1 / 44

Section 12.1

Section 12.1. Scatter Plots and Correlation. With the quality added value you’ve come to expect from D.R.S., University of Cordele. HAWKES LEARNING SYSTEMS math courseware specialists. Regression, Inference, and Model Building 12.1 Scatter Plots and Correlation.

jariah
Download Presentation

Section 12.1

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Section 12.1 Scatter Plots and Correlation With the quality added value you’ve come to expect from D.R.S., University of Cordele

  2. HAWKES LEARNING SYSTEMS math courseware specialists Regression, Inference, and Model Building 12.1 Scatter Plots and Correlation Plot (x,y) data points and think about whether x and y are somehow related Types of Relationships: Strong Linear Relationship Weak Linear Relationship No Relationship Non-Linear Relationship

  3. Source: Yahoo! Sports. “NFL - Statistics by Position.” http://sports.yahoo.com/nfl/stats/byposition?pos=QB&conference=NFL&year=season_20 11&sort=49&timeframe=All (20 May 2012). Source: Spotrac.com. “NFL Player Contracts, Salaries, and Transactions.” http://www.spotrac.com/nfl/ (2 Oct. 2012).

  4. Example 12.1: Creating a Scatter Plot to Identify Trends in Data Use the data from Table 12.2 to produce a scatter plot that shows the relationship between the base salary of an NFL quarterback and the number of touchdowns the quarterback has thrown in one season. Solution We might expect for the number of touchdowns a quarterback throws in one season to influence his salary. Taking this into consideration, we will place the number of touchdowns on the x-axis and the base salary on the y-axis.

  5. Scatter Plot of (touchdowns, salary) on TI-84 Put Touchdowns in list L1, Salary in list L2 Y= old algebra plots should be cleared out of there 2nd STAT PLOT all should be “Off” to start with 1:Plot 1: On, choose Type, Lists L1 and L2, Mark • Remember 2ND 1, 2ND 2 to put in list names? ZOOM 9:ZoomStat If unexplainable error, 2ND MEM 7 1 2 to clear all and then retype the lists of data. TRACE and Left Arrow and Right Arrow to explore it

  6. Example 12.1: Creating a Scatter Plot to Identify Trends in Data (cont.) Is there any apparent relationship between the number of passing touchdowns and the salary? __________________

  7. Example 12.2: Creating a Scatter Plot to Identify Trends in Data Use the data in Table 12.2 to produce a scatter plot that shows the relationship between the number of touchdowns thrown in one season and the corresponding quarterback rating for the given sample of NFL quarterbacks. Solution In this case, we would expect that the number of touchdowns thrown by a quarterback does influence that quarterback’s rating, since number of touchdowns is one of many factors used to determine the quarterback rating.

  8. Scatter Plot of (touchdowns, rating) on TI-84 Ratings in list L3, Touchdowns still in L1, Salary in L2 Type the Ratings into List L3 if you haven’t already done so. 2ndSTAT PLOT 1:Plot 1: Change to Lists L1 and L3 ZOOM 9:ZoomStat TRACE and Left Arrow and Right Arrow to explore it

  9. Example 12.2: Creating a Scatter Plot to Identify Trends in Data (cont.) Is there any apparent relationship between the number of passing touchdowns and the QB Rating? _____________. It appears to be a _______ relationship with _______ slope.

  10. Example 12.3: Determining Whether a Scatter Plot Would Have a Positive Slope, Negative Slope, or Not Follow a Straight-Line Pattern Determine whether the points in a scatter plot for the two variables are likely to have a positive slope, negative slope, or not follow a straight-line pattern. a. The number of hours you study for an exam and the score you make on that exam _________________ b. The price of a used car and the number of miles on the odometer _____________________________ c. The pressure on a gas pedal and the speed of the car _____________________________________ d. Shoe size and IQ for adults ___________________

  11. Scatter Plots and Correlation The Pearson correlation coefficient, , is the parameter that measures the strength of a linear relationship between two quantitative variables in a population. The correlation coefficient for a sample is denoted by r. It always takes a value between −1 and 1, inclusive. ρ is the Greek letter “rho”. Practice writing the rho character here:

  12. Question: “Are x and y related?” ρ (Greek letter rho) is the population parameter for the Correlation Coefficient r (our alphabet’s letter r) is the sample statistic for the Correlation Coefficient We use our sample r to estimate the population’s parameter ρ

  13. HAWKES LEARNING SYSTEMS math courseware specialists Regression, Inference, and Model Building 12.1 Scatter Plots and Correlation • –1 ≤ r ≤ 1 • Close to –1 means a strong negative correlation. • Close to 0 means no correlation. • Close to 1 means a strong positive correlation.

  14. Scatter Plots and Correlation Pearson Correlation Coefficient The Pearson correlation coefficientfor paired data from a sample is given by where n is the number of data pairs in the sample, xi is the ith value of the explanatory variable, and yi is the ith value of the response variable.

  15. Example 12.4: Calculating the Correlation Coefficient Using a TI-83/84 Plus Calculator Calculate the correlation coefficient, r, for the data from Table 12.2 relating touchdowns thrown and base salaries. Solution The data we need from Table 12.2 are reproduced in the following table. (Should already be in your calculator’s lists.) But we will not dig into the details of that awful formula! The TI-84 has built-in goodies.

  16. Example 12.4: Calculating the Correlation Coefficient Using a TI-83/84 Plus Calculator (cont.) • Do you expect r to be • Close to -1 ? • Close to 0? • Close to 1? in List L2 in List L1

  17. Example 12.4: Calculating the Correlation Coefficient Using a TI-83/84 Plus Calculator (cont.) It’s STAT, TESTS, ALPHA F (ALPHA E on the 83/Plus) Repeat for the lists for Passing Touchdowns and QB Rating. In that case, r = _______. Put in the list names VARS, Y-VARS, 1, 1 will be useful later. Highlight Calculate, press ENTER, down arrows to find r = _____________

  18. Use TI-84 LinRegTTest for a full Hypothesis Test(more than just getting the correlation coefficient, r) The next few slides describe the use of LinRegTTest. It’s STAT, TESTS, ALPHA F (ALPHA E on the 83/Plus) This description is about the full hypothesis test to determine “Is the relationship significant?” The outputs include the value of r, the correlation coefficient, which is of greatest interest at this early point in our study. The Hawkes materials talk about the LinReg feature but I’m recommending the LinRegTTest instead because you get more information for about the same effort.

  19. Hypothesis Test for significant Null Hypothesis: “No relationship” Alternative: “But there IS a significant relationship!” There’s some level of significance specified in advance, like or It involves calculating a value and finding “what is the -value of this ?” And if -value < , reject the null hypothesis • If so, then we say “Yes, significant relationship!”

  20. Hypothesis Test for significant Usually we do this two-tailed test: • Null Hypothesis : “No relationship” • Alternative Hypothesis: , “There is a significant linear relationship.” Be aware of a couple one-tailed variations: • Test for significant POSITIVE correlation only:using and • Test for significant NEGATIVE correlation only:using and

  21. LinRegTTest inputs (not identical to the quarterback example!) • Here are the inputs: • Xlist and Ylist – where you put the data • Shortcut: 2ND 2 puts L2 • Freq: 1 (unless…) • β & ρ: ≠ 0 • This is the Alternative Hypothesis • RegEq: VARS, right arrow to Y-VARS, 1, 1 • Just put it in for later • Highlight “Calculate” • Press ENTER

  22. LinRegTTest Outputs, first screen(from a different problem) • t= the t statistic value for this test (the formula is in the book) • p = the p-value for this t test statistic • in this kind of a test • later – for regression

  23. LinRegTTest Outputs, second screen (from a different problem) • b later, for Regression • s much later, for advanced Regression • r2 = how much of the output variable (weight) is explained by the input variable (girth) • r = the correlation coefficient for the sample • Close to – strong positive relationship • Or – strong negative

  24. Testing the Correlation Coefficient for Significance Using Hypothesis Testing This is the one we use the most. Testing Linear Relationships for Significance Significant Linear Relationship (Two-Tailed Test) H0:  = 0 (Implies that there is no significant linear relationship) Ha:  ≠ 0 (Implies that there is a significant linear relationship) Testing Linear Relationships for Significance (cont.) Significant Negative Linear Relationship (Left-Tailed Test) H0:  ≥ 0 (Implies that there is no significant negative linear relationship) Ha:  < 0 (Implies that there is a significant negative linear relationship) Be aware that this one exists. Testing Linear Relationships for Significance (cont.) Significant Positive Linear Relationship (Right-Tailed Test) H0:  ≤ 0 (Implies that there is no significant positive linear relationship) Ha:  > 0 (Implies that there is a significant positive linear relationship) Be aware that this one exists. (Now they’re getting into the Hypothesis Testing we saw a brief preview of earlier in this set of slides.)

  25. Testing the Correlation Coefficient for Significance Using Hypothesis Testing Test Statistic for a Hypothesis Test for a Correlation Coefficient The test statistic for testing the significance of the correlation coefficient is given by TI-84 LinRegTTest will calculate this value for us. Test Statistic for a Hypothesis Test for a Correlation Coefficient (cont.) where r is the sample correlation coefficient and n is the number of data pairs in the sample. The number of degrees of freedom for the t-distribution of the test statistic is given by n- 2.

  26. Testing the Correlation Coefficient for Significance Using Hypothesis Testing Rejection Regions for Testing Linear Relationships Significant Linear Relationship (Two-Tailed Test) Reject the null hypothesis, H0 , if Significant Negative Linear Relationship (Left-Tailed Test) Reject the null hypothesis, H0 , if Significant Positive Linear Relationship (Right-Tailed Test) Reject the null hypothesis, H0 , if But we will use the p-value method because LinRegTTest gives us a p-value and the experiment specifies the α

  27. Example 12.7: Performing a Hypothesis Test to Determine if the Linear Relationship between Two Variables Is Significant Use a hypothesis test to determine if the linear relationship between the number of parking tickets a student receives during a semester and his or her GPA during the same semester is statistically significant at the 0.05 level of significance. Refer to the data presented in the following table.

  28. Example 12.7 Use the TI-84 LinRegTTest to perform the hypothesis test. Use the p-value method: The LinRegTTest gives you a p-value. If the p-value is < the given Level of Significance α = 0.05, then REJECT the null hypothesis; conclude that there IS a significant linear relationship. Otherwise, Fail To Reject – no significant relationship. And you can disregard most or all of the by-hand detail that is in the book and in the online Help.

  29. Example 12.7: Performing a Hypothesis Test to Determine if the Linear Relationship between Two Variables Is Significant (cont.) Solution Step 1: State the null and alternative hypotheses. We wish to test the claim that a significant linear relationship exists between the number of parking tickets a student receives during a semester and his or her GPA during the same semester. Thus, the hypotheses are stated as follows.

  30. Example 12.7: Performing a Hypothesis Test to Determine if the Linear Relationship between Two Variables Is Significant (cont.) Step 2: Determine which distribution to use for the test statistic, and state the level of significance. We will use the t-test statistic presented previously in this section along with a significance level of = 0.05 to perform this hypothesis test. Step 3: Gather data and calculate the necessary sample statistics. (Do LinRegTTest)

  31. Example 12-7 Hypothesis Test, concluded Compare p = _____ vs. α = ______ Decision: { Reject / Fail to Reject } the Null Hypothesis. Conclusion about Signficant Linear Relationship: Conclusion in Plain English:

  32. Example 12.8: Performing a Hypothesis Test to Determine if the Linear Relationship between Two Variables Is Significant An online retailer wants to research the effectiveness of its mail-out catalogs. The company collects data from its eight largest markets with respect to the number of catalogs (in thousands) that were mailed out one fiscal year versus sales (in thousands of dollars) for that year. The results are as follows.

  33. Example 12.8: Performing a Hypothesis Test to Determine if the Linear Relationship between Two Variables Is Significant (cont.) Use a hypothesis test to determine if the linear relationship between the number of catalogs mailed out and sales is statistically significant at the 0.01 level of significance. Step 1: Hypotheses: H0: ___________ meaning _____________________. Ha: ___________ meaning _____________________. Step 2: Decision to use the t distribution and level of significance _____ = 0.01

  34. Example 12.8: Performing a Hypothesis Test to Determine if the Linear Relationship between Two Variables Is Significant (cont.) Step 3: Gather data and calculate the necessary sample statistics. Using a TI-83/84 Plus calculator, enter the values for the numbers of catalogs mailed (x) in L1 and the sales values (y) in L2. Run LinRegTTest. Step 4: Conclusion: { Reject / Fail to Reject } the Null Hypothesis. Interpretation:

  35. Coefficient of Determination Thecoefficient of determination, r2 , is a measure of the proportion of the variation in the response variable (y) that can be associated with the variation in the explanatory variable (x). This too is reported to you in the LinRegTTest outputs.

  36. Example 12.9: Calculating and Interpreting the Coefficient of Determination If the correlation coefficient for the relationship between the numbers of rooms in houses and their prices is r = 0.65, how much of the variation in house prices can be associated with the variation in the numbers of rooms in the houses? Solution Recall that the coefficient of determination tells us the amount of variation in the response variable (house price) that is associated with the variation in the explanatory variable (number of rooms).

  37. Example 12.9: Calculating and Interpreting the Coefficient of Determination (cont.) Thus, the coefficient of determination for the relationship between the numbers of rooms in houses and their prices will tell us the proportion or percentage of the variation in house prices that can be associated with the variation in the numbers of rooms in the houses. Also, recall that the coefficient of determination is equal to the square of the correlation coefficient.

  38. Example 12.9: Calculating and Interpreting the Coefficient of Determination (cont.) Since we know that the correlation coefficient for these data is r = 0.65, we can calculate the coefficient of determination as r2 = _____ Thus, approximately _____% of the variation in house prices can be associated with the variation in the numbers of rooms in the houses.

  39. Correlation Coefficient in Excel

  40. More with Excel That’s about all that can be done with basic Excel. There is an advanced feature on Data tab, then the Data Analysis add-in. It gets intothe Regression topic in the next lesson.

  41. Testing the Correlation Coefficient for Significance Using Critical Values of the Pearson Correlation Coefficient to Determine the Significance of a Linear Relationship A sample correlation coefficient, r, is statistically significant if (Why is this discussion here? Sometimes they give you a shred of a problem that gives some summary results and you have to use a printed table to make the determination. That’s the only time you’ll need to do this, for a few of those kinds of problems. In “real life”, in large problems, the LinRegTTest p-value is compared to alpha.)

  42. Example 12.6: Using a Table of Critical Values to Determine Significance of a Linear Relationship Use the critical values in Table I to determine if the correlation between the number of passing touchdowns and base salary from Example 12.4 is statistically significant. Use a 0.05 level of significance. Solution Begin by finding the critical value for  = 0.05 with n = 10 in Table I. Find the value in the table where the row for n = 10 intersects the column for  = 0.05.

  43. Example 12.6: Using a Table of Critical Values to Determine Significance of a Linear Relationship (cont.) INTERPRETATION: “If my sample’s correlation coefficient, r, is at least as big as the value you look up in this table, then YES, significant linear relationship. Otherwise, no, no significant linear relationship.”

  44. Example 12.6: Using a Table of Critical Values to Determine Significance of a Linear Relationship (cont.) Thus, r= 0.632. Comparing this critical value to the absolute value of the correlation coefficient we found for the data in Example 12.4, we have 0.251 < 0.632, and thus  r  < r. Therefore, the linear relationship between the variables is not statistically significant at the 0.05 level of significance. Thus, we do not have sufficient evidence, at the 0.05 level of significance, to conclude that a linear relationship exists between the number of passing touchdowns during the 2011–2012 season and the 2012 base salary of an NFL quarterback.

More Related