Without the actual data table, it's impossible to definitively answer these questions. However, I can explain how you would go about answering them.

1. To determine the association between the two variables "Reaction" and "Gender", you would need to analyze the data. If there is a noticeable difference in reactions between different genders, then you could say that the reaction to the new fitness gadget is associated with the gender of the customer. If there is no noticeable difference, then the reaction is not associated with the gender of the customer.

2. A scatter plot may or may not be the most appropriate graphical representation for the given data. It depends on the nature of the data. If both "Reaction" and "Gender" are numerical variables, a scatter plot could be appropriate. However, if "Gender" is a categorical variable (which it usually is), a bar chart or box plot might be more appropriate.

3. The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. A correlation coefficient close to 1 indicates a strong positive linear relationship. However, without the actual data, it's impossible to calculate the correlation coefficient for "Reaction" and "Gender". Furthermore, the correlation coefficient is typically used for two numerical variables, not for a numerical and a categorical variable.

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Without the actual data table, it's impossible to definitively answer these questions. However, I can explain how you would go about answering them.

1. To determine the association between the two variables "Reaction" and "Gender", you would need to analyze the data. If there is a noticeable difference in reactions between different genders, then you could say that the reaction to the new fitness gadget is associated with the gender of the customer. If there is no noticeable difference, then the reaction is not associated with the gender of the customer.

2. A scatter plot may or may not be the most appropriate graphical representation for the given data. It depends on the nature of the data. If both "Reaction" and "Gender" are numerical variables, a scatter plot could be appropriate. However, if "Gender" is a categorical variable (which it usually is), a bar chart or box plot might be more appropriate.

3. The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. A correlation coefficient close to 1 indicates a strong positive linear relationship. However, without the actual data, it's impossible to calculate the correlation coefficient for "Reaction" and "Gender". Furthermore, the correlation coefficient is typically used for two numerical variables, not for a numerical and a categorical variable.

Knowee AI · Accepted Answer

Without the actual data table, it's impossible to definitively answer these questions. However, I can explain how you would go about answering them.

1. To determine the association between the two variables "Reaction" and "Gender", you would need to analyze the data. If there is a noticeable difference in reactions between different genders, then you could say that the reaction to the new fitness gadget is associated with the gender of the customer. If there is no noticeable difference, then the reaction is not associated with the gender of the customer.

2. A scatter plot may or may not be the most appropriate graphical representation for the given data. It depends on the nature of the data. If both "Reaction" and "Gender" are numerical variables, a scatter plot could be appropriate. However, if "Gender" is a categorical variable (which it usually is), a bar chart or box plot might be more appropriate.

3. The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. A correlation coefficient close to 1 indicates a strong positive linear relationship. However, without the actual data, it's impossible to calculate the correlation coefficient for "Reaction" and "Gender". Furthermore, the correlation coefficient is typically used for two numerical variables, not for a numerical and a categorical variable.

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