Thursday, January 10, 2013

Linear Regression

Discuss basic ideas of linear regression and correlation.
_ Create and interpret a line of best fit.
_ Calculate and interpret the correlation coefficient.
_ Calculate and interpret outliers.

1.1 Introduction

Professionals often want to know how two or more variables are related. For example, is there a relationship between the grade on the second math exam a student takes and the grade on the final exam? If there is a relationship, what is it and how strong is the relationship?
In another example, your income may be determined by your education, your profession, your years of experience, and your ability. The amount you pay a repair person for labor is often determined by an initial amount plus an hourly fee. These are all examples in which regression can be used. The type of data described in the examples is bivariate data - "bi" for two variables. In reality, statisticians use multivariate data, meaning many variables.

In this chapter, you will be studying the simplest form of regression, "linear regression" with one independent variable (x). This involves data that fits a line in two dimensions. You will also study correlation which measures how strong the relationship is.

1.2 Linear Equations

Linear regression for two variables is based on a linear equation with one independent variable. It has the form:
                        y = a + bx
where a and b are constant numbers.

is the independent variable, and  is the dependent variable. Typically, you choose a value to substitute for the independent variable and then solve for the dependent variable.

Example 1

The following examples are linear equations.
                                     y = 3 + 2x
                                    y = 0.01 + 1.2x
The graph of a linear equation of the form y = a + bx is a straight line. Any line that is not vertical can be described by this equation.

Example 2

Figure 1: Graph of the equation y = -1 + 2x.

Linear equations of this form occur in applications of life sciences, social sciences, psychology, business etc.

Example 2

Svetlana tutors to make extra money for college. For each tutoring session, she charges a one time fee of $25 plus $15 per hour of tutoring. A linear equation that expresses the total amount of money Svetlana earns for each session she tutors is y = 25 + 15x.
What are the independent and dependent variables? What is the y-intercept and what is the slope? Interpret them using complete sentences.


The independent variable (x) is the number of hours Svetlana tutors each session. The dependent variable (y) is the amount, in dollars, Svetlana earns for each session.
The y-intercept is 25 (a = 25). At the start of the tutoring session, Svetlana charges a one-time fee of $25 (this is when x = 0). The slope is 15 (b = 15). For each session, Svetlana earns $15 for each hour she tutors.

1.3 Scatter Plots
Before we take up the discussion of linear regression and correlation, we need to examine a way to display the relation between two variables x and y. The most common and easiest way is a scatter plot. The following example illustrates a scatter plot.
1.4 Slope and Y-Intercept of a Linear Equation

For the linear equation y = a + bx, b = slope and a = y-intercept. From algebra recall that the slope is a number that describes the steepness of a line and the y-intercept is the y coordinate of the point (0, a) where the line crosses the y-axis.

1.5 Facts about the Correlation Coefficient for Linear Regression
_ A positive r means that when x increases, y increases and when x decreases, y decreases (positive correlation).
_ A negative r means that when x increases, y decreases and when x decreases, y increases (negative correlation).
_ An r of zero means there is absolutely no linear relationship between x and y (no correlation).
_ High correlation does not suggest that x causes y or y causes x. We say "correlation does not imply causation." For example, every person who learned math in the 17th century is dead. However, learning math does not necessarily cause death!

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