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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 scatte 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 (.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) = 74.8% Use Scenario 3-8. Which of the following statements can be made on the basis of the computer output? 83.5% of the variation in egg production can be accounted for by the linear regression. O 83.5% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in egg production can be accounted for by the linear regression. 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 scatte 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 (.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) = 74.8% Use Scenario 3-8. Which of the following statements can be made on the basis of the computer output? 83.5% of the variation in egg production can be accounted for by the linear regression. O 83.5% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in egg production can be accounted for by the linear regression. Number of eggs (in thousands)

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
6th Edition
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Bruce Crauder, Benny Evans, Alan Noell
Chapter3: Straight Lines And Linear Functions
Section3.CR: Chapter Review Exercises
Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...
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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 60 - 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) = 74.8% Use Scenario 3-8. Which of the following statements can be made on the basis of the computer output? O 83.5% of the variation in egg production can be accounted for by the linear regression. O 83.5% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in egg production can be accounted for by the linear regression. 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 60 - 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) = 74.8% Use Scenario 3-8. Which of the following statements can be made on the basis of the computer output? O 83.5% of the variation in egg production can be accounted for by the linear regression. O 83.5% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in fish length can be accounted for by the linear regression. O 74.8% of the variation in egg production can be accounted for by the linear regression. 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-Sg = 83.5$ R-Sq (adj) = 74.88 Use Scenario 3-8. What is the correlation coefficient (r)? O 0.835 O 0.913 O -0.835 O -0.913 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 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-Sg = 83.5$ R-Sq (adj) = 74.88 Use Scenario 3-8. What is the correlation coefficient (r)? O 0.835 O 0.913 O -0.835 O -0.913 Number of eggs (in thousands)
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