A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.891 b=30.426 r2=0.550564 r=-0.742 Use this to predict the number of situps a person who watches 11 hours of TV can do (to one decimal place)
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 regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).
The results of the regression were:
y=ax+b
a=-0.891
b=30.426
r2=0.550564
r=-0.742
Use this to predict the number of situps a person who watches 11 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and the number of situps a person can do (y) is,
=a*X+b
=-0.891*X+ 30.426
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