Correlation and Regression


“Correlation and Regression” (Note: Please respond to the following two [2] items):

1. Debate the following statement: “Correlation means Causation.” Determine whether this statement is true or false, and provide reasoning for your determination, using the Possible Relationships Between Variables table from your textbook. Elementary Statistics: A Brief Version, 6th edition
Author: Allan Bluman
ISBN: 1259211274

2. Biddle and Hamermesh (1990) built a multiple regression model to study the tradeoff between time spent in sleeping and working and to look at other factors affecting sleep:
Sleep = ?0 + ?1 totwrk + ?2 educ + ?3 age + ?
where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. Suppose the following equation is estimated:
Sleep = 3500 – 0.15 totwrk – 11.20 educ + 2.29 age + ?

-Discuss what would happen to someone’s sleep if they choose to work more.
-Analyze whether the factors of totwrk, educ, and age are enough factors to explain the variation in sleep.
-Explain which additional factors should be explored in order to explain the variation in sleep.
– Provide your reasoning.

Sample paper

Correlation and Regression

The statement “correlation means causation” is not true. Correlation is a term indicating how closely related two variables or things are to one another. Correlation does not necessarily mean causation because when two things are correlated, the case is not always that one thing causes the other. According to Bluman (2006), the observed relationship between the variables may be the result of the influence of a third variable. Correlation may also occur when there interrelationships among different variables. As such, two things might be correlated but this may be due to the influence of multiple variables. Another possible reason for correlation between variables is the element of chance. The researcher may find correlation between two variables, but the finding could be a matter of coincidence.

If someone chose to work more, sleep would decline. From the equation, working more would lead to a higher negative totwrk, leading to a reduction in the total amount of sleep. The factors towrk, educ, and age are not enough factors to explain the variation in sleep. This is because the model explains about 11 percent of the variations. Some additional factors should be explored to explain the variation in sleep. One of the factors is the number of children in the family. Another factor might be stress levels of the individuals.


Bluman, A. G. (2008). Elementary statistics: A brief version : Allan G. Bluman. Dubuque:           McGraw-Hill Companies.

Related: Rejecting and Accepting the Null