A researcher believes that there is a linear association between the level of potassium content (y) in milligrams and the amount of fiber (x) in grams in cereal. The regression line for the data is computed to be: ŷ = 36+27x rate. It was also computed that r = .62 c. Explain in detail what a residual is and what information it provides about the model and the data point.
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.
A researcher believes that there is a linear association between the level of potassium
content (y) in milligrams and the amount of fiber (x) in grams in cereal. The regression line for
the data is computed to be: ŷ = 36+27x rate. It was also computed that r = .62
c. Explain in detail what a residual is and what information it provides about the model and the data point.
Residual:
It is the difference between the predicted value() with the observed value(Y). It actually gives the distance between the regression line and the data point
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