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+ Chapter 1: Exploring Data Section 1.3 Describing Quantitative Data with Numbers The Practice of Statistics, 4th edition - For AP* STARNES, YATES, MOORE + Chapter 1 Exploring Data Introduction: Data Analysis: Making Sense of Data 1.1 Analyzing Categorical Data 1.2 Displaying Quantitative Data with Graphs 1.3 Describing Quantitative Data with Numbers + Section 1.3 Describing Quantitative Data with Numbers Learning Objectives After this section, you should be able to… MEASURE center with the mean and median MEASURE spread with standard deviation and interquartile range IDENTIFY outliers CONSTRUCT a boxplot using the five-number summary CALCULATE numerical summaries with technology The most common measure of center is the ordinary arithmetic average, or mean. Definition: To find the mean x (pronounced “x-bar”) of a set of observations, add their values and divide by the number of observations. If the n observations are x1, x2, x3, …, xn, their mean is: sum of observations x1 x 2 ... x n x n n In mathematics, the capital Greek letter Σis short for “add them all up.” Therefore, the formula for the mean can be written in more compact notation: x x n i Describing Quantitative Data Center: The Mean + Measuring Example – McDonald’s Beef Sandwiches (g) Hamburger 9g Cheeseburger 12 g Double Cheeseburger 23 g McDouble 19 g Quarter Pounder® 19 g Quarter Pounder® with Cheese 26 g Double Quarter Pounder® with Cheese 42 g Big Mac® 29 g Big N' Tasty® 24 g Big N' Tasty® with Cheese 28 g Angus Bacon & Cheese 39 g Angus Deluxe 39 g Angus Mushroom & Swiss 40 g McRib ® 26 g Mac Snack Wrap 19 g 0 1 1 2 2 3 3 4 9 2 999 34 6689 99 02 Key: 2|3 represents 23 grams of fat in a McDonald’s beef sandwich. (a) Find the mean amount of fat total for all 15 beef sandwiches. x Describing Quantitative Data Here are data for the amount of fat (in grams) for McDonald’s beef Fat sandwiches: + Alternate 9 12 ...19 26.3 grams 15 (b) The three Angus burgers are relatively new additions to the menu. How much did they increase the average when they were added? x 23.0 grams The Angus burgers increased the mean amount of fat by 3.3 grams. Another common measure of center is the median. In section 1.2, we learned that the median describes the midpoint of a distribution. Definition: The median M is the midpoint of a distribution, the number such that half of the observations are smaller and the other half are larger. To find the median of a distribution: 1)Arrange all observations from smallest to largest. 2)If the number of observations n is odd, the median M is the center observation in the ordered list. 3)If the number of observations n is even, the median M is the average of the two center observations in the ordered list. Describing Quantitative Data Center: The Median + Measuring Use the data below to calculate the mean and median of the commuting times (in minutes) of 20 randomly selected New York workers. Example, page 53 10 30 5 25 40 20 10 15 30 20 15 20 85 15 65 15 60 60 40 45 10 30 5 25 ... 40 45 x 31.25 minutes 20 0 1 2 3 4 5 6 7 8 5 005555 0005 Key: 4|5 00 represents a 005 005 5 New York worker who reported a 45minute travel time to work. 20 25 M 22.5 minutes 2 Describing Quantitative Data Center + Measuring Alternate Example – McDonald’s Chicken Sandwiches Finding the median when n is even. Here is data for the amount of fat (in grams) for McDonald’s chicken sandwiches: 0 99 McChicken ® Premium Grilled Chicken Classic Premium Crispy Chicken Classic Premium Grilled Chicken Club Sandwich Premium Crispy Chicken Club Sandwich Premium Grilled Chicken Ranch BLT Premium Crispy Chicken Ranch BLT Southern Style Crispy Chicken Ranch Snack Wrap® (Crispy) Ranch Snack Wrap® (Grilled) Honey Mustard Snack Wrap® (Crispy) Honey Mustard Snack Wrap® (Grilled) Chipotle BBQ Snack Wrap® (Crispy) Chipotle BBQ Snack Wrap® (Grilled) Fat 16 g 10 g 20 g 17 g 28 g 12 g 23 g 17 g 17 g 10 g 16 g 9g 15 g 9g 1 1 2 2 3 002 566777 03 8 Key: 2|3 represents 23 grams of fat in a McDonald’s chicken sandwich. Describing Quantitative Data Finding the median when n is odd. Here are the amounts of fat (in grams) for McDonald’s beef sandwiches, in order: 9 12 19 19 19 23 24 26 26 28 29 39 39 40 42 The bold 26 in the middle of the list is the median M. There are 7 values below the median and 7 values above the median. + Alternate Example – McDonald’s Beef Sandwiches Find and interpret the median. Since there are an even number of values (14), there is no middle value. The center pair of values, the bold 16s in the stemplot, have an average of 16. Thus, M = 16. About half of the chicken sandwiches at McDonald’s have less than 16 grams of fat and about half have more than 16 grams of fat. The mean and median measure center in different ways, and both are useful. Don’t confuse the “average” value of a variable (the mean) with its “typical” value, which we might describe by the median. Comparing the Mean and the Median The mean and median of a roughly symmetric distribution are close together. If the distribution is exactly symmetric, the mean and median are exactly the same. In a skewed distribution, the mean is usually farther out in the long tail than is the median. + Comparing the Mean and the Median Describing Quantitative Data A measure of center alone can be misleading. A useful numerical description of a distribution requires both a measure of center and a measure of spread. How to Calculate the Quartiles and the Interquartile Range To calculate the quartiles: 1)Arrange the observations in increasing order and locate the median M. 2)The first quartile Q1 is the median of the observations located to the left of the median in the ordered list. 3)The third quartile Q3 is the median of the observations located to the right of the median in the ordered list. The interquartile range (IQR) is defined as: IQR = Q3 – Q1 Describing Quantitative Data Spread: The Interquartile Range (IQR) + Measuring and Interpret the IQR + Find Travel times to work for 20 randomly selected New Yorkers 10 30 5 25 40 20 10 15 30 20 15 20 85 15 65 15 60 60 40 45 5 10 10 15 15 15 15 20 20 20 25 30 30 40 40 45 60 60 65 85 Q1 = 15 M = 22.5 Q3= 42.5 IQR = Q3 – Q1 = 42.5 – 15 = 27.5 minutes Interpretation: The range of the middle half of travel times for the New Yorkers in the sample is 27.5 minutes. Describing Quantitative Data Example, page 57 + Alternate Example – McDonald’s Beef Sandwiches 9 12 19 19 19 23 24 26 26 28 29 39 39 40 42 Since there are an odd number of observations, the median is the middle one, the bold 26 in the list. The first quartile is the median of the 7 observations to the left of the median, which is the highlighted 19. The third quartile is the median of the 7 observations to the right of the median, which is the highlighted 39. Thus, the interquartile range is IQR = 39 – 19 = 20 grams of fat. Alternate Example–McDonald’s Chicken Sandwiches Here are the 14 amounts of fat in order: 9 9 10 10 12 15 16 16 17 17 17 20 23 28 Since there is no middle value, the median falls between the two middle values (the two 16s). The first quartile is the median of the 7 values to the left of the median (Q1 = 10) and the third quartile is the median of the 7 values to the right of the median (Q3 = 17). Thus, IQR = 17 – 10 = 7 grams of fat. The range of the middle 50% of amounts of fat for McDonald’s chicken sandwiches is 7 grams. Describing Quantitative Data Here are the amounts of fat in the 15 McDonald’s beef sandwiches, in order. In addition to serving as a measure of spread, the interquartile range (IQR) is used as part of a rule of thumb for identifying outliers. Definition: The 1.5 x IQR Rule for Outliers Call an observation an outlier if it falls more than 1.5 x IQR above the third quartile or below the first quartile. Example, page 57 In the New York travel time data, we found Q1=15 minutes, Q3=42.5 minutes, and IQR=27.5 minutes. 0 1 2 For these data, 1.5 x IQR = 1.5(27.5) = 41.25 3 Q1 - 1.5 x IQR = 15 – 41.25 = -26.25 4 Q3+ 1.5 x IQR = 42.5 + 41.25 = 83.75 5 Any travel time shorter than -26.25 minutes or longer than 6 7 83.75 minutes is considered an outlier. 8 5 005555 0005 00 005 005 5 Describing Quantitative Data Outliers + Identifying Problem: Determine whether the Premium Crispy Chicken Club Sandwich with 28 grams of fat is an outlier. Solution: Here are the 14 amounts of fat in order: 9 9 10 10 12 15 16 16 17 17 17 20 23 28 Earlier we found that Q1 = 10, M = 16 and Q3 = 17. Outliers are any values below Q1 – 1.5IQR = 10 – 1.5(7) = -0.5 and any values above Q3 + 1.5IQR = 17 + 1.5(7) = 27.5. Thus, the value 28 is an outlier. Describing Quantitative Data + Alternate Example–McDonald’s Chicken Sandwiches + Five-Number Summary The minimum and maximum values alone tell us little about the distribution as a whole. Likewise, the median and quartiles tell us little about the tails of a distribution. To get a quick summary of both center and spread, combine all five numbers. Definition: The five-number summary of a distribution consists of the smallest observation, the first quartile, the median, the third quartile, and the largest observation, written in order from smallest to largest. Minimum Q1 M Q3 Maximum Describing Quantitative Data The The five-number summary divides the distribution roughly into quarters. This leads to a new way to display quantitative data, the boxplot. How to Make a Boxplot •Draw and label a number line that includes the range of the distribution. •Draw a central box from Q1 to Q3. •Note the median M inside the box. •Extend lines (whiskers) from the box out to the minimum and maximum values that are not outliers. + Boxplots (Box-and-Whisker Plots) Describing Quantitative Data a Boxplot + Construct Consider our NY travel times data. Construct a boxplot. 10 30 5 25 40 20 10 15 30 20 15 20 85 15 65 15 60 60 40 45 5 10 10 15 15 15 15 20 20 20 25 30 30 40 40 45 60 60 65 85 Min=5 Q1 = 15 M = 22.5 Q3= 42.5 Max=85 Recall, this is an outlier by the 1.5 x IQR rule Describing Quantitative Data Example Example – The Previous Home Run + Alternate 13 27 26 44 30 39 40 34 45 44 24 32 44 39 29 44 38 47 34 40 20 12 10 Here is the data in order, with the 5 number summary identified: 10 12 13 20 24 26 27 29 30 32 34 34 38 39 39 40 40 44 44 44 44 45 47 The boundaries for outliers are 26 – 1.5(44 – 26) = -1 and 44 + 1.5(44 – 26) = 71, so there are no outliers. Collection 1 Box Plot Here is a boxplot of the data: 10 15 20 25 30 35 40 45 50 HomeRuns Describing Quantitative Data Here are the number of home runs that Hank Aaron hit in each of his 23 seasons: The most common measure of spread looks at how far each observation is from the mean. This measure is called the standard deviation. Let’s explore it! Consider the following data on the number of pets owned by a group of 9 children. 1) Calculate the mean. 2) Calculate each deviation. deviation = observation – mean deviation: 1 - 5 = -4 deviation: 8 - 5 = 3 x =5 Describing Quantitative Data Spread: The Standard Deviation + Measuring Spread: The Standard Deviation (xi-mean) 1 1 - 5 = -4 (-4)2 = 16 3 3 - 5 = -2 (-2)2 = 4 3) Square each deviation. 4 4 - 5 = -1 (-1)2 = 1 4) Find the “average” squared deviation. Calculate the sum of the squared deviations divided by (n-1)…this is called the variance. 4 4 - 5 = -1 (-1)2 = 1 4 4 - 5 = -1 (-1)2 = 1 5 5-5=0 (0)2 = 0 7 7-5=2 (2)2 = 4 8 8-5=3 (3)2 = 9 9 9-5=4 (4)2 = 16 5) Calculate the square root of the variance…this is the standard deviation. Sum=? “average” squared deviation = 52/(9-1) = 6.5 Standard deviation = square root of variance = Describing Quantitative Data (xi-mean)2 xi + Measuring Sum=? This is the variance. 6.5 2.55 Spread: The Standard Deviation The standard deviation sx measures the average distance of the observations from their mean. It is calculated by finding an average of the squared distances and then taking the square root. This average squared distance is called the variance. (x1 x ) 2 (x 2 x ) 2 ... (x n x ) 2 1 variance = s (x i x ) 2 n 1 n 1 2 x 1 2 standard deviation = sx (x x ) i n 1 Describing Quantitative Data Definition: + Measuring x Example – Foot lengths 25, 22, 20, 25, 24, 24, 28 The mean foot length is 24 cm. Here are the deviations from the mean (xi – x ) and the squared deviations from the mean (xi – x )2: x 25 22 20 25 24 24 28 xi – x 25 – 24 = 1 22 – 24 = -2 20 – 24 = -4 25 – 24 = 1 24 – 24 = 0 24 – 24 = 0 28 – 24 = 4 Sum = 0 ( x i – x )2 (1)2 = 1 (-2)2 = 4 (-4)2 = 16 (1)2 = 1 (0)2 = 0 (0)2 = 0 (4)2 = 16 Sum = 38 The " average" squared deviation is 38 6.33, so the standard deviation 7 -1 6.33 2.52 cm. This 2.52 cm is roughly th e average distance each foot length is from the mean. Describing Quantitative Data Here are the foot lengths (in centimeters) for a random sample of 7 fourteen-year-olds from the United Kingdom. + Alternate We now have a choice between two descriptions for center and spread Mean and Standard Deviation Median and Interquartile Range Choosing Measures of Center and Spread •The median and IQR are usually better than the mean and standard deviation for describing a skewed distribution or a distribution with outliers. •Use mean and standard deviation only for reasonably symmetric distributions that don’t have outliers. •NOTE: Numerical summaries do not fully describe the shape of a distribution. ALWAYS PLOT YOUR DATA! + Choosing Measures of Center and Spread Describing Quantitative Data The following data show the number of contacts that a sample of high school students had in their cell phones. Feel free to use the data below, but it will probably be more interesting for your students if you collect and use their number of contacts. Male: 124 41 29 27 44 87 85 260 290 31 168 169 167 214 135 114 105 103 96 144 Female: 30 83 116 22 173 155 134 180 124 33 213 218 183 110 State: Do the data give convincing evidence that one gender has more contacts than the other? Plan: We will compute numerical summaries, graph parallel boxplots, and compare shape, center, spread and unusual values. + Alternate Example – Who has more contacts-males or females? Describing Quantitative Data Male Female 20 14 122.15 126.71 73.83 65.75 27 22 64.5 83 109.5 129 167.5 180 290 218 Shape: The female distribution is approximately symmetric but the male distribution is slightly skewed to the right. Center: The median number of contacts for the females is slightly higher than the median number of contacts for the males. Spread: The distribution of contacts for males is more spread out than the distribution of females since both the IQR and range is larger. Outliers: Neither of the distributions have any outliers. Conclude: In the samples, females have more contacts than males, since both the mean and median values are slightly larger. However, the differences are small so this is not convincing evidence that one gender has more contacts than the other. + Describing Quantitative Data Alternate Example – Who has more contacts-males or females? Do: Here are the numerical summaries and boxplots for each distribution: N sx min Q1 M Q3 max x + Section 1.3 Describing Quantitative Data with Numbers Summary In this section, we learned that… A numerical summary of a distribution should report at least its center and spread. The mean and median describe the center of a distribution in different ways. The mean is the average and the median is the midpoint of the values. When you use the median to indicate the center of a distribution, describe its spread using the quartiles. The interquartile range (IQR) is the range of the middle 50% of the observations: IQR = Q3 – Q1. + Section 1.3 Describing Quantitative Data with Numbers Summary In this section, we learned that… An extreme observation is an outlier if it is smaller than Q1–(1.5xIQR) or larger than Q3+(1.5xIQR) . The five-number summary (min, Q1, M, Q3, max) provides a quick overall description of distribution and can be pictured using a boxplot. The variance and its square root, the standard deviation are common measures of spread about the mean as center. The mean and standard deviation are good descriptions for symmetric distributions without outliers. The median and IQR are a better description for skewed distributions. + Looking Ahead… In the next Chapter… We’ll learn how to model distributions of data… • Describing Location in a Distribution • Normal Distributions