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Published byFaith Compton Modified over 4 years ago

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**THE STANDARD DEVIATION AS A RULER AND THE NORMAL MODEL**

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**in such different events be compared?**

The women’s heptathlon in the Olympics consists of seven track and field events: the 200-m & the 800-m runs, 100-m hurdles, shot put, javelin, high jump, and long jump. Somehow, the performances have to be combined into one score. How can performances in such different events be compared?

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**Which performance deserves more points?**

They don’t even have the same units; the races are recorded in seconds and the throwing and jumping events, in meters. In the 2000 Olympics, the best 800-m time, run by Getrud Bacher of Italy, was 8 seconds faster than the mean. The winning long jump by the Russian Yelena Prokhorova was 60 cm longer than the mean. Which performance deserves more points?

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**The trick in comparing very different looking values**

is to use the standard deviation. “How far is the value from the mean?” “How different are these two statistics?” The standard deviation tells us how the collection of values varies, so it’s a natural ruler for comparing an individual to the group.

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Bacher’s winning 800-m time of 129 seconds was 8 seconds faster than the mean of 137 seconds. How many standard deviations better than the mean is that? The standard deviation of all 27 qualifying times was 5 seconds. In other words, and x = 129 or 1.6 standard deviations better than the mean. Calculate Prokhorova’s performance in terms of “standard deviations better than the mean” if her winning long jump was 660 cm and the average long jump was 6-m with a standard deviation of 30 cm. Was Bacher’s winning 800-m sprint better or Prokhorova’s winning long jump?

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**STANDARDIZING WITH Z-SCORES**

To understand how an athlete performed in a heptathlon event, we standardized her result, finding out how many standard deviations from the event mean she performed. We call the results standardized values or “z-scores”

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Standardized values have NO UNITS because z-scores measure the distance of each data value from the mean in standard deviations. A z-score of 2 tells us that a data value is 2 standard deviations above the mean. It doesn’t matter whether the original variable was measured in inches, dollars, or seconds. What does having a z-score of -3 mean? Regardless of what direction, the farther the data value is from the mean, the more unusual it is.

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Now we can compare values that are measured on different variables, with different scales, with different units, or for different populations. What method did we learn about in the last chapter that enabled us to change units or scales?

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ANSWER THIS: Your statistics teacher has announced that the lower of your two tests will be dropped. You got a 90 on test 1 and an 80 on test 2. You’re all set to drop the 80 until she announces that she grades “on a curve.” She standardized the scores in order to decide which is the lower one. If the mean of the first test is 88 with a standard deviation of 4 and the mean of the second was a 75 with a standard deviation of 5, which one will be dropped and is this “fair”?

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**LINEAR TRANSFORMATIONS**

SHIFTING DATA Adding or subtracting a constant to every data value adds (or subtracts) the same constant to measures of position, but leaves measures of spread unchanged. RESCALING When we multiply (or divide) all the data values by any constant, all measures of position (mean, median, percentiles), and measures of spread (range, IQR, std dev) are multiplied (or divided) by that same constant.

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**IMPORTANT Standardizing data into z-scores is just shifting**

them by the mean and then rescaling them by the standard deviation. When we shift the data by subtracting the mean from every data value, we are shifting the mean to what value? Does this change the standard deviation? When we divide each of these shifted values by s, the standard deviation should be divided as well. Since the standard deviation was s to start with, the new standard deviation becomes what value?

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**HOW DOES STANDARDIZING AFFECT THE DISTRIBUTION OF A VARIABLE?**

Standardizing into z-scores: does not change the shape of the distribution of a variable changes the center by making the mean 0 changes the spread by making the standard deviation 1

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EXAMPLE #1 Many colleges and universities require applicants to submit scores on standardized tests. The college you want to apply to says that while there is no minimum score required, the middle 50% of their students have combined SAT scores between 1530 and You’d feel confident if you knew her score was in their top 25%, but unfortunately, you took the ACT test. How high does your ACT need to be to make it into the top quarter of equivalent SAT scores? For college bound seniors, the average SAT score is about 1500 and the standard deviation is about 250 points. For the same group, the ACT average is 20.8 with a standard deviation of 4.8.

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**Quantitative Variable Condition**

scores for both tests are quantitative but have no meaningful units other than points If the middle 50% scores between 1530 and 1850, if you want to be in the top quarter, what score would you have to have?

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**Calculate the corresponding z-score:**

The SAT score of 1850 is 1.4 standard deviations above the mean of all test takers. For the ACT, 1.40 standard deviations above the mean is (4.8) = 27.52 So to be in the top quarter of applicants, you need to have an ACT score of at least

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**How far does a z-score have to be from zero in order to indicate that it is surprising or unusual?**

We need to MODEL the data’s distribution. A model will let us say much more precisely how often we’d be likely to see z-scores of different sizes. Models will be wrong – they can’t match reality exactly, but they will still be useful.

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AP STATISTICS DO NOW: Check your homework solutions with your group (and the key on your desks).

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**A COMPARISON DISTRIBUTION MODEL Real data Theoretical values Observed**

Imagined Histogram Mathematical curve Statistics Parameters Center Spread s Spread

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**Bell shaped curves are called Normal Models**

Bell shaped curves are called Normal Models. Normal models are appropriate for distributions whose shapes are unimodal and roughly symmetric. There is a normal model for every possible combination of mean and standard deviation. We write to represent a normal model with a mean of mu and a standard deviation of sigma. Why the Greek? This particular mean and standard deviation are not numerical summaries of the data. They are part of the model. Such numbers are called parameters of the model. We use greek letters for parameters and latin letters for statistics (numerical summaries of actual data). The z-score formula becomes It’s easier to standardize the data first and then we can use the model N(0, 1) – this normal model is called the STANDARD NORMAL MODEL or the STANDARD NORMAL DISTRIBUTION. Let’s practice writing sigma!

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**Be careful: you don’t want to use the Normal model for any distribution.**

You must check the Normality Assumption or Nearly Normal Condition: the shape of the data’s distribution is unimodal and symmetric MAKE A PICTURE FIRST

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**Does your picture look like this?**

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**About 68% of the values fall within 1 standard deviation of the mean**

EMPIRICAL RULE About 68% of the values fall within 1 standard deviation of the mean 95% fall within two standard deviations 99.7 % fall within 3 standard deviations of the mean.

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The Rule

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ANSWER THIS: As a group, the Dutch are the tallest people in the world. The average Dutch man is 184 cm tall – just over 6 ft tall. If a Normal model is appropriate and the standard deviation is 8cm, what percentage of all Dutch men will be over 2 meters (6’6’’) tall?

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Example #2 Suppose that it takes you 20 minutes, on average, to drive to school, with a standard deviation of 2 minutes. Suppose a Normal model is appropriate for the distributions of driving times. How often will you arrive at school in less than 22 minutes? How often will it take you more than 24 minutes? Do you think the distribution of your driving times is unimodal and symmetric? What does this say about the accuracy of your predictions?

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The SAT Reasoning Test has three parts: Writing, Math, Critical Reading (Verbal). Each part has a distribution that is roughly unimodal and symmetric and is designed to have an overall mean of 500 and a standard deviation of 100 for all test takers. In any one year, the mean and standard deviation may differ from these target values by a small amount, but they are good overall approximations. Suppose you earned a 600 on one part of your SAT. Where do you stand among all students who took the test?

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**Suppose you scored a 680 on the test, now where would you stand?**

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**What percentage of people score higher than a 725 on any given section?**

What percentage of people score lower than 300? What percentage score between a 360 and a 510 on any given section?

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Problem #1 Assume the cholesterol levels of Adult American women can be described by a normal model with a mean of 188 mg/dL and a standard deviation of 24. Sketch and label a normal model

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**What percent of adult women do you expect to have cholesterol levels over 200 mg/dL?**

What percent do you expect to have cholesterol levels between 150 and 170 mg/dL? Estimate the IQR of the cholesterol levels. Above what value are the highest 15% of women’s cholesterol levels?

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=188 mg/dL and s = 24. What percent of adult women do you expect to have cholesterol levels over 200 mg/dL?

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