The following table shows the number of full-time faculty that WVU has on staff throughout the years. Year # of Faculty 2003 2007 2009 2013 1250 1290 1430 1470 2017 1540 2018 1650 (A) Find the equation of the regression line that represents the number of full-time faculty, y, as a function of x years since 2000. (round the regression coefficients to two decimal place y = (B) What is the correlation coefficient (r-value) for the regression model? (Round your answer to three decimal places) %3D (C) Given the value of r, does the regression line model the data well? That is, does the equation do a good job of modeling the data? O No, the r-value is high, meaning the data can't be modeled using a line O Yes, the r-value is high, meaning the data can be modeled using a line O No, the r-value is low, meaning the data can't be modeled using a line O Yes, the r-value is low, meaning the data can be modeled using a line

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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.