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不 Use the given data to complete parts (a) and (b). × ye 2.2 4 3.9 1.3 2.8 3.6 4.7 4.9 (a) Draw a scatter diagram of the data. Choose the correct answer. ○ A. 4 2- Ay Ho 2 ○ B. O C. 6- Q 4- 2- Compute the linear correlation coefficient. The linear correlation coefficient for the four pieces of data is ☐ (Round to three decimal places as needed.) (b) Draw a scatter diagram of the data with the additional data point (10.4.9.2). Choose the correct answer. OA. Ay 12- 8- 4- O B. ○ C. Q 12- 12- 8- a 8- 4 0- 6 4- Ay Q a 2+ G G 0- ර ර හ Q OD. Ay 12 Q 8- 4- G Compute the linear correlation coefficient with the additional data point. The linear correlation coefficient for the five pieces of data is (Round to three decimal places as needed.) Comment on the effect the additional data point has on the linear correlation coefficient OA. The additional data point weakens the appearance of a linear association between the data points. OB. The additional data point strengthens the appearance of a linear association between the data points. OC. The additional data point does not affect the linear correlation coefficient. Explain why correlations should always be reported with scatter diagrams. OA. The scatter diagram is needed to determine if the correlation is positive or negative. OB. The scatter diagram is needed to see if the correlation coefficient is being affected by the presence of outliers OC. The scatter diagram can be used to distinguish between association and causation.

不 Use the given data to complete parts (a) and (b). × ye 2.2 4 3.9 1.3 2.8 3.6 4.7 4.9 (a) Draw a scatter diagram of the data. Choose the correct answer. ○ A. 4 2- Ay Ho 2 ○ B. O C. 6- Q 4- 2- Compute the linear correlation coefficient. The linear correlation coefficient for the four pieces of data is ☐ (Round to three decimal places as needed.) (b) Draw a scatter diagram of the data with the additional data point (10.4.9.2). Choose the correct answer. OA. Ay 12- 8- 4- O B. ○ C. Q 12- 12- 8- a 8- 4 0- 6 4- Ay Q a 2+ G G 0- ර ර හ Q OD. Ay 12 Q 8- 4- G Compute the linear correlation coefficient with the additional data point. The linear correlation coefficient for the five pieces of data is (Round to three decimal places as needed.) Comment on the effect the additional data point has on the linear correlation coefficient OA. The additional data point weakens the appearance of a linear association between the data points. OB. The additional data point strengthens the appearance of a linear association between the data points. OC. The additional data point does not affect the linear correlation coefficient. Explain why correlations should always be reported with scatter diagrams. OA. The scatter diagram is needed to determine if the correlation is positive or negative. OB. The scatter diagram is needed to see if the correlation coefficient is being affected by the presence of outliers OC. The scatter diagram can be used to distinguish between association and causation.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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不 Use the given data to complete parts (a) and (b). × ye 2.2 4 3.9 1.3 2.8 3.6 4.7 4.9 (a) Draw a scatter diagram of the data. Choose the correct answer. ○ A. 4 2- Ay Ho 2 ○ B. O C. 6- Q 4- 2- Compute the linear correlation coefficient. The linear correlation coefficient for the four pieces of data is ☐ (Round to three decimal places as needed.) (b) Draw a scatter diagram of the data with the additional data point (10.4.9.2). Choose the correct answer. OA. Ay 12- 8- 4- O B. ○ C. Q 12- 12- 8- a 8- 4 0- 6 4- Ay Q a 2+ G G 0- ර ර හ Q OD. Ay 12 Q 8- 4- G Compute the linear correlation coefficient with the additional data point. The linear correlation coefficient for the five pieces of data is (Round to three decimal places as needed.) Comment on the effect the additional data point has on the linear correlation coefficient OA. The additional data point weakens the appearance of a linear association between the data points. OB. The additional data point strengthens the appearance of a linear association between the data points. OC. The additional data point does not affect the linear correlation coefficient. Explain why correlations should always be reported with scatter diagrams. OA. The scatter diagram is needed to determine if the correlation is positive or negative. OB. The scatter diagram is needed to see if the correlation coefficient is being affected by the presence of outliers OC. The scatter diagram can be used to distinguish between association and causation.
Transcribed Image Text:不 Use the given data to complete parts (a) and (b). × ye 2.2 4 3.9 1.3 2.8 3.6 4.7 4.9 (a) Draw a scatter diagram of the data. Choose the correct answer. ○ A. 4 2- Ay Ho 2 ○ B. O C. 6- Q 4- 2- Compute the linear correlation coefficient. The linear correlation coefficient for the four pieces of data is ☐ (Round to three decimal places as needed.) (b) Draw a scatter diagram of the data with the additional data point (10.4.9.2). Choose the correct answer. OA. Ay 12- 8- 4- O B. ○ C. Q 12- 12- 8- a 8- 4 0- 6 4- Ay Q a 2+ G G 0- ර ර හ Q OD. Ay 12 Q 8- 4- G Compute the linear correlation coefficient with the additional data point. The linear correlation coefficient for the five pieces of data is (Round to three decimal places as needed.) Comment on the effect the additional data point has on the linear correlation coefficient OA. The additional data point weakens the appearance of a linear association between the data points. OB. The additional data point strengthens the appearance of a linear association between the data points. OC. The additional data point does not affect the linear correlation coefficient. Explain why correlations should always be reported with scatter diagrams. OA. The scatter diagram is needed to determine if the correlation is positive or negative. OB. The scatter diagram is needed to see if the correlation coefficient is being affected by the presence of outliers OC. The scatter diagram can be used to distinguish between association and causation.
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