimation from falling?
Indicator Variables
Transcribed Image Text:A regression model to predict the price of diamonds Included the following predictor variables: the weight of the stone (in carats where 1 carat = 0.2 gram), the color rating (D, E, F, G, H, or I), and the clarity rating (IF, VVS1, VVS2, VS1, or VS2). (a) Identify the quantitative predictor variable(s). (You may select more than one answer. Click the box with a check mark for the correct answer and double click to empty the box for the wrong answer.) Weight ? Color Rating ? Clarity Rating (b) How many Indicator variables would be included in the model in order to prevent the least squares estimation from falling? Indicator Variables
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