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### Question

Researcher is investigating a possible effect of facebook use on exam grades and he wants to try a linear regression model. he has outside data, collected via a survey, showing that the correlation between the student’s exam grade and the number of hours spent on facebook in the days before the exam is about -0.12. if the researcher wanted to use linear regression to predict the exam score for a student who spent 6 hours on facebook before his exam, which is of the following best describes the researcher’s

### Answer #1 for Questions: Researcher is investigating a possible effect of facebook use on exam grades and he wants to try a linear regression model. he has outside data, collected via a survey, showing that the correlation between the student’s exam grade and the number of hours spent on facebook in the days before the exam is about -0.12. if the researcher wanted to use linear regression to predict the exam score for a student who spent 6 hours on facebook before his exam, which is of the following best describes the researcher’s

**Answer:**

**Linear regression**

- attempts to model the relationship between two variables by fitting a linear equation to observed data.
- One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.

Before attempting to fit a linear model to observed data, a modeler **should first determine whether or not there is a relationship between the variables of interest.** This does not necessarily imply that one variable causes the other, but that there is some significant association between the two variables.

A valuable numerical measure of association between two variables is the correlation coefficient, which is a value between -1 and 1 indicating the strength of the association of the observed data for the two variables.

A **linear regression** line has an equation of the form** Y = a + bX**, where **X **is the explanatory variable and **Y** is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

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