Suppose a random sample of 100 20-year-old men is selected from a population, and that these men’s height and weight are recorded. A regression of weight on height yields "Weight" = -79.24 + 4.16 X Height, R2 = 0.72, SER = 12.6, (3.42) (.42) where weight is measured in pounds and height is measured in inches. A man has a late growth spurt and grows 2 inches over the course of a year. Construct a 95% confidence interval for the person’s weight gain.
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.
Suppose a random sample of 100 20-year-old men is selected from a population, and that these men’s height and weight are recorded. A regression of weight on height yields "Weight" = -79.24 + 4.16 X Height, R2 = 0.72, SER = 12.6, (3.42) (.42) where weight is measured in pounds and height is measured in inches. A man has a late growth spurt and grows 2 inches over the course of a year. Construct a 95% confidence interval for the person’s weight gain.
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