John's parents recorded his height at various ages up to 66 months. They decide to use the least-squares regression line of John's height on age to predict his height at age 21 years (252 months). We conclude: John's height, in inches, should be about half his age, in months. that the parents will get a fairly accurate estimate of his height at age 21 years because the data are clearly correlated. such a prediction could be misleading because it involves extrapolation (predicting beyond the scope of the model). none of the above.
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.
- John's height, in inches, should be about half his age, in months.
- that the parents will get a fairly accurate estimate of his height at age 21 years because the data are clearly
correlated. - such a prediction could be misleading because it involves extrapolation (predicting beyond the scope of the model).
- none of the above.
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