# 2600 Scatter Plot Transformations

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| By Anthony Nunan
A
Anthony Nunan
Community Contributor
Quizzes Created: 136 | Total Attempts: 44,092
Questions: 52 | Attempts: 463

Settings

• 1.

### When a scatter plot is non linear, the residual plot will be

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

D. Patterned
Explanation
When a scatter plot is non-linear, it means that the relationship between the variables being plotted is not a straight line. In such cases, the residual plot will typically exhibit a pattern, rather than being random. This pattern can indicate a systematic deviation from the expected values, suggesting that there may be another underlying factor influencing the relationship between the variables. Therefore, the correct answer is "patterned."

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• 2.

### Which quadrant is displayed in the image above

• A.

• B.

• C.

• D.

Explanation
Based on the given options and the image, the correct answer is Quadrant 4. In a coordinate plane, Quadrant 4 is located in the bottom right corner, where both the x-coordinate and y-coordinate are positive. Since the point in the image is in this region, it falls into Quadrant 4.

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• 3.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

A. Linear
Explanation
The residual plot is a graphical representation of the difference between the observed values and the predicted values in a regression analysis. In a linear regression, the residuals should be randomly scattered around the horizontal axis with no clear pattern. If the residual plot shows a linear pattern, it suggests that the original scatter plot data is linear.

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• 4.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

A. Linear
Explanation
The given residual plot shows a clear pattern where the residuals are evenly distributed around the horizontal line at zero. This indicates that the relationship between the variables in the original scatter plot is linear. In a linear relationship, the dependent variable can be explained by a straight line.

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• 5.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

A. Linear
Explanation
The given residual plot shows a clear pattern where the residuals are evenly distributed around the horizontal line of zero. This indicates that the relationship between the independent and dependent variables is linear. In a linear relationship, the change in the dependent variable is directly proportional to the change in the independent variable. Therefore, the original scatter plot data can be considered linear.

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• 6.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

A. Linear
Explanation
The residual plot helps us analyze the relationship between the independent and dependent variables in a regression model. In this case, since the residual plot shows a linear pattern, it suggests that the original scatter plot data is linear. This means that there is a linear relationship between the independent and dependent variables, and the data points tend to fall along a straight line.

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• 7.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

A. Linear
Explanation
The given residual plot indicates that the original scatter plot data follows a linear pattern. This can be inferred from the fact that the residuals, which represent the vertical distance between the observed data points and the corresponding predicted values, are randomly scattered around the horizontal line at zero. In a linear relationship, the residuals should be evenly distributed around zero, suggesting that the data points are closely aligned along a straight line.

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• 8.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

A. Linear
Explanation
The residual plot is a graphical representation of the difference between the observed values and the predicted values in a regression analysis. In a linear regression, the residuals should be randomly scattered around the horizontal line at zero. If the residual plot shows a consistent pattern or trend, it suggests that the relationship between the variables is not linear. However, if the residuals are randomly scattered around zero, it indicates that the original scatter plot data is linear.

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• 9.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

A. Linear
Explanation
The residual plot shows the difference between the observed values and the predicted values in a linear regression model. If the residual plot is linear, it indicates that the relationship between the predictor variable and the response variable is also linear. Therefore, the original scatter plot data is linear.

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• 10.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

B. Non-linear
Explanation
The residual plot is a graphical representation of the difference between the observed values and the predicted values in a regression analysis. In a linear relationship, the residuals should be randomly scattered around the horizontal line at zero. However, in a non-linear relationship, the residuals will not be randomly scattered and will show a distinct pattern. Therefore, based on the given residual plot, we can conclude that the original scatter plot data is non-linear.

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• 11.

### From this residual plot, we can tell the original scatter plot data is :

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned

B. Non-linear
Explanation
The residual plot helps us analyze the pattern of the residuals (the differences between the observed and predicted values). If the residual plot shows a clear and systematic pattern, it suggests that the relationship between the variables is non-linear. Therefore, based on the given information, we can conclude that the original scatter plot data is non-linear.

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• 12.

• A.

Linear

• B.

Non-linear

• C.

Random

• D.

Patterned