Statistics professor wants to use the number of hours (x) a student studies for a statistics final exam to predict the final exam score (y). A regression model was fit based on data collected from a class during the previous semester, with the following results: (y hat): y = 35.0 + 3X
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.
Statistics professor wants to use the number of hours (x) a student studies for a statistics final exam to predict the final exam score (y). A regression model was fit based on data collected from a class during the previous semester, with the following results:
(y hat): y = 35.0 + 3X
1. What is the y-intercept?
2. What is the slope?
3. If the student spends 2 hours studying, what could be his/her final exam score?
5. If the student does not study for the final exam, what is the predicted final exam score?
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