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# Chapter 2: Modeling Distributions of Data

Chapter 2: Modeling Distributions of Data. Section 2.1 Describing Location in a Distribution. The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE. Chapter 2 Modeling Distributions of Data. 2.1 Describing Location in a Distribution 2.2 Normal Distributions.

## Chapter 2: Modeling Distributions of Data

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1. Chapter 2: Modeling Distributions of Data Section 2.1 Describing Location in a Distribution The Practice of Statistics, 4th edition - For AP* STARNES, YATES, MOORE

2. Chapter 2Modeling Distributions of Data • 2.1Describing Location in a Distribution • 2.2Normal Distributions

3. Section 2.1Describing Location in a Distribution Learning Objectives After this section, you should be able to… • MEASURE position using percentiles • INTERPRET cumulative relative frequency graphs • MEASURE position using z-scores • TRANSFORM data • DEFINE and DESCRIBE density curves

4. Describing Location in a Distribution • Measuring Position: Percentiles • One way to describe the location of a value in a distribution is to tell what percent of observations are less than it. Definition: The pth percentile of a distribution is the value with p percent of the observations less than it. Example, p. 85 Jenny earned a score of 86 on her test. How did she perform relative to the rest of the class? 6 7 7 2334 7 5777899 8 00123334 8 569 9 03 6 7 7 2334 7 5777899 8 00123334 8 569 9 03 Her score was greater than 21 of the 25 observations. Since 21 of the 25, or 84%, of the scores are below hers, Jenny is at the 84th percentile in the class’s test score distribution.

5. Concept Check • Find the percentile for Norman who scored 72 • Find the percentile for Katie who scored 93 • Two students scored 80, what is their percentile?

6. Calculate and Interpret Percentiles for the following: The stemplot below shows the number of wins for each of the 30 Major League Baseball teams in 2009 • Colorado Rockies with 92 wins • NY Yankies with 103 wins • Cleveland Indians with 65 wins

7. Describing Location in a Distribution • Cumulative Relative Frequency Graphs A cumulative relative frequency graph (or ogive) displays the cumulative relative frequency of each class of a frequency distribution.

8. Interpreting Cumulative Relative Frequency Graphs Describing Location in a Distribution Use the graph from page 88 to answer the following questions. • Was Barack Obama, who was inaugurated at age 47, unusually young? • Estimate and interpret the 65th percentile of the distribution 65 11 58 47

9. Check Your Understanding • Multiple Choice: Mark receives a score report detailing his performance on a statewide test. On the math section, Mark earned a raw score of 39, which placed him at the 68th percentile. This means that • Mark did better than about 39% of the students who took the test • Mark did worse than about 39% of the students who took the test • Mark did better than about 68% of the students who took the test • Mark did worse than about 65% of the students who took the test • Mark got fewer than half of the questions correct on this test

10. Check Your Understanding 2. Mrs. Munson is concerned about how her daughter’s height and weight compare with those of other girls of the same age. She uses an online calculator to determine that her daughter is at the 87th percentile for weight and the 67th percentile for height. Explain to Mrs. Munson what this means.

11. Percentiles and Quartiles • Median corresponds to the 50th percentile • 1st Quartile is roughly 25th percentile • 3rd Quartile is roughly 75th percentile

12. Check Your Understanding The graph displays the relative frequency of the lengths of phone calls made from the mathematics department office at HHS last month. • About what percent of the calls lasted less than 30 minutes? 30 minutes or more? • Estimate Quartile 1 and 3 and the IQR of the distribution

13. Describing Location in a Distribution • Measuring Position: z-Scores • A z-score tells us how many standard deviations from the mean an observation falls, and in what direction. Definition: If x is an observation from a distribution that has known mean and standard deviation, the standardized value of x is: A standardized value is often called a z-score. Jenny earned a score of 86 on her test. The class mean is 80 and the standard deviation is 6.07. What is her standardized score?

14. You Calculate • Find a z-score for Katie who scored 93 • 2. Find a z-score for Norman who scored 72

15. Describing Location in a Distribution • Using z-scores for Comparison We can use z-scores to compare the position of individuals in different distributions. Example, p. 91 Jenny earned a score of 86 on her statistics test. The class mean was 80 and the standard deviation was 6.07. She earned a score of 82 on her chemistry test. The chemistry scores had a fairly symmetric distribution with a mean 76 and standard deviation of 4. On which test did Jenny perform better relative to the rest of her class?

16. Home Run Kings • The single-season home run record for major league baseball has been set just three times since Babe Ruth hit 60 home runs in 1927. Roger Maris hit 61 in 1961, Mark McGwire hit 70 in 1998 and Barry Bonds hit 73 in 2001. In an absolute sense, Barry Bonds had the best performance of these four players, since he hit the most home runs in a single season. However, in a relative sense this may not be true. Baseball historians suggest that hitting a home run has been easier in some eras than others. This is due to many factors, including quality of batters, quality of pitchers, hardness of the baseball, dimensions of ballparks, and possible use of performance-enhancing drugs. To make a fair comparison, we should see how these performances rate relative to others hitters during the same year. Calculate the standardized score for each player and compare.

17. Assignment • #1-4 page 142-143 • #1-2 p 319

18. Describing Location in a Distribution • Transforming Data Transforming converts the original observations from the original units of measurements to another scale. Transformations can affect the shape, center, and spread of a distribution. Effect of Adding (or Subracting) a Constant • Adding the same number a (either positive, zero, or negative) to each observation: • adds a to measures of center and location (mean, median, quartiles, percentiles), but • Does not change the shape of the distribution or measures of spread (range, IQR, standard deviation). Example, p. 93

19. Describing Location in a Distribution • Transforming Data Effect of Multiplying (or Dividing) by a Constant • Multiplying (or dividing) each observation by the same number b (positive, negative, or zero): • multiplies (divides) measures of center and location by b • multiplies (divides) measures of spread by |b|, but • does not change the shape of the distribution Example, p. 95

20. Check for Understanding The above shows the heights of students in a class. • Suppose that you convert the heights from inches to centimeters (1 inch= 2.54 cm). Describe the effect this will have on the shape, center, and spread of the distribution. • If the entire class stood on a 6 in platform and then measured the distance from the ground to the top of their head, how would the shape, center, and spread of the distribution compare with the original height distribution?

21. Continued 3. Now suppose that you convert the class’s heights to z-scored. What would be the shape, center, and spread of this distribution. Explain.

22. Describing Location in a Distribution • Density Curves • In Chapter 1, we developed a kit of graphical and numerical tools for describing distributions. Now, we’ll add one more step to the strategy. Exploring Quantitative Data • Always plot your data: make a graph. • Look for the overall pattern (shape, center, and spread) and for striking departures such as outliers. • Calculate a numerical summary to briefly describe center and spread. 4. Sometimes the overall pattern of a large number of observations is so regular that we can describe it by a smooth curve.

23. Describing Location in a Distribution • Density Curve • Definition: • A density curve is a curve that • is always on or above the horizontal axis, and • has area exactly 1 underneath it. • A density curve describes the overall pattern of a distribution. The area under the curve and above any interval of values on the horizontal axis is the proportion of all observations that fall in that interval. The overall pattern of this histogram of the scores of all 947 seventh-grade students in Gary, Indiana, on the vocabulary part of the Iowa Test of Basic Skills (ITBS) can be described by a smooth curve drawn through the tops of the bars.

24. Density Curve

25. Check Your Understanding • Explain why this is a legitimate density curve. • About what proportion of observations lie between 7 and 8? • Where would the mean and the median occur approximately? • Explain why the mean and median have the relationship that they do in this case.

26. For each figure where is the mean and the median?

27. For each figure where is the mean and the median

28. Assignment on Transforming Data In 2010, Taxi Cabs in New York City charged an initial fee of \$2.50 plus \$2 per mile. In equation form, fare = 2.50 + 2(miles). At the end of a month a businessman collects all of his taxi cab receipts and analyzed the distribution of fares. The distribution was skewed to the right with a mean of \$15.45 and a standard deviation of \$10.20. • What are the mean and standard deviation of the lengths of his cab rides in miles?

29. Describing Location in a Distribution • Describing Density Curves • Our measures of center and spread apply to density curves as well as to actual sets of observations. Distinguishing the Median and Mean of a Density Curve The median of a density curve is the equal-areas point, the point that divides the area under the curve in half. The mean of a density curve is the balance point, at which the curve would balance if made of solid material. The median and the mean are the same for a symmetric density curve. They both lie at the center of the curve. The mean of a skewed curve is pulled away from the median in the direction of the long tail.

30. Section 2.1Describing Location in a Distribution Summary In this section, we learned that… • There are two ways of describing an individual’s location within a distribution – the percentile and z-score. • A cumulative relative frequency graph allows us to examine location within a distribution. • It is common to transform data, especially when changing units of measurement. Transforming data can affect the shape, center, and spread of a distribution. • We can sometimes describe the overall pattern of a distribution by a density curve (an idealized description of a distribution that smooths out the irregularities in the actual data).

31. Looking Ahead… In the next Section… • We’ll learn about one particularly important class of density curves – the Normal Distributions • We’ll learn • The 68-95-99.7 Rule • The Standard Normal Distribution • Normal Distribution Calculations, and • Assessing Normality

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