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# Chancerossman 2015 iscam iii chapter 2 135 chapter 2

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Chance/Rossman, 2015 ISCAM III Chapter 2 135 CHAPTER 2: ANALYZING QUANTITATIVE DATA This chapter parallels the previous one in many ways. The difference here is that these investigations will involve a quantitative variable rather than a categorical one. This requires us to learn different tools for graphing and summarizing our data, as well as for statistical inference. In the end, you will find that the basic concepts and principles that you learned in Chapters 1 still apply. Section 1: Descriptive Statistics Investigation 2.1: Birth weights Normal model, Assessing model fit Investigation 2.2: How long can you stand it? Skewed data Investigation 2.3: Cancer pamphlets - Application Section 2: Inference for Mean Investigation 2.4: The Ethan Allen Sampling distributions for x Investigation 2.5: Healthy body temperatures One-sample t -procedures Probability Detour: Student’s t Distribution Investigation 2.6: Healthy body temperatures (cont.) Prediction intervals Section 3: Inference for Other Statistics (optional) Investigation 2.7: Water oxygen levels Sign test Investigation 2.8: Turbidity t -procedures with transformed data Investigation 2.9: Heroin treatment times - Bootstrapping Example 2.1: Pushing On One-sample t -procedures Example 2.2: Distracted Driving? Sign test Investigating Statistical Concepts, Applications, and Methods

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Chance/Rossman, 2015 ISCAM III Investigation 2.1 136 SECTION 1: DESCRIPTIVE STATISTICS In this section, we will begin our exploration of numerical and graphical summaries when the variable is quantitative. You will learn a new set of tools, but keep in mind that the overall goal is to informatively summarize the data and to let the data tell their story . Investigation 2.1: Birth Weights The CDC’s Vital Statistics Data allows you to download birth records for all births in the U.S. in a particular year. In fact, we downloaded the records for all 3,940,764 births in 2013 and then extracted several variables including the birth weight of the child (in grams). Can we use these data to build a model of how birth weights can be expected to behave in the future? Can we use that model to make predictions about certain kinds of birth weights? The file USbirthsJan2013.txt contains information on all the births in January 2013, including birth weight, whether the baby was full term (gestation over 36 weeks), the 5 minute apgar score (an immediate measure of the infant’s health), and the amount of weight gained by the mother during pregnancy (in lbs). (a) Are these data likely to be representative of birth weights for all 3,940,764 U.S. births in 2013? Explain. (b) Is the variable birthweight quantitative or categorical? Our next step is to look at the data! As in Chapter 1, we will want to consider which graphical and numerical summaries reveal the most information about the distribution.
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