Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

The article "That's Rich: More You Drink, More You Earn" (Calgary Herald, April 16,2002 ) reported that there was a positive correlation between alcohol consumption and income. Is it reasonable to conclude that increasing alcohol consumption will increase income? Give at least two reasons or examples to support your answer.

Short Answer

Expert verified
No, it is not reasonable to conclude that increasing alcohol consumption will increase income. Correlation does not imply causation. The correlation could be due to lurking variables. For instance, professionals earning higher might have more social gatherings involving alcohol. This does not mean drinking caused their higher income. Additionally, increasing alcohol consumption in a low wage job will not automatically result in an income rise, illustrating that drinking more doesn't directly cause increased income.

Step by step solution

01

Understand Correlation Does Not Imply Causation

Correlation and causation are different things. A correlation simply means that a relationship exists between two factors, but it does not indicate that one factor is the cause of the other. The fact that alcohol consumption and income concur does not imply that drinking more alcohol actually causes an increase in income. It could be that other factors are at play, which result in higher income earners consuming more alcohol.
02

Discuss Possible lurking/confounding variables

There might be lurking or confounding variables that make it seem like there's a direct relationship between alcohol consumption and income. For example, it could be that people with high income have more leisure time or tend to socialize more, and these occasions involve alcohol consumption. So, the increased alcohol consumption is a result of having a higher income, not the cause of it.
03

Provide Examples

You can think of examples to further illustrate this point. For instance, a person working in low wage job who begins to consume more alcohol does not mean his salary will automatically increase. Similarly, a well-earning professional may decide to stop drinking alcohol, but this won't necessarily cause a decrease in their income. These examples ensure their understanding of why increased alcohol consumption does not directly lead to increased income.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

A sample of 548 ethnically diverse students from Massachusetts were followed over a 19 -month period from 1995 and 1997 in a study of the relationship between TV viewing and eating habits (Pediatrics [2003]: 1321- 1326). For each additional hour of television viewed per day, the number of fruit and vegetable servings per day was found to decrease on average by \(0.14\) serving. a. For this study, what is the dependent variable? What is the predictor variable? b. Would the least-squares line for predicting number of servings of fruits and vegetables using number of hours spent watching TV as a predictor have a positive or negative slope? Explain.

Both \(r^{2}\) and \(s_{e}\) are used to assess the fit of a line. a. Is it possible that both \(r^{2}\) and \(s_{e}\) could be large for a bivariate data set? Explain. (A picture might be helpful.) b. Is it possible that a bivariate data set could yield values of \(r^{2}\) and \(s_{e}\) that are both small? Explain. (Again, a picture might be helpful.) c. Explain why it is desirable to have \(r^{2}\) large and \(s_{e}\) small if the relationship between two variables \(x\) and \(y\) is to be described using a straight line.

The following data on \(x=\) soil depth (in centimeters) and \(y=\) percentage of montmorillonite in the soil were taken from a scatterplot in the paper "Ancient Maya Drained Field Agriculture: Its Possible Application Today in the New River Floodplain, Belize, C.A." (Agricultural Ecosystems and Environment \([1984]: 67-84)\) : $$ \begin{array}{lllllllr} x & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ y & 58 & 34 & 32 & 30 & 28 & 27 & 22 \end{array} $$ a. Draw a scatterplot of \(y\) versus \(x\). b. The equation of the least-squares line is \(\hat{y}=64.50-\) \(0.45 x\). Draw this line on your scatterplot. Do there appear to be any large residuals? c. Compute the residuals, and construct a residual plot. Are there any unusual features in the plot?

The paper "Postmortem Changes in Strength of Gastropod Shells" (Paleobiology [1992]: \(367-377\) ) included scatterplots of data on \(x=\) shell height (in centimeters) and \(y=\) breaking strength (in newtons) for a sample of \(n=38\) hermit crab shells. The least-squares line was \(\hat{y}=-275.1+244.9 x\) a. What are the slope and the intercept of this line? b. When shell height increases by \(1 \mathrm{~cm}\), by how much does breaking strength tend to change? c. What breaking strength would you predict when shell height is \(2 \mathrm{~cm} ?\) d. Does this approximate linear relationship appear to hold for shell heights as small as \(1 \mathrm{~cm} ?\) Explain.

The paper "Effects of Canine Parvovirus (CPV) on Gray Wolves in Minnesota" (Journal of Wildlife Management \([1995]: 565-570\) ) summarized a regression of \(y=\) percentage of pups in a capture on \(x=\) percentage of \(\mathrm{CPV}\) prevalence among adults and pups. The equation of the least-squares line, based on \(n=10\) observations, was \(\hat{y}=62.9476-0.54975 x\), with \(r^{2}=.57\) a. One observation was \((25,70)\). What is the corresponding residual? b. What is the value of the sample correlation coefficient? c. Suppose that \(\mathrm{SSTo}=2520.0\) (this value was not given in the paper). What is the value of \(s_{e} ?\)

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free