StatCrunch Manatees = -49.048987 + 1.4062442 Boats Sample size: 24 R (correlation ooefficient) = 0.85014394 Estimate of error standard deviation: 9.6605284 Predicted values: X value Pred. Y s.e.(Pred. y) 95% C.I. for mean 95% P.I. for new 1.9724935(66.391071, 74.572473)(50.033706, 90.929839) 85 70.481772
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.
Identifying Total Variation What percentage of the total variation in manatee fatalities can be explained by the
From the output, the correlation coefficient is 0.8501.
The following points are the accepted guidelines for interpreting the correlation coefficient:
- 0 indicates no linear relationship.
- +1 indicates a perfect positive linear relationship.
- −1 indicates a perfect negative linear relationship.
- Values between 0 and 0.4 (0 and −0.4) indicate a weak positive (negative) linear relationship.
- Values between 0.4 and 0.7 (0.4 and −0.7) indicate a moderate positive (negative) linear relationship.
- Values between 0.7 and 1.0 (−0.7 and −1.0) indicate a strong positive (negative) linear relationship through a firm linear rule.
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