Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.4 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. 7.4 139 Overhead Width (cm) 8.2 203 9.9 269 8.5 184 8.6 8.4 Weight (kg) 210 207 9 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y =-220.7+ (49.7x (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.4 cm is - 101.4 kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OC. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.4 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. 7.4 139 Overhead Width (cm) 8.2 203 9.9 269 8.5 184 8.6 8.4 Weight (kg) 210 207 9 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y =-220.7+ (49.7x (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.4 cm is - 101.4 kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OC. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.
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
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Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Question
Transcribed Image Text:Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.4 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use
a significance level of 0.05.
Overhead Width (cm)
7.4
8.2
9.9
8.5
8.6
8.4
Weight (kg)
139
203
269
184
210
207
A Click the icon to view the critical values of the Pearson correlation coefficient r.
The regression equation is y =-220.7 + 49.7 x.
(Round to one decimal place as needed.)
The best predicted weight for an overhead width of 2.4 cm is - 101.4 kg.
(Round to one decimal place as needed.)
Can the prediction be correct? What is wrong with predicting the weight in this case?
O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data.
O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data.
OC. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation.
O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.
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