The regression line is: y=y= Answer ++ Answer xx Based on the regression line, we would expect the value of response variable to be Answer when the explanatory variable is 0. For each increase of 1 in of the explanatory variable, we can expect a(n) Answerdecreaseincrease of Answer in the response variable. If x=3.5x=3.5, the yˆ=y^= Answer. This is an example of Answerextrapolationinterpolation . The correlation coefficient is r=r= Answer. (Round to the nearest hundredth.)
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.
12) Data Set: {(1,4),(2,6),(3,8),(4,10),(5,12),(6,14),(7,16)}{(1,4),(2,6),(3,8),(4,10),(5,12),(6,14),(7,16)}
- The regression line is: y=y= Answer ++ Answer xx
- Based on the regression line, we would
expect the value of response variable to be Answer when the explanatory variable is 0. - For each increase of 1 in of the explanatory variable, we can expect a(n) Answerdecreaseincrease
of Answer in the response variable.
- If x=3.5x=3.5, the yˆ=y^= Answer. This is an example of Answerextrapolationinterpolation
.
- The
correlation coefficient is r=r= Answer. (Round to the nearest hundredth.)
13) Data Set: {(1,−2.5),(2,−4),(3,−5.5),(4,−6),(5,−6.5),(6,−8),(7,−8.5)}{(1,−2.5),(2,−4),(3,−5.5),(4,−6),(5,−6.5),(6,−8),(7,−8.5)}
- The regression line is: y=y= Answer −− Answer xx
- Based on the regression line, we would expect the value of response variable to be Answer when the explanatory variable is 0.
- For each increase of 1 in of the explanatory variable, we can expect a(n) Answerdecreaseincrease
of Answer in the response variable.
- If x=2.5x=2.5, the yˆ=y^= Answer. This is an example of Answerextrapolationinterpolation
.
- The correlation coefficient is r=r= Answer. (Round to the nearest hundredth.)
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