Modeling Distributions of Data 2.1 Describing Location in a Distribution 2.2 Normal Distributions

Normal Distributions After this section, you should be able to… Learning Objectives After this section, you should be able to… DESCRIBE and APPLY the 68-95-99.7 Rule DESCRIBE the standard Normal Distribution PERFORM Normal distribution calculations ASSESS Normality

Normal Distributions Normal Distributions One particularly important class of density curves are the Normal curves, which describe Normal distributions. All Normal curves are symmetric, single-peaked, and bell- shaped A Specific Normal curve is described by giving its mean µ and standard deviation σ. Normal Distributions Two Normal curves, showing the mean µ and standard deviation σ.

Density curves A density curve is a mathematical model of a distribution. The total area under the curve, by definition, is equal to 1, or 100%. The area under the curve for a range of values is the proportion of all observations for that range. Histogram of a sample with the smoothed, density curve describing theoretically the population. Here is our histogram. One woman in the first group, 2 in the second, etc. This is a normal distribution - it has a single peak, is symmetric, does not have outliers, and when a curve is drawn to describe it, the curve takes on a particular shape. 4

Density curves come in any imaginable shape. Some are well known mathematically and others aren’t.

A family of density curves Here means are the same (m = 15) while standard deviations are different (s = 2, 4, and 6). Here means are different (m = 10, 15, and 20) while standard deviations are the same (s = 3)

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. Describing Location in a Distribution 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. 7

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. Describing Location in a Distribution 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.

Normal Distributions Normal Distributions Definition: A Normal distribution is described by a Normal density curve. Any particular Normal distribution is completely specified by two numbers: its mean µ and standard deviation σ. The mean of a Normal distribution is the center of the symmetric Normal curve. The standard deviation is the distance from the center to the change-of-curvature points on either side. We abbreviate the Normal distribution with mean µ and standard deviation σ as N(µ,σ). Normal distributions are good descriptions for some distributions of real data. Normal distributions are good approximations of the results of many kinds of chance outcomes. Many statistical inference procedures are based on Normal distributions.

Normal Distributions The 68-95-99.7 Rule Although there are many Normal curves, they all have properties in common. Normal Distributions Definition: The 68-95-99.7 Rule (“The Empirical Rule”) In the Normal distribution with mean µ and standard deviation σ: Approximately 68% of the observations fall within σ of µ. Approximately 95% of the observations fall within 2σ of µ. Approximately 99.7% of the observations fall within 3σ of µ. 99.7%

Interpreting Standard Deviation: Empirical Rule (68 – 95 – 99.7 Rule) For data with a (symmetric) bell-shaped distribution, the standard deviation has the following characteristics: About 68% of the data lie within one standard deviation of the mean. About 95% of the data lie within two standard deviations of the mean. About 99.7% of the data lie within three standard deviations of the mean. 11

Interpreting Standard Deviation: Empirical Rule (68 – 95 – 99.7 Rule) 99.7% within 3 standard deviations 2.35% 95% within 2 standard deviations 13.5% 68% within 1 standard deviation 34% 12

All Normal curves N(m,s) share the same properties About 68% of all observations are within 1 standard deviation (s) of the mean (m). About 95% of all observations are within 2 s of the mean m. Almost all (99.7%) observations are within 3 s of the mean. Inflection point Going to an example from the book on women’s heights, the mean here was 64.5, standard deviation 2.5 inches. When we talk about the mean and standard deviation with respect to the curve instead of the actual sample, we use different notation. Mu for mean, sigma for sd. If you consider the area under the curve to represent all of the individuals, then you can divide it into chunks to represent parts of the whole. Like if you divided it down the middle, half of the people are in each half. Here it is divided up into parts not through the middle but by lines that are 1, 2 or 3 standard deviations away from the mean. If you look at the center, pink part, it is the area 1 sd on either side of the mean. By definition for normal curves, this area is 68% of the total. So if you know the mean and sd, you also know that 68% of women are between 62 and 67 inches tall. Similarly for the areas defined by lines drawn 2 or 3 sd from the mean. We might want to know what percent of women are over 72 inches tall. That is 3 sd. We can see that 99.7 percent of women are less than 72 or greater than 57. Or that .3 percent of women are really tall or really short. Since the distribution is symmetric, we can divide by two to find the percent of women that are really tall: .15% You need to be able to work problems like I just did - bunch in book. But what if you want to know something not defined by the sd? Like, what percentage of women are taller than 68 inches? Know that half are smaller than 64.5. And that half of this middle area, 34%, are smaller than 67 inches, so 50+34 = 84% are smaller than 67, or 16% are larger than 67 inches. But you want to know the proportion larger than 68 inches. You can look this up on a table, but first you have to do something called standardizing. The reason is that although all normal curves share the properties shown above, they differ by their mean and standard deviation. You would have to have a different table for every curve. When you standardize a normal distribution, you change it so the mean is 0 and the sd is 1. Any normal distribution can be standardized. mean µ = 64.5 standard deviation s = 2.5 N(µ, s) = N(64.5, 2.5) Reminder: µ (mu) is the mean of the idealized curve, while is the mean of a sample. s (sigma) is the standard deviation of the idealized curve, while s is the s.d. of a sample. 13

Standardized height (no units) The standard Normal distribution Because all Normal distributions share the same properties, we can standardize our data to transform any Normal curve N(m,s) into the standard Normal curve N(0,1). N(0,1) => N(64.5, 2.5) Standardized height (no units) For each x we calculate a new value, z (called a z-score).

The cool thing about working with normally distributed data is that we can manipulate it and then find answers to questions that involve comparing seemingly non-comparable distributions. We do this by “standardizing” the data. All this involves is changing the scale so that the mean now = 0 and the standard deviation =1. If you do this to different distributions it makes them comparable. N(0,1) We do this by standardizing the distributions - really all this is redefining them not changing the shape but the bottom axis so that instead of being N(mu, sigma) they are N(mean =0,sd=1), and the bottom axis is in terms of the SD rather than the Height. You get this by calculating a value z for every point in x your data set. If you were to then draw the density curve for the z values you get a curve with a mean of 0 and a sd of 1. Once you have standardized, you can look up any value you want using a table. So, for instance, we knew that 68% of women were between 62 and 67 inches tall from knowing simple rules about 1,2,3 sd from mean. But if wanted to know the percentage of women that were less than 63 inches tall. Can’t just use those rules. need to standardize and go to table A - standard normal probabilities - on green card in book or in back. First standardize x to get z, the number of sd from the mean. It is 0.6 to the left (is negative). Look for -0.6 in left column (z), and then going across row, under .00 column (no more decimals on (-0.6) you find .2743. Twenty seven percent of women are shorter than 62 inches tall. 15

The distribution of Iowa Test of Basic Skills (ITBS) vocabulary scores for 7th grade students in Gary, Indiana, is close to Normal. Suppose the distribution is N(6.84, 1.55). Sketch the Normal density curve for this distribution. What percent of ITBS vocabulary scores are less than 3.74? What percent of the scores are between 5.29 and 9.94? Normal Distributions

The Standard Normal Distribution All Normal distributions are the same if we measure in units of size σ from the mean µ as center. Normal Distributions Definition: The standard Normal distribution is the Normal distribution with mean 0 and standard deviation 1. If a variable x has any Normal distribution N(µ,σ) with mean µ and standard deviation σ, then the standardized variable has the standard Normal distribution, N(0,1).

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. Describing Location in a Distribution 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?

Describing Location in a Distribution Using z-scores for Comparison Describing Location in a Distribution We can use z-scores to compare the position of individuals in different distributions. 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? 19

Using Table A Table A gives the area under the standard Normal curve to the left of any z value. .0082 is the area under N(0,1) left of z = -2.40 .0080 is the area under N(0,1) left of z = -2.41 0.0069 is the area under N(0,1) left of z = -2.46 We do this by standardizing the distributions - really all this is redefining them not changing the shape but the bottom axis so that instead of being N(mu, sigma) they are N(mean =0,sd=1), and the bottom axis is in terms of the SD rather than the Height. You get this by calculating a value z for every point in x your data set. If you were to then draw the density curve for the z values you get a curve with a mean of 0 and a sd of 1. Once you have standardized, you can look up any value you want using a table. So, for instance, we knew that 68% of women were between 62 and 67 inches tall from knowing simple rules about 1,2,3 sd from mean. But if wanted to know the percentage of women that were less than 63 inches tall. Can’t just use those rules. need to standardize and go to table A - standard normal probabilities - on green card in book or in back. First standardize x to get z, the number of sd from the mean. It is 0.6 to the left (is negative). Look for -0.6 in left column (z), and then going across row, under .00 column (no more decimals on (-0.6) you find .2743. Twenty seven percent of women are shorter than 62 inches tall. (…)

The Standard Normal Table Normal Distributions Because all Normal distributions are the same when we standardize, we can find areas under any Normal curve from a single table. Definition: The Standard Normal Table Table A is a table of areas under the standard Normal curve. The table entry for each value z is the area under the curve to the left of z. Suppose we want to find the proportion of observations from the standard Normal distribution that are less than 0.81. We can use Table A: P(z < 0.81) = .7910 Z .00 .01 .02 0.7 .7580 .7611 .7642 0.8 .7881 .7910 .7939 0.9 .8159 .8186 .8212

Normal Distributions Finding Areas Under the Standard Normal Curve Find the proportion of observations from the standard Normal distribution that are between -1.25 and 0.81. Can you find the same proportion using a different approach? 1 - (0.1056+0.2090) = 1 – 0.3146 = 0.6854

Normal Distribution Calculations Normal Distributions How to Solve Problems Involving Normal Distributions State: Express the problem in terms of the observed variable x. Plan: Draw a picture of the distribution and shade the area of interest under the curve. Do: Perform calculations. Standardize x to restate the problem in terms of a standard Normal variable z. Use Table A and the fact that the total area under the curve is 1 to find the required area under the standard Normal curve. Conclude: Write your conclusion in the context of the problem.

Ex. Women heights N(µ, s) = N(64.5, 2.5) Women heights follow the N(64.5”,2.5”) distribution. What percent of women are shorter than 67 inches tall (that’s 5’6”)? Area= ??? Area = ??? mean µ = 64.5" standard deviation s = 2.5" x (height) = 67" m = 64.5” x = 67” z = 0 z = 1 We calculate z, the standardized value of x: We do this by standardizing the distributions - really all this is redefining them not changing the shape but the bottom axis so that instead of being N(mu, sigma) they are N(0,1). we had a bunch of observations we called x - height of women, and they come from this distribution N(mu, sigma). Percentage of women shorter than 67 is 50+half of 68=34, or 84% Because of the 68-95-99.7 rule, we can conclude that the percent of women shorter than 67” should be, approximately, .68 + half of (1 - .68) = .84 or 84%. 24

Percent of women shorter than 67” For z = 1.00, the area under the standard Normal curve to the left of z is 0.8413. N(µ, s) = N(64.5”, 2.5”) Area ≈ 0.84 Conclusion: 84.13% of women are shorter than 67”. By subtraction, 1 - 0.8413, or 15.87% of women are taller than 67". Area ≈ 0.16 m = 64.5” x = 67” z = 1

Tips on using Table A area right of z = area left of -z Because the Normal distribution is symmetrical, there are 2 ways that you can calculate the area under the standard Normal curve to the right of a z value. area right of z = 1 - area left of z

area left of z1 – area left of z2 Tips on using Table A To calculate the area between 2 z- values, first get the area under N(0,1) to the left for each z-value from Table A. Then subtract the smaller area from the larger area. A common mistake made by students is to subtract both z values. But the Normal curve is not uniform. area between z1 and z2 = area left of z1 – area left of z2  The area under N(0,1) for a single value of z is zero (Try calculating the area to the left of z minus that same area!)

What proportion of all students would be NCAA qualifiers (SAT ≥ 820)? The National Collegiate Athletic Association (NCAA) requires Division I athletes to score at least 820 on the combined math and verbal SAT exam to compete in their first college year. The SAT scores of 2003 were approximately normal with mean 1026 and standard deviation 209. What proportion of all students would be NCAA qualifiers (SAT ≥ 820)? area right of 820 = total area - area left of 820 = 1 - 0.1611 ≈ 84% Note: The actual data may contain students who scored exactly 820 on the SAT. However, the proportion of scores exactly equal to 820 is 0 for a normal distribution is a consequence of the idealized smoothing of density curves.

The NCAA defines a “partial qualifier” eligible to practice and receive an athletic scholarship, but not to compete, as a combined SAT score is at least 720. What proportion of all students who take the SAT would be partial qualifiers? That is, what proportion have scores between 720 and 820? area between = area left of 820 - area left of 720 720 and 820 = 0.1611 - 0.0721 ≈ 9% About 9% of all students who take the SAT have scores between 720 and 820.

Ex. Gestation time in malnourished mothers What is the effects of better maternal care on gestation time and premies? The goal is to obtain pregnancies 240 days (8 months) or longer. What improvement did we get by adding better food? 266 s 15 250 s 20 Now, this will become more apparent later on in the class, but the cool thing about standardizing is that it allows you to compare across different scales. Remember we started out the day using gestation time as an example. Women who are malnourished risk have premature babies, and studies are being done to see whether different diet and vitamin supplements work better. Let’s say the goal is to get them to carry the baby at least 240 days (8 months). For treatment 1, say vitamins only, get normal distribution with mean 250, sd is 20. Treatment 2 is vitamins plus a meals on wheels program. Mean is 266, sd 15 . The mean is increased, but the spread has changed too. You can eyeball this and see that more of the women in treatment two are above our goal of 240 days, but how much of an improvement is it? 30

Under each treatment, what percent of mothers failed to carry their babies at least 240 days? Vitamins Only m=250, s=20, x=240 Remember we started out the day using gestation time as an example. Women who are malnourished risk have premature babies, and studies are being done to see whether different diet and vitamin supplements work better. Let’s say the goal is to get them to carry the baby at least 240 days (8 months). For treatment 1, say vitamins only, get normal distribution with mean 250, sd is 20. Treatment 2 is vitamins plus a meals on wheels program. Mean is 266, sd 15 . You can eyeball this and see that more of the women in treatment two are above our goal of 240 days, but how much of an improvement is it? The mean is increased, but the spread has changed too. Let’s standardize to get the proportion of women below 240 in each distribution. Go through - bottom line is that adding food to vitamins resulted in the proportion of women with gestation times of less than 240 days going from 30.85% to only 4.18%. You see figures like this in news stories all the time - if you went to the primary (medical literature) you would see that they had to go through this rigamarole to get you that tidy summary. Vitamins only: 30.85% of women would be expected to have gestation times shorter than 240 days. 31

Vitamins and better food m=266, s=15, x=240 Vitamins and better food: 4.18% of women would be expected to have gestation times shorter than 240 days. Remember we started out the day using gestation time as an example. Women who are malnourished risk have premature babies, and studies are being done to see whether different diet and vitamin supplements work better. Let’s say the goal is to get them to carry the baby at least 240 days (8 months). For treatment 1, say vitamins only, get normal distribution with mean 250, sd is 20. Treatment 2 is vitamins plus a meals on wheels program. Mean is 266, sd 15 . You can eyeball this and see that more of the women in treatment two are above our goal of 240 days, but how much of an improvement is it? The mean is increased, but the spread has changed too. Let’s standardize to get the proportion of women below 240 in each distribution. Go through - bottom line is that adding food to vitamins resulted in the proportion of women with gestation times of less than 240 days going from 30.85% to only 4.18%. You see figures like this in news stories all the time - if you went to the primary (medical literature) you would see that they had to go through this rigamarole to get you that tidy summary. Compared to vitamin supplements alone, vitamins and better food resulted in a much smaller percentage of women with pregnancy terms below 8 months (4% vs. 31%). 32

Normal Distribution Calculations Normal Distributions When Tiger Woods hits his driver, the distance the ball travels can be described by N(304, 8). What percent of Tiger’s drives travel between 305 and 325 yards? Using Table A, we can find the area to the left of z=2.63 and the area to the left of z=0.13. 0.9957 – 0.5517 = 0.4440. About 44% of Tiger’s drives travel between 305 and 325 yards.

Assessing Normality Normal Distributions The Normal distributions provide good models for some distributions of real data. Many statistical inference procedures are based on the assumption that the population is approximately Normally distributed. Consequently, we need a strategy for assessing Normality. Normal Distributions Plot the data. Make a dotplot, stemplot, or histogram and see if the graph is approximately symmetric and bell-shaped. Check whether the data follow the 68-95-99.7 rule. Count how many observations fall within one, two, and three standard deviations of the mean and check to see if these percents are close to the 68%, 95%, and 99.7% targets for a Normal distribution.

Normal Probability Plots Most software packages can construct Normal probability plots. These plots are constructed by plotting each observation in a data set against its corresponding percentile’s z-score. Normal Distributions Interpreting Normal Probability Plots If the points on a Normal probability plot lie close to a straight line, the plot indicates that the data are Normal. Systematic deviations from a straight line indicate a non-Normal distribution. Outliers appear as points that are far away from the overall pattern of the plot.

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

Example: Using the Empirical Rule In a survey conducted by the National Center for Health Statistics, the sample mean height of women in the United States (ages 20-29) was 64 inches, with a sample standard deviation of 2.71 inches. Estimate the percent of the women whose heights are between 64 inches and 69.42 inches. 37

Solution: Using the Empirical Rule Because the distribution is bell-shaped, you can use the Empirical Rule. 34% 13.5% 55.87 58.58 61.29 64 66.71 69.42 72.13 34% + 13.5% = 47.5% of women are between 64 and 69.42 inches tall. 38

Example: Comparing z-Scores from Different Data Sets In 2007, Forest Whitaker won the Best Actor Oscar at age 45 for his role in the movie The Last King of Scotland. Helen Mirren won the Best Actress Oscar at age 61 for her role in The Queen. The mean age of all best actor winners is 43.7, with a standard deviation of 8.8. The mean age of all best actress winners is 36, with a standard deviation of 11.5. Find the z-score that corresponds to the age for each actor or actress. Then compare your results. 39

Solution: Comparing z-Scores from Different Data Sets Forest Whitaker 0.15 standard deviations above the mean Helen Mirren 2.17 standard deviations above the mean 40

Solution: Comparing z-Scores from Different Data Sets The z-score corresponding to the age of Helen Mirren is more than two standard deviations from the mean, so it is considered unusual. Compared to other Best Actress winners, she is relatively older, whereas the age of Forest Whitaker is only slightly higher than the average age of other Best Actor winners. 41

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. Describing Location in a Distribution 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.

Normal Distributions Summary In this section, we learned that… The Normal Distributions are described by a special family of bell- shaped, symmetric density curves called Normal curves. The mean µ and standard deviation σ completely specify a Normal distribution N(µ,σ). The mean is the center of the curve, and σ is the distance from µ to the change-of-curvature points on either side. All Normal distributions obey the 68-95-99.7 Rule, which describes what percent of observations lie within one, two, and three standard deviations of the mean.

Normal Distributions In this section, we learned that… All Normal distributions are the same when measurements are standardized. The standard Normal distribution has mean µ=0 and standard deviation σ=1. Table A gives percentiles for the standard Normal curve. By standardizing, we can use Table A to determine the percentile for a given z-score or the z-score corresponding to a given percentile in any Normal distribution. To assess Normality for a given set of data, we first observe its shape. We then check how well the data fits the 68-95-99.7 rule. We can also construct and interpret a Normal probability plot.

Inverse normal calculations We may also want to find the observed range of values that correspond to a given proportion/ area under the curve. For that, we use Table A backward: For an area to the left of 1.25 % (0.0125), we first find the desired area/ proportion in the body of the table, we then read the corresponding z-value from the left column and top row. For an area to the left of 1.25 % (0.0125), the z-value is -2.24

the z-value is -2.24 Vitamins and better food Summary How long are the longest 75% of pregnancies when malnutritioned mothers are given vitamins and better food? m=266, s=15, upper area 75% Summary the z-value is -2.24 upper 75% ? Remember we started out the day using gestation time as an example. Women who are malnourished risk have premature babies, and studies are being done to see whether different diet and vitamin supplements work better. Let’s say the goal is to get them to carry the baby at least 240 days (8 months). For treatment 1, say vitamins only, get normal distribution with mean 250, sd is 20. Treatment 2 is vitamins plus a meals on wheels program. Mean is 266, sd 15 . You can eyeball this and see that more of the women in treatment two are above our goal of 240 days, but how much of an improvement is it? The mean is increased, but the spread has changed too. Let’s standardize to get the proportion of women below 240 in each distribution. Go through - bottom line is that adding food to vitamins resulted in the proportion of women with gestation times of less than 240 days going from 30.85% to only 4.18%. You see figures like this in news stories all the time - if you went to the primary (medical literature) you would see that they had to go through this rigamarole to get you that tidy summary. Remember that Table A gives the area to the left of z. Thus we need to search for the lower 25% in Table A in order to get z.  The 75% longest pregnancies in this group are about 256 days or longer.