2464 Scatter Plots Calculating Residuals

  • AP Statistics
  • IB Mathematics
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| By Anthony Nunan
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Anthony Nunan
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| Attempts: 243 | Questions: 78 | Updated: Mar 21, 2025
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1. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 173cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
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About This Quiz
2464 Scatter Plots Calculating Residuals - Quiz

The '2464 Scatter Plots Calculating Residuals' quiz assesses understanding of linearity assumptions in residual plots across six different datasets. It evaluates the ability to analyze and interpret data, crucial for learners in statistics and data analysis.

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2. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 175cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
3. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 181cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
4. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 186cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
5. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 187cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
6. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 188cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
7. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 78 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
8. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 76 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
9. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 74 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
10. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 72 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
11. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 69 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
12. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 65 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
13. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 64 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
14. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 62 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
15. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 59 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
16. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 58 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
17. The data set above (Data Set 4) shows the grades for students completing two separate assessments in English The x data is for a English presentation, and the y data is for a written exam. For the student attaining 56 in the English presentation, what is the residual to the nearest whole mark?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
18. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 32sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
19. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 25sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
20. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 45sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
21. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 38sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
22. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 58sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
23. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 41sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
24. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 33sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
25. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 60sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
26. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 43?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
27. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 45?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
28. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 51?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
29. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 56?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
30. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 58?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
31. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 59?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
32. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 64?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
33. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 65?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
34. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 72?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
35. A linear regression line has a general equation of y = a + bx. Using Data Set 2, I found that the value for a is 20.634 and the value for b is 0.619. What is the residual value for 'y' to the nearest whole number when the independent variable (x) is 69?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
36. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  78?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
37. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  76?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
38. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  74?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
39. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  69?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
40. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  65?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
41. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  64?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
42. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  62?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
43. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  59?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
44. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  56?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
45. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  51?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
46. y = 97.7 - 0.617 xThe linear regression equation above has been rounded to three significant figures from Data Set 3. What is the residual value to the nearest whole number when the independent variable (x) is  45?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
47. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 32?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
48. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 27?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
49. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x =25?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
50. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 27?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
51. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 45?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
52. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 35?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
53. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 58?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
54. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 41?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
55. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 33?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
56. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 43?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
57. Using Data Set 1, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

Based on the given information, the assumption of linearity is correct in this instance. This can be inferred from the fact that the residual plot, which is a visual representation of the differences between the observed and predicted values, does not exhibit any clear patterns or trends. In a linear regression analysis, a random and evenly distributed residual plot indicates that the assumption of linearity holds true.

Submit
58. Using Data Set 4, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

Based on the given information, the assumption of linearity is correct in this instance. This can be inferred from the residual plot, which is a graphical representation of the differences between the observed and predicted values in a regression model. If the plot shows a random pattern with no clear trends or patterns, it indicates that the assumption of linearity is valid. Since the answer is "Yes," it suggests that the residual plot in Data Set 4 satisfies this condition, supporting the assumption of linearity.

Submit
59. Using Data Set 5, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

Based on the given information, the assumption of linearity is correct in this instance. This can be inferred from the fact that the answer is "Yes" without any further explanation or context provided.

Submit
60. Using Data Set 6, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The assumption of linearity is not correct in this instance based on the residual plot. A residual plot is a scatterplot of the residuals against the predicted values. If the points in the plot are randomly scattered around the horizontal axis, it suggests that the assumption of linearity is met. However, if there is a clear pattern or curvature in the plot, it indicates a violation of the linearity assumption. Therefore, based on the given information, the assumption of linearity is not correct.

Submit
61. Using Data Set 7, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The given answer "Yes" suggests that the assumption of linearity is correct in this instance. This means that the relationship between the independent and dependent variables can be adequately represented by a straight line. The residual plot, which shows the difference between the observed and predicted values, likely indicates that the residuals are randomly scattered around zero, indicating that the linear model is appropriate for the data. However, without further information or context, it is difficult to provide a more specific explanation.

Submit
62. Using Data Set 8, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The explanation for the given correct answer is that by using Data Set 8 and checking the residual plot, it can be determined whether the assumption of linearity is correct in this instance. The residual plot helps to assess if the residuals are randomly scattered around the horizontal axis, indicating that the relationship between the variables is linear. If the plot shows a random pattern with no discernible trends or patterns, it suggests that the assumption of linearity is valid. Therefore, the correct answer is "Yes" in this case.

Submit
63. Using Data Set 10, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The correct answer is "Yes" because the assumption of linearity is correct in this instance. This can be determined by checking the residual plot, which is a graphical representation of the difference between the observed and predicted values. If the plot shows a random distribution of points around the horizontal line, it indicates that the relationship between the variables is linear. Therefore, since the residual plot supports linearity, the assumption is correct.

Submit
64. Using Data Set 11, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The answer is "No" because if the assumption of linearity is not correct, the residual plot will show a pattern or trend, indicating that the relationship between the independent and dependent variables is not linear. Therefore, based on the residual plot analysis, it can be concluded that the assumption of linearity is not correct in this instance.

Submit
65. Using Data Set 12, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The answer is "No" because the assumption of linearity is not correct in this instance. This conclusion is based on the analysis of the residual plot, which provides information about the relationship between the independent and dependent variables. If the plot shows a random pattern with no clear trend, it suggests that the assumption of linearity is met. However, if there is a clear pattern or curvature in the plot, it indicates a violation of the linearity assumption. Therefore, based on the information provided, the assumption of linearity is not correct in this instance.

Submit
66. Using Data Set 21, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The assumption of linearity is not correct in this instance. This can be determined by checking the residual plot, which is a graphical representation of the differences between the observed and predicted values. If the plot shows a pattern or systematic deviation from randomness, it indicates that the relationship between the variables is not linear. In this case, since the answer is "No," it suggests that there is evidence of non-linearity in the data set.

Submit
67. Using Data Set 22, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

Based on the given information, the assumption of linearity is not correct in this instance. This can be inferred from the residual plot, which is a graphical representation of the difference between the observed and predicted values. If the points in the residual plot are randomly scattered around the horizontal line, it suggests that the assumption of linearity holds. However, if there is a clear pattern or trend in the residual plot, it indicates a violation of the linearity assumption. Since the correct answer is "No," it implies that the residual plot shows a pattern or trend, indicating a violation of linearity.

Submit
68. Using Data Set 22, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The answer is "No" because if the assumption of linearity is incorrect, the residual plot will show a pattern or trend, indicating that the relationship between the dependent and independent variables is not linear. Therefore, based on the information provided, the assumption of linearity is not correct in this instance.

Submit
69. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 161cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
70. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 165cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
71. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 168cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
72. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 172cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
73. Using Data Set 2, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

The explanation for the correct answer "Yes" is that when using Data Set 2 and checking the residual plot, it indicates that the assumption of linearity is correct in this instance. The residual plot shows that the residuals are randomly scattered around the horizontal line at zero, suggesting that there is no pattern or systematic deviation from linearity. This indicates that the linear regression model is appropriate for the data and the assumption of linearity holds true.

Submit
74. Using Data Set 3, and checking the residual plot, is the assumption of linearity correct in this instance? 

Explanation

Based on the given information, the assumption of linearity is correct in this instance. This can be inferred by examining the residual plot, which is a graphical representation of the difference between the observed and predicted values. If the plot shows a random pattern with no clear trend or systematic deviation, it indicates that the assumption of linearity is valid. Since the answer is "Yes," it suggests that the residual plot in Data Set 3 demonstrates a random pattern, supporting the assumption of linearity.

Submit
75. The data set above (Data Set 8) shows the height (x) and weight (y) of a team of soccer players. Using the linear regression line, what is the residual to the nearest kg for the player who is 178cm tall?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
76. The data set above (Data Set 6) shows time taken to run 100 metres before and after a four week fitness training programme to the nearest second (sec). There were 14 people tested. For the person running 35sec before the training started, what is the residual value?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
77. Please fill answer below_______

Explanation

not-available-via-ai

Submit
78. A linear regression equation takes the general form y = a + bx. Using Data Set 5, I found that a = 8 and b = 0.56. To the nearest whole number, what is the residual value for y when x = 38?

Explanation

Find the predicted value first. The residual value is the raw value minus the predicted value. Use the table to find the raw value, and the subtract the predicted value. Use the App equ2predict wherever possible.

Submit
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The data set above (Data Set 8) shows the height (x) and weight (y) of...
The data set above (Data Set 8) shows the height (x) and weight (y) of...
The data set above (Data Set 8) shows the height (x) and weight (y) of...
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Y = 97.7 - 0.617 xThe linear regression equation above has been...
Y = 97.7 - 0.617 xThe linear regression equation above has been...
Y = 97.7 - 0.617 xThe linear regression equation above has been...
Y = 97.7 - 0.617 xThe linear regression equation above has been...
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Please fill answer below_______
A linear regression equation takes the general form y = a + bx. Using...
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