The regression equation is Y = -1.41 + .0235 XI + .00486 X2 Predictor Coef SE Coef T Constant -1.4053 .4848 X1 X2 .023467 .008666 .001077 S = .1298 R-Sq = R-Sq (adj) Analysis of Variance SS 1.76209 SOURCE DF MS Regression Residual Error Total 1.88000
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.
Analyzing College Grade Point Average. Recall that in exercise 49, the admissions officer for Clearwater College developed the following estimated regression equation relating final college GPA to the student's SAT mathematics score and high-school GPA.
y = -1.41 + 0.235x1 + 0.00486x2
where
x1 = high-school grade point average
x2 = SAT mathematics score
y = final college grade point average
A portion of the associated computer output follows.
a. Complete the missing entries in this output.
b. Use the F test and a .05 level of significance to see whether a significant relationship is present
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