1.
Using Data Set 1, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
A. Yes
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.
2.
Using Data Set 2, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
A. Yes
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.
3.
Using Data Set 3, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
A. Yes
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.
4.
Using Data Set 4, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
A. Yes
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.
5.
Using Data Set 5, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
A. Yes
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.
6.
Using Data Set 6, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
B. No
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.
7.
Using Data Set 7, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
A. Yes
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.
8.
Using Data Set 8, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
A. Yes
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.
9.
Using Data Set 10, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
A. Yes
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.
10.
Using Data Set 11, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
B. No
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.
11.
Using Data Set 12, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
B. No
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.
12.
Using Data Set 21, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
B. No
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.
13.
Using Data Set 22, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
B. No
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.
14.
Using Data Set 22, and checking the residual plot, is the assumption of linearity correct in this instance?
Correct Answer
B. No
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.
15.
Please fill answer below_______
Correct Answer
N/A
16.
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?
Correct Answer
5
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.
17.
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?
Correct Answer
-4
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.
18.
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?
Correct Answer
12
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.
19.
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?
Correct Answer
-6
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.
20.
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?
Correct Answer
-1
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.
21.
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?
Correct Answer
-6
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.
22.
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?
Correct Answer
-7
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.
23.
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?
Correct Answer
-7
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.
24.
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?
Correct Answer
-12
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.
25.
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?
Correct Answer
3
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.
26.
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?
Correct Answer
-1
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.
27.
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?
Correct Answer
-1
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.
28.
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?
Correct Answer
-4
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.
29.
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?
Correct Answer
1
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.
30.
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?
Correct Answer
5
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.
31.
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?
Correct Answer
4
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.
32.
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?
Correct Answer
9
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.
33.
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?
Correct Answer
12
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.
34.
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?
Correct Answer
-8
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.
35.
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?
Correct Answer
-12
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.
36.
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?
Correct Answer
-11
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.
37.
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?
Correct Answer
-5
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.
38.
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?
Correct Answer
-8
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.
39.
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?
Correct Answer
-2
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.
40.