Pearson eText for Statistical Reasoning for Everyday Life -- Instant Access (Pearson+)
5th Edition
ISBN: 9780137561544
Author: Jeffrey Bennett, William Briggs
Publisher: PEARSON+
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Chapter 7.4, Problem 1E
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Data is collected on the number of ice cream cones sold in a shop on a certain day and the number of people wearing sunglasses visiting the shop that same day. What type of relationship is most likely to exist between these two variables?
positive correlation and causal relationship
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positive correlation but not causal relationship
negative correlation but not causal relationship
Briefly explain what it means by "High correlation does not imply causality."
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one causes a change of the other".
. Differentiate between correlation vs. causation. When two variables are correlated (such as strength
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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?
Chapter 7 Solutions
Pearson eText for Statistical Reasoning for Everyday Life -- Instant Access (Pearson+)
Ch. 7.1 - Correlation. What is a correlation? Give three...Ch. 7.1 - Scatterplot. What is a scatterplot, and how is one...Ch. 7.1 - Types of Correlation. Define and distinguish...Ch. 7.1 - Correlation Coefficient. What does the correlation...Ch. 7.1 - Does It Make Sense? For Exercises 58, determine...Ch. 7.1 - Does It Make Sense? For Exercises 58, determine...Ch. 7.1 - Does It Make Sense? For Exercises 58, determine...Ch. 7.1 - Does It Make Sense? For Exercises 58, determine...Ch. 7.1 - Correlation. Exercises 916 list pairs of...Ch. 7.1 - Correlation. Exercises 916 list pairs of...
Ch. 7.1 - Correlation. Exercises 916 list pairs of...Ch. 7.1 - Correlation. Exercises 916 list pairs of...Ch. 7.1 - Correlation. Exercises 916 list pairs of...Ch. 7.1 - Correlation. Exercises 916 list pairs of...Ch. 7.1 - Correlation. Exercises 916 list pairs of...Ch. 7.1 - Correlation. Exercises 916 list pairs of...Ch. 7.1 - Crickets and Temperature. One classic example of a...Ch. 7.1 - Two-Day Forecast. Figure 7.8 shows a scatterplot...Ch. 7.1 - Properties of the Correlation Coefficient. For...Ch. 7.1 - Properties of the Correlation Coefficient. For...Ch. 7.1 - Properties of the Correlation Coefficient. For...Ch. 7.1 - Properties of the Correlation Coefficient. For...Ch. 7.1 - Scatterplot and Correlation. In Exercises 2330,...Ch. 7.1 - Scatterplot and Correlation. In Exercises 2330,...Ch. 7.1 - Scatterplot and Correlation. In Exercises 2330,...Ch. 7.1 - Prob. 26ECh. 7.1 - Scatterplot and Correlation. In Exercises 2330,...Ch. 7.1 - Scatterplot and Correlation. In Exercises 2330,...Ch. 7.1 - Scatterplot and Correlation. In Exercises 2330,...Ch. 7.1 - Scatterplot and Correlation. In Exercises 2330,...Ch. 7.1 - Your Own Positive Correlations. Give examples of...Ch. 7.1 - Your Own Negative Correlations. Give examples of...Ch. 7.2 - Outliers. Briefly explain how an outlier can make...Ch. 7.2 - Grouped Data. Briefly explain how data that...Ch. 7.2 - Explanations for Correlation. What are the three...Ch. 7.2 - Prob. 4ECh. 7.2 - Does It Make Sense? For Exercises 58, determine...Ch. 7.2 - Does It Make Sense? For Exercises 58, determine...Ch. 7.2 - Does It Make Sense? For Exercises 58, determine...Ch. 7.2 - Does It Make Sense? For Exercises 58, determine...Ch. 7.2 - Correlation and Causality. Exercises 916 present...Ch. 7.2 - Correlation and Causality. Exercises 916 present...Ch. 7.2 - Correlation and Causality. Exercises 916 present...Ch. 7.2 - Correlation and Causality. Exercises 916 present...Ch. 7.2 - Correlation and Causality. Exercises 916 present...Ch. 7.2 - Correlation and Causality. Exercises 916 present...Ch. 7.2 - Correlation and Causality. Exercises 916 present...Ch. 7.2 - Correlation and Causality. Exercises 916 present...Ch. 7.2 - Outlier Effects. Consider the scatterplot in...Ch. 7.2 - Outlier Effects. Consider the scatterplot in...Ch. 7.2 - Footprint and Height. The following table lists...Ch. 7.2 - January and July High Temperatures. The following...Ch. 7.2 - Birth and Death Rates. Figure 7.17 shows the birth...Ch. 7.2 - Penny Weight and Date. The scatterplot in Figure...Ch. 7.3 - Best-Fit Line. What is a best-fit line? How is a...Ch. 7.3 - Prob. 2ECh. 7.3 - Interpreting r2. What does the square of the...Ch. 7.3 - Prob. 4ECh. 7.3 - Prob. 5ECh. 7.3 - Does It Make Sense? For Exercises 58, determine...Ch. 7.3 - Does It Make Sense? For Exercises 58, determine...Ch. 7.3 - Does It Make Sense? For Exercises 58, determine...Ch. 7.3 - Best-Fit Lines. Exercises 916 refer to tables in...Ch. 7.3 - Best-Fit Lines. Exercises 916 refer to tables in...Ch. 7.3 - Prob. 11ECh. 7.3 - Best-Fit Lines. Exercises 916 refer to tables in...Ch. 7.3 - Best-Fit Lines. Exercises 916 refer to tables in...Ch. 7.3 - Best-Fit Lines. Exercises 916 refer to tables in...Ch. 7.3 - Prob. 15ECh. 7.3 - Prob. 16ECh. 7.4 - Correlation and Causality. What is the difference...Ch. 7.4 - Prob. 2ECh. 7.4 - Establishing Causality. Briefly state in your own...Ch. 7.4 - Confidence in Causality. Describe three levels of...Ch. 7.4 - Prob. 5ECh. 7.4 - Does It Make Sense? For Exercises 58, determine...Ch. 7.4 - Does It Make Sense? For Exercises 58, determine...Ch. 7.4 - Does It Make Sense? For Exercises 58, determine...Ch. 7.4 - Physical Models. For Exercises 912, determine...Ch. 7.4 - Physical Models. For Exercises 912, determine...Ch. 7.4 - Physical Models. For Exercises 912, determine...Ch. 7.4 - Physical Models. For Exercises 912, determine...Ch. 7.4 - Altitude and Health. When some people climb to...Ch. 7.4 - Smoking and Lung Cancer. There is a strong...Ch. 7.4 - Other Lung Cancer Causes. Several things besides...Ch. 7.4 - Longevity of Orchestra Conductors. A famous study...Ch. 7.4 - Older Moms. A study reported in Nature claims that...Ch. 7.4 - High-Voltage Power Lines. Suppose that people...Ch. 7.4 - Gun Control. Those who favor gun control often...Ch. 7.4 - Vasectomies and Prostate Cancer. The article Does...Ch. 7 - Pizza and the Subway. For Exercises 16, refer to...Ch. 7 - Pizza and the Subway. For Exercises 16, refer to...Ch. 7 - Pizza and the Subway. For Exercises 16, refer to...Ch. 7 - Pizza and the Subway. For Exercises 16, refer to...Ch. 7 - Pizza and the Subway. For Exercises 16, refer to...Ch. 7 - Pizza and the Subway. For Exercises 16, refer to...Ch. 7 - For 10 pairs of sample data values, the...Ch. 7 - In a study involving randomly selected subjects,...Ch. 7 - A researcher collects paired sample data values...Ch. 7 - Estimate the value of the linear correlation...Ch. 7 - Fill in the blanks: Every possible correlation...Ch. 7 - Which of the following are likely to have a...Ch. 7 - For a collection of 50 pairs of sample data...Ch. 7 - Estimate the correlation coefficient for the data...Ch. 7 - Refer again to the scatterplot in Figure 7.24....Ch. 7 - Fill in the blank: If r = 0.900, then _____ % of...Ch. 7 - In Exercises 710, determine whether the given...Ch. 7 - Prob. 8CQCh. 7 - Prob. 9CQCh. 7 - Prob. 10CQ
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- Find an interesting instance where two variables are strongly correlated, but share no causal link. Explain the situation, describe the variables, and explain why it doesn't make sense for the data to be correlated.arrow_forwardWhy is it important to determine whether two variables are related before causality is determined?arrow_forwardCausation A study has shown that there is a correlation between body weight and blood pressure. Higher body weights are associated with higher blood pressure levels. Can we conclude that gaining weight is a cause of increased blood pressure?arrow_forward
- Airline fares are increasing as the oil prices are increasing. Is this a positive or negative correlation between the two? Which is the Independent variable? Which is the dependent variable?arrow_forwardIn what ways would you measure causality in research? Select all that apply. Measuring the changes observed Controlling confounding influences Observing the effects on the dependent variable(s) You can't really measure cause and effect.arrow_forwardHow forecasting is quite different from the estimation of causal effects.?arrow_forward
- Does a high value of r2 allow us to conclude that two variables are causally related? Explain. - A high value of r2 can only allow us to conclude that two variables are causally related in linear relationships, but not in nonlinear relationships. - Yes. Regression or correlation analysis always allows us to conclude that two variables are causally related. - A high value of r2 can only allow us to conclude that two variables are causally related in nonlinear relationships, but not in linear relationships. - No. Regression or correlation analysis can never allow us to conclude that two variables are causally related. - Yes. Since r2 is the percentage of the total sum of squares that can be explained by using the estimated regression equation, a high value of r2 allows us to conclude that two variables are causally related.arrow_forwardLinear Regression Discuss any two variables that you believe are correlated. Clearly state your two variables and talk about how they are related or how one might lead to affect the other. Note that correlation does not imply causation. Two variables might be associated but this does not mean one causes the other.arrow_forward
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