Consider the following data for two variables, x and y.x 2 3 4 5 7 7 7 8 9y 4 5 4 6 4 6 9 5 11a. Does there appear to be a linear relationship between x and y? Explain.b. Develop the estimated regression equation relating x and y.c. Plot the standardized residuals versus yˆ for the estimated regression equation developed in part (b). Do the model assumptions appear to be satisfied? Explain.d. Perform a logarithmic transformation on the dependent variable y. Develop an estimated regression equation using the transformed dependent variable. Do the modelassumptions appear to be satisfied by using the transformed dependent variable?Does a reciprocal transformation work better in this case? Explain
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.
Consider the following data for two variables, x and y.
x 2 3 4 5 7 7 7 8 9
y 4 5 4 6 4 6 9 5 11
a. Does there appear to be a linear relationship between x and y? Explain.
b. Develop the estimated regression equation relating x and y.
c. Plot the standardized residuals versus yˆ for the estimated regression equation developed in part (b). Do the model assumptions appear to be satisfied? Explain.
d. Perform a logarithmic transformation on the dependent variable y. Develop an estimated regression equation using the transformed dependent variable. Do the model
assumptions appear to be satisfied by using the transformed dependent variable?
Does a reciprocal transformation work better in this case? Explain
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