Suppose that a researcher, using data on class size (CS) and average test scores from 102 third-grade classes, estimates the OLS regression TestScore= 509.992 + (-5.7036)× CS, R = 0.11, SER=11.3. A classroom has 24 students. The regression's prediction for that classroom's average test score is (Round your response to two decimal places.) IS Last year a classroom had 21 students, and this year it has 25 students. The regression's prediction for the change in the classroom average test score is (Round your response to two decimal places.) The sample average class size across the 102 classrooms is 20.97. (Hint: Review the formulas for the OLS estimators.) (Round The sample average of the test scores across the 102 classrooms is your response to two decimal places.)
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.
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