1 / 53

Section 3.4

Section 3.4. Day 2. When is it appropriate to use a least squares regression line (LSRL) or correlation?. When is it appropriate to use a least squares regression line (LSRL) or correlation? Only when you have a linear pattern. Linear Pattern or Not?. Linear Pattern or Not?. No…

ettag
Download Presentation

Section 3.4

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 3.4 Day 2

  2. When is it appropriate to use a least squares regression line (LSRL) or correlation?

  3. When is it appropriate to use a least squares regression line (LSRL) or correlation? Only when you have a linear pattern.

  4. Linear Pattern or Not?

  5. Linear Pattern or Not? No… …if “outlier” is excluded, then a circle would be used = NO SHAPE. YES … …if “outlier” is included, an ellipse could be formed then LINE could be used.

  6. Linear Pattern or Not?

  7. Linear Pattern or Not? Yes (no pattern to residuals)

  8. Linear Pattern or Not?

  9. Linear Pattern or Not? No (Curved data)

  10. Linear Pattern or Not?

  11. Linear Pattern or Not? Yes (no pattern to residuals)

  12. Linear Pattern or Not?

  13. Linear Pattern or Not? No (residuals have a curve pattern)

  14. Linear Pattern or Not?

  15. Linear Pattern or Not? YES

  16. Page 159, E34

  17. Page 159, E34 a) Find the equation of the least squares line for predicting graduation rate from student/faculty ratio. What is explanatory variable? What is response variable?

  18. Page 159, E34 a) Find the equation of the least squares line for predicting graduation rate from student/faculty ratio. What is explanatory variable? What is response variable?

  19. Page 159, E34 a) graduation rate = y-intercept + slope (student/faculty ratio)

  20. Page 159, E34 a) graduation rate = y-intercept + slope (student/faculty ratio) Slope:

  21. Page 159, E34 a) graduation rate = y-intercept + slope (student/faculty ratio) Slope: b1 = (-0.5) = -0.0096511628

  22. Page 159, E34 To find the y-intercept, use the fact that the point of averages (11.7, 0.827) is on the regression line.

  23. Page 159, E34 To find the y-intercept, use the fact that the point of averages (11.7, 0.827) is on the regression line. y = a + bx 0.827 = a + (- 0.00965)(11.7) intercept = ?

  24. Page 159, E34 To find the y-intercept, use the fact that the point of averages (11.7, 0.827) is on the regression line. y = a + bx 0.827 = a + (- 0.00965)(11.7) intercept = 0.93991

  25. Page 159, E34 graduation rate = 0.94 – 0.00965(student/faculty ratio)

  26. Page 159, E34 b) Find the equation of the least squares line for predicting student/faculty ratio from graduation rate.

  27. Page 159, E34(b) student/faculty ratio = y-intercept + slope(graduation rate)

  28. Page 159, E34(b) student/faculty ratio = y-intercept + slope(graduation rate) Slope:

  29. Page 159, E34(b) student/faculty ratio = y-intercept + slope(graduation rate) Slope:

  30. Page 159, E34(b) To find the y-intercept, use the fact that the point of averages (0.827, 11.7) is on the regression line. y = a + bx 11.7 = a + (-25.90)(0.827) intercept = ?

  31. Page 159, E34(b) To find the y-intercept, use the fact that the point of averages (0.827, 11.7) is on the regression line. y = a + bx 11.7 = a + (-25.9)(0.827) intercept = 33.12

  32. Page 159, E34(b) student/faculty ratio = 33.12 – 25.9(graduation rate)

  33. Page 159, E34(b) a) graduation rate = 0.94 – 0.00965(student/faculty ratio) b) student/faculty ratio = 33.12 – 25.9(graduation rate)

  34. Page 160, E37(b)

  35. Page 160, E37(b) b. If we kept the price of cheeseburgers down, college would be more affordable. Lurking variable?

  36. Page 160, E37(b) b. If we kept the price of cheeseburgers down, college would be more affordable. The lurking variable is inflation over the years—all costs have gone up over the years.

  37. Page 177, E47 a) Use your calculator to create a residual plot for this data.

  38. Page 177, E47

  39. Page 177, E47b Which pizza has largest positive residual? Which pizza has largest negative residual?

  40. Page 177, E47b Which pizza has largest positive residual? Which pizza has largest negative residual?

  41. Page 177, E47b Which pizza has largest positive residual? Pizza Hut’s Stuffed Crust Which pizza has largest negative residual?

  42. Page 177, E47b Which pizza has largest negative residual?

  43. Page 177, E47b Which pizza has largest negative residual?

  44. Page 177, E47b Which pizza has largest negative residual? Pizza Hut’s Pan

  45. Page 177, E47b Are any of these residuals so extreme as to suggest that those pizzas should be regarded as extreme?

  46. Page 177, E47b Are any of these residuals so extreme as to suggest that those pizzas should be regarded as extreme? Make a modified boxplot of the residuals to check for outliers.

  47. Page 177, E47b Are any of these residuals so extreme as to suggest that those pizzas should be regarded as extreme? No residuals are outliers so no pizza should be regarded as extreme.

  48. Page 177, E49

  49. Page 177, E49 A. I B. IV C. III D. II

  50. Page 177, E49 Linear model would be appropriate for C and D.

More Related