(b) A grocery store conducted a survey. It showed that when customers buy less cereal, it does not indicate a change in the amount of milk they purchase. What can we conclude? O There is no correlation between amount of cereal bought and amount of milk purchased. O There is a correlation between amount of cereal bought and amount of milk purchased. However, there is no causation. This is because there is likely a decrease in the amount of milk purchased with a decrease in the amount of cereal bought. O There is a correlation between amount of cereal bought and amount of milk purchased. There may or may not be causation. Further studies would have to be done to determine this. (c) John compared the player statistics from team's basketball season. He determined that being taller does not imply that a player scores more or fewer points. What is likely true? O There is no correlation between height and number of points scored. O There is a correlation between height and number of points scored. There may or may not be causation. Further studies would have to be done to determine this. O There is a correlation between height and number of points scored. However, there is no causation. This is because there is probably an increase in the number points scored with an increase in height.

You have been asked to find if there is a correlation between the amount of calories per serving in a bowl of cereal versus the amount of sugar (in grams) in the cereal.  The following data has been given  Which variable is the predictor and which variable is the response Sugar Calories 18 200 23 210 12 170 12 190 37 220 19 175 23 201 22 210 32 210 11 190 11 197 29 205

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Does "correlation imply causation"? Give one example in which a variable X ispositively correlated with another variable Y, but we cannot deduce that X causes Y.

You wish to determine if there is a linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.05 and round all values to 4 decimal places. Driver Age Number of Driver Deaths per 100,000 65 26 33 25 38 34 42 30 17 34 80 20 30 35 71 29 65 28 50 30 Но: р %3D 0 На: р 2 0 Find the Linear Correlation Coefficient r = Find the p-value p-value The p-value is O Less than (or equal to) a O Greater than a The p-value leads to a decision to O Reject Ho О Аcсеpt Ho O Do Not Reject Ho The conclusion is There is a significant linear correlation between driver age and number of driver deaths. O There is insufficient evidence to make a conclusion about the linear correlation between driver age and number of driver deaths. O There is a significant negative linear correlation between driver age and number of driver deaths. O There is a significant positive…

Dr. Morgendorffer wants to develop a questionnaire that measures employees' burnout. But during her literature review, she learns that burnout is a variable with three separate dimensions: exhaustion, cynicism, and detachment. Which of the following checks will help ensure that Dr. Morgendorffer's burnout questionnaire is valid? O a. Ensuring that the items across all three dimensions are correlated with each other O b. Ensuring that there is a high level of stability for each of the three dimensions OC. Ensuring that the dimensions represent parallel forms of the questionnaire O d. Ensuring that all three dimensions have been adequately represented in the questionnaire

According to multiple research, there is a -0.697 correlation between happiness and loneliness. This means that _________.   a. People who are happier tend to be less lonely.   b. People who are happier tend to be more lonely.   c. There is no link between happiness and loneliness.   d. There is a causal relationship between happiness and loneliness.

. Differentiate between correlation vs. causation. When two variables are correlated (such as strength and running speed), they are associated with one another. Explain how, even if there is a correlation between the two, one might not cause the other. Provide an example of an association between variables where a causal relationship makes sense conceptually. Why can't correlations be used as a tool to prove a causal relationship between variables rather than just an association?

A national consumer magazine reported the following correlations. The correlation between car weight and car reliability is - 0.30. . The correlation between car weight and annual maintenance cost is 0.20. Which of the following statements are true? I. Heavier cars tend to be less reliable. II. Heavier cars tend to cost more to maintain. III. Car weight is related more strongly to reliability than to maintenance cost. (A) I only (B) II only (C) III only (D) I and II only (E) I, II, and III