The regression equation is y = - 137.0 + 40.1 x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.9 cm is (Round to one decimal place as needed.) kg.
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.
![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 1,9 cm. Can the prediction be correct? What is wrong with p
the weight in this case? Use a significance level of 0.05.
Overhead Width (cm)
Weight (kg)
7.7
7.3
9.9
9.5
9.7
7.5
153
166
271
228
257
172
E Click the icon to view the critical values of the Pearson correlation coefficientr
The regression equation is y = - 137.0 + 40.1 x.
(Round to one decimal place as needed.)
The best predicted weight for an overhead width of 1.9 cm is kg.
(Round to one decimal place as needed.)
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