1. Identify the (least squares) estimated regression equation for the full regression model. a. How would you define each of the terms in the model b. What do each of the terms in the model represent. How would you interpret the value of each of the estimated parameter estimates in the context of the data source. c. Examine the overall ANOVA table for the full model and carry out an F test.What are the hypotheses for the F test? What are you actually testing for?. d. Ensure that you understand what each of the terms in the ANOVA table represents and how you would calculate them. Identify the relationships between SST, SSR and SSE. How would you calculate degrees of freedom (df) for each of the variance terms? e. For multiple regression, where there is more than one explanatory variable, how would you test for the significance of each of the individual explanatory variables. What test would you use, make sure you can identify this in the SAS output and you can construct the relevant hypothesis and carry out the appropriate test of significance. f. Practice using the estimated regression equation to estimate values of y for a given set of values of x, remember to use values of x which fall within the observation range. g. Make sure you understand what a residual is in the context of a multiple regression model. Show how you would calculate a residual for a particular observation.
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.
1. Identify the (least squares) estimated regression equation for the full regression model.
a. How would you define each of the terms in the model
b. What do each of the terms in the model represent. How would you interpret the value of each of the estimated parameter estimates in the context of the data source.
c. Examine the overall ANOVA table for the full model and carry out an F test.What are the hypotheses for the F test? What are you actually testing for?.
d. Ensure that you understand what each of the terms in the ANOVA table represents and how you would calculate them. Identify the relationships between SST, SSR and SSE. How would you calculate degrees of freedom (df) for each of the variance terms?
e. For multiple regression, where there is more than one explanatory variable, how would you test for the significance of each of the individual explanatory variables. What test would you use, make sure you can identify this in the SAS output and you can construct the relevant hypothesis and carry out the appropriate test of significance.
f. Practice using the estimated regression equation to estimate values of y for a given set of values of x, remember to use values of x which fall within the observation
g. Make sure you understand what a residual is in the context of a multiple regression model. Show how you would calculate a residual for a particular observation.
Step by step
Solved in 4 steps
