he sample statistics for this dataset are: Height: X-bar= 67.9931 Sx= 1.90168 Weight: Y-bar: 127.0794 sy: 11.66090 Linear correlation coefficient: 0.502859 Determine the regression line and use it to predict the weight (in pounds) of a child who is 72.50 inches tall. Round you answer to two digits after the decimal 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.
The sample statistics for this dataset are:
Height: X-bar= 67.9931 Sx= 1.90168
Weight: Y-bar: 127.0794 sy: 11.66090
Linear
Determine the regression line and use it to predict the weight (in pounds) of a child who is 72.50 inches tall. Round you answer to two digits after the decimal point.
X is the height
Y is the weight
The sample statistics is given below
correlation coefficient (r) = 0.502859
The regression line is of the following format
Y = a + bX
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