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A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatter plo of her results and computer regression analysis of egg production versus fish length are given below. Note that Number of eggs is given in thousands (1.e., "40" means 40,000 eggs). Egg production vs fish length 70 60 50 40 30 - 20 4.2 4.4 4.6 4.8 5.0 5.2 Fish length, cm. Predictor Coef SE Coef T P Constant 25.55 0.697 0.13 0.899 Calories 5.392 0.2409 2.61 0.027 S = 6.751 R-Sq = 83.5$ R-Sq (adj) = 74.88 Use Scenario 3-8. The equation of the least-squares regression line is ỹ = 5.392 + 25.55x '= 0.697 + 0.2409x ỹ = 25.55 + 5.392x ý = 0.2409 + 0.697x Number of eggs (in thousands)

A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatter plo of her results and computer regression analysis of egg production versus fish length are given below. Note that Number of eggs is given in thousands (1.e., "40" means 40,000 eggs). Egg production vs fish length 70 60 50 40 30 - 20 4.2 4.4 4.6 4.8 5.0 5.2 Fish length, cm. Predictor Coef SE Coef T P Constant 25.55 0.697 0.13 0.899 Calories 5.392 0.2409 2.61 0.027 S = 6.751 R-Sq = 83.5$ R-Sq (adj) = 74.88 Use Scenario 3-8. The equation of the least-squares regression line is ỹ = 5.392 + 25.55x '= 0.697 + 0.2409x ỹ = 25.55 + 5.392x ý = 0.2409 + 0.697x Number of eggs (in thousands)

College Algebra
College Algebra
10th Edition
ISBN:9781337282291
Author:Ron Larson
Publisher:Ron Larson
Chapter3: Polynomial Functions
Section3.5: Mathematical Modeling And Variation
Problem 2ECP
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A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatter plot of her results and computer regression analysis of egg production versus fish length are given below. Note that Number of eggs is given in thousands (1.e., "40" means 40,000 eggs). Egg production vs fish length 70 60 50 40 30 20 4.2 4.4 4.6 4.8 5.0 5.2 Fish length, cm. Predictor Coef SE Coef T P Constant 25.55 0.697 0.13 0.899 Calories 5.392 0.2409 2.61 0.027 S = 6.751 R-Sg = 83.5 R-Sq (adj) = 74.88 Use Scenario 3-8. The equation of the least-squares regression line is O ŷ = 5.392 + 25.55x O ŷ = 0.697 + 0.2409x O ỹ = 25.55 + 5.392x O ŷ = 0.2409 + 0.697x Number of eggs (in thousands)
Transcribed Image Text:A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A scatter plot of her results and computer regression analysis of egg production versus fish length are given below. Note that Number of eggs is given in thousands (1.e., "40" means 40,000 eggs). Egg production vs fish length 70 60 50 40 30 20 4.2 4.4 4.6 4.8 5.0 5.2 Fish length, cm. Predictor Coef SE Coef T P Constant 25.55 0.697 0.13 0.899 Calories 5.392 0.2409 2.61 0.027 S = 6.751 R-Sg = 83.5 R-Sq (adj) = 74.88 Use Scenario 3-8. The equation of the least-squares regression line is O ŷ = 5.392 + 25.55x O ŷ = 0.697 + 0.2409x O ỹ = 25.55 + 5.392x O ŷ = 0.2409 + 0.697x Number of eggs (in thousands)
A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A sc of her results and computer regression analysis of egg production versus fish length are given below. Note that Number of eggs is given in thousands (1.e., "40" means 40,000 eggs). Egg production vs fish length 70 - 60 - 50 40 30 - 20 - 4.2 4.4 4.6 4.8 5.0 5.2 Fish length, cm. Predictor Coef SE Coef T P Constant 25.55 0.697 0.13 0.899 Calories 5.392 0.2409 2.61 0.027 S = 6.751 R-Sq = 83.5$ R-Sq (adj) = 74.88 Use Scenario 3-8. On average, how far are the predicted y-values from the actual y-values? 0.835 0.697 0.748 O 6.751 Number of eggs (in thousands)
Transcribed Image Text:A fisheries biologist studying whitefish in a Canadian Lake collected data on the length (in centimeters) and egg production for 25 female fish. A sc of her results and computer regression analysis of egg production versus fish length are given below. Note that Number of eggs is given in thousands (1.e., "40" means 40,000 eggs). Egg production vs fish length 70 - 60 - 50 40 30 - 20 - 4.2 4.4 4.6 4.8 5.0 5.2 Fish length, cm. Predictor Coef SE Coef T P Constant 25.55 0.697 0.13 0.899 Calories 5.392 0.2409 2.61 0.027 S = 6.751 R-Sq = 83.5$ R-Sq (adj) = 74.88 Use Scenario 3-8. On average, how far are the predicted y-values from the actual y-values? 0.835 0.697 0.748 O 6.751 Number of eggs (in thousands)
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