Chapter 10. Regression. Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania
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1 Chapter 10 Regression Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania
2 Scatter Diagrams A graph in which pairs of points, (x, y), are plotted with x on the horizontal axis and y on the vertical axis. The explanatory variable is x. The response variable is y. One goal of plotting paired data is to determine if there is a linear relationship between x and y. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 10 2
3 Finding the Best Fitting Line One goal of data analysis is to find a mathematical equation that best represents the data. For our purposes, best means the line that comes closest to each point on the scatter diagram. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 10 3
4 Not All Relationships are Linear Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 10 4
5 Correlation Strength Correlation is a measure of the linear relationship between two quantitative variables. If the points lie exactly on a straight line, we say there is perfect correlation. As the strength of the relationship decreases, the correlation moves from perfect to strong to moderate to weak. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 10 5
6 Correlation If the response variable, y, tends to increase as the explanatory variable, x, increases, the variables have positive correlation. If the response variable, y, tends to decrease as the explanatory variable, x, increases, the variables have negative correlation. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 10 6
7 Correlation Examples Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 10 7
8 Correlation Coefficient The mathematical measurement that describes the correlation is called the sample correlation coefficient. Denoted by r. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 10 8
9 Correlation Coefficient Features 1) r has no units. 2) -1 r 1 3) Positive values of r indicate a positive relationship between x and y (likewise for negative values). 4) r = 0 indicates no linear relationship. 5) Switching the explanatory variable and response variable does not change r. 6) Changing the units of the variables does not change r. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 10 9
10 r = 0 Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
11 r = 1 or r = -1 Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
12 0 < r < 1 Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
13 -1 < r < 0 Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
14 Developing a Formula for r Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
15 Developing a Formula for r Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
16 A Computational Formula for r Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
17 Critical Thinking: Population vs. Sample Correlation Beware: high correlations do not imply a cause and effect relationship between the explanatory and response variables!! Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
18 Critical Thinking: Lurking Variables A lurking variable is neither the explanatory variable nor the response variable. Beware: lurking variables may be responsible for the evident changes in x and y. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
19 Correlations Between Averages If the two variables of interest are averages rather than raw data values, the correlation between the averages will tend to be higher! Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
20 Linear Regression When we are in the paired-data setting: Do the data points have a linear relationship? Can we find an equation for the best fitting line? Can we predict the value of the response variable for a new value of the predictor variable? What fractional part of the variability in y is associated with the variability in x? Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
21 Least-Squares Criterion The line that we fit to the data will be such that the sum of the squared distances from the line will be minimized. Let d = the vertical distance from any data point to the regression line. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
22 Least-Squares Criterion Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
23 Least-Squares Line yˆ a bx a is the y-intercept. b is the slope. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
24 Finding the Regression Equation Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
25 Finding the Regression Equation Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
26 Using the Regression Equation ( x, y) The point is always on the least-squares line. Also, the slope tells us that if we increase the explanatory variable by one unit, the response variable will change by the slope (increase or decrease, depending on the sign of the slope). Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
27 Critical Thinking: Making Predictions We can simply plug in x values into the regression equation to calculate y values. Extrapolation can result in unreliable predictions. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
28 Coefficient of Determination Another way to gauge the fit of the regression equation is to calculate the coefficient of determination, r 2. We can view the mean of y as a baseline for all the y values. Then: Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
29 Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
30 Coefficient of Determination We algebraically manipulate the above equation to obtain: Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
31 Coefficient of Determination Thus, r 2 is the measure of the total variability in y that is explained by the regression on x. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
32 Coefficient of Determination Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
33 Inferences on the Regression Equation We can make inferences about the population correlation coefficient, ρ, and the population regression line slope, β. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
34 Inferences on the Regression Equation Inference requirements: The set of ordered pairs (x, y) constitutes a random sample from all ordered pairs in the population. For each fixed value of x, y has a normal distribution. All distributions for all y values have equal variance and a mean that lies on the regression equation. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
35 Testing the Correlation Coefficient Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
36 Testing the Correlation Coefficient We convert to a Student s t distribution. The sample size must be 3. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
37 Testing Procedure Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
38 Standard Error of Estimate As the points get closer to the regression line, S e decreases. If all the points lie exactly on the line, S e will be zero. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
39 Computational Formula For S e Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
40 Confidence Intervals for y The population regression line: y = α + βx + ε Where ε is random error Because of ε, for each x value there is a corresponding distribution for y. Each y distribution has the same standard deviation, estimated by S e. Each y distribution is centered on the regression line. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
41 Confidence Intervals for Predicted y Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
42 Confidence Intervals for Predicted y Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
43 Confidence Intervals for Predicted y The confidence intervals will be narrower near the mean of x. The confidence intervals will be wider near the extreme values of x. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
44 Confidence Intervals for Predicted y Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
45 Inferences About Slope β Recall the population regression equation: y = α + βx + ε Let S e be the standard error of estimate from the sample. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
46 Inferences About Slope β Has a Student s t distribution with n 2 degrees of freedom. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
47 Testing β Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
48 Finding a Confidence Interval for β Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
49 Multiple Regression In practice, most predictions will improve if we consider additional data. Statistically, this means adding predictor variables to our regression techniques. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
50 Multiple Regression Terminology y is the response variable. x 1 x k are explanatory variables. b 0 b k are coefficients. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
51 Regression Models A mathematical package that includes the following: 1) A collection of variables, one of which is the response, and the rest are predictors. 2) A collection of numerical values associated with the given variables. 3) Using software and the above numerical values, construct a least-squares regression equation. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
52 Regression Models A mathematical package that includes the following: 4) The model includes information about the variables, coefficients of the equation, and various goodness of fit measures. 5) The model allows the user to make predictions and build confidence intervals for various components of the equation. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
53 Coefficient of Multiple Determination How well does our equation fit the data? Just as in linear regression, computer output will provide r 2. r 2 is a measure of the amount of variability in y that is explained by the regression on all of the x variables. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
54 Predictions in the Multiple Regression Case Similar to the techniques described for linear regression, we can plug in values for all of the x variables and compute a predicted y value. Predicting y for any x values outside the x- range is extrapolation, and the results will be unreliable. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
55 Testing a Coefficient for Significance At times, one or more of the predictor variables may not be helpful in determining y. Using software, we can test any or all of the x variables for statistical significance. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
56 Testing a Coefficient for Significance The hypotheses will be: H 0 : β i = 0 H 1 : β i 0 for the i th coefficient in the model. If we fail to reject the null hypothesis for a given coefficient, we may consider removing that predictor from the model. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
57 Confidence Intervals for Coefficients As in the linear case, software can provide confidence intervals for any β i in the model. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved
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