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buec 232chatper 2 study guide

buec 232chatper 2 study guide - 80 CHAPTER 2 Examining...

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EXAMPLE 2.1 CHAPTER 2 Examining Relationships The effects of alcohol 80 independent variable dependent variable . Introduction Alcohol has many effects on the body. One effect is a drop in body temperature. To study this effect, researchers give several different amounts of alcohol to mice, then measure the change in each mouse’s body temperature in the 15 minutes after taking the alcohol. Amount of alcohol is the explanatory variable, and change in body temperature is the response variable. RESPONSE VARIABLE, EXPLANATORY VARIABLE response variable explanatory variable independent variables, dependent variables. A medical study finds that short women are more likely to have heart attacks than women of average height, while tall women have the fewest heart attacks. An insurance group reports that heavier cars have fewer deaths per 10,000 ve- hicles registered than do lighter cars. These and many other statistical studies look at the relationship between two variables. To understand such a relation- ship, we must often examine other variables as well. To conclude that shorter women have higher risk from heart attacks, for example, the researchers had to eliminate the effect of other variables such as weight and exercise habits. Our topic in this chapter is relationships between variables. One of our main themes is that the relationship between two variables can be strongly influenced by other variables that are lurking in the background. Because variation is everywhere, statistical relationships are overall tenden- cies, not ironclad rules. They allow individual exceptions. Although smokers on the average die younger than nonsmokers, some people live to 90 while smoking three packs a day. To study a relationship between two variables, we measure both variables on the same individuals. Often, we think that one of the variables explains or influences the other. A measures an outcome of a study. An explains or influences changes in a response variable. You will often find explanatory variables called and response variables called The idea behind this language is that the response variable depends on the explanatory variable. Because the words “independent” and “dependent” have other meanings in statistics that are unrelated to the explanatory-response distinction, we prefer to avoid those words. It is easiest to identify explanatory and response variables when we actually set values of one variable in order to see how it affects another variable. When we don’t set the values of either variable but just observe both vari- ables, there may or may not be explanatory and response variables. Whether there are depends on how we plan to use the data.
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v v v EXAMPLE 2.2 Introduction SAT scores 81 ............................... ................. Jim wants to know how the average SAT math and verbal scores in the 51 states (including the District of Columbia) are related to each other. He doesn’t think that either score explains or causes the other. Jim has two related variables, and neither is an explanatory variable.
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