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Published byRaymond Miller Modified over 4 years ago

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**Objectives 1.2 Describing distributions with numbers**

Measures of center: mean, median Mean versus median Measures of spread: quartiles, standard deviation Five-number summary and boxplot Choosing among summary statistics Changing the unit of measurement

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**Numerical descriptions of distributions**

Describe the shape, center, and spread of a distribution… Center: mean, median and mode. Spread: range, IQR, standard deviation (SD). We treat these as aids to understanding the distribution of the variable at hand… The mean is often called the "average" and is in fact the arithmetic average ("add all the values and divide by the number of observations").

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**Measure of center: sample mean: Example 1**

The mean or arithmetic average To calculate the average, or mean, add all values, then divide by the number of individuals. It is the “center of mass.” Sum of heights is 301.2 divided by 5 women = 301.2/5=60.24 inches Most of you know what a mean, or common arithmetic average is. You should know how to calculate the mean both by hand and using your calculator. See Dr. Baldi.

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**Measure of center: sample mean: Example 2**

w o ma n ( i ) h ei gh t x = 1 5 8 . 2 14 6 4 9 15 3 7 16 17 18 19 20 21 22 10 23 11 24 12 25 13 S Mathematical notation: (Sample mean) There is some standard math notation for referring to the mean and the numbers used to calculate it. We number the individuals using the letter I, here I goes from 1 to 25. The total number is n, or 25 We refer to the variable height, associated with each individual, using x. Doesn’t have to be I or x, but usually is. I will always make it clear to you what is what, as in column headings here. The x’s get numbered to match the individual. The mean, x BAR, is the sum of the individual heights, or the x sub I’s, divided by the total Number of individuals n. A shorthand way to write the same equation is below, where the summation symbol means to sum the x values, or heights, as I goes from 1 to n. Mean height is about 5’4” Learn right away how to get the mean using your calculators.

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**Your numerical summary must be meaningful!**

Height of 25 women in a class The distribution of women’s heights appears coherent and symmetrical. The mean is a good numerical summary.

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The Median (M) is often called the "middle" value and is the value at the midpoint of the observations when they are ranked from smallest to largest value…. Steps to get median: arrange the data from smallest to largest if n is odd then the median is the single observation in the center (at the (n+1)/2 position in the ordering) if n is even then the median is the average of the two middle observations (at the (n+1)/2 position; i.e., in between…) E.g1: 5, 1, 7, 4, 3 E.g2: 5, 1, 7, 4, 3, 8

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**Measure of center: the median**

Note: for a median, 50% of the data are less than it and 50% of the data are bigger than it Example1: with the data listed below, what are the mean and median? 2, 3, 5, 1. Example2: with the data listed below, what are the mean and median? 2, 3, 5, 1, 100. Example3: with the data listed below, what are the mean and median? -100, 2, 3, 5, 1, 100. Question: What can we conclude from the examples above? Mean is sensitive to outliers; Median is robust to outliers.

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**Measure of center: the median**

The median is the midpoint of a distribution—the number such that half of the observations are smaller and half are larger. 1. Sort observations by size. n = number of observations ______________________________ 2.a. If n is odd, the median is observation (n+1)/2 down the list n = 25 (n+1)/2 = 26/2 = 13 Median = 3.4 2.b. If n is even, the median is the mean of the two middle observations. n = 24 n/2 = 12 Median = ( ) /2 = 3.35

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**Mean and median of a distribution with outliers**

Without the outliers With the outliers Percent of people dying The median, on the other hand, is only slightly pulled to the right by the outliers (from 3.4 to 3.6). The mean is pulled to the right a lot by the outliers (from 3.4 to 4.2).

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**Impact of skewed data Mean and median of a symmetric Disease X:**

Mean and median are the same. Mean and median of a symmetric Multiple myeloma: … and a right-skewed distribution The mean is pulled toward the skew.

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We can describe the shape, center and spread of a density curve in the same way we describe data… e.g., the median of a density curve is the “equal-areas” point - the point on the horizontal axis that divides the area under the density curve into two equal (.5 each) parts. The mean of the density curve is the balance point - the point on the horizontal axis where the curve would balance if it were made of a solid material. (See figures 1.24b and 1.25 below)

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**The mean is pulled toward the skew.**

Skewness: The mean is pulled toward the skew. The mean is pulled toward the skew. Mode = Mean = Median SYMMETRIC Mean Mode Mode Mean Median Median SKEWED LEFT (negatively) SKEWED RIGHT (positively)

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**Measure of spread: the quartiles**

Spread: percentiles, quartiles (Q1 and Q3), IQR, 5-number summary (and boxplots), range, standard deviation pth percentile of a variable is a data value such that p% of the values of the variable fall at or below it. The lower (Q1) and upper (Q3) quartiles are special percentiles dividing the data into quarters (fourths). get them by finding the medians of the lower and upper halves of the data IQR = interquartile range = Q3 - Q1 = spread of the middle 50% of the data. IQR is used with the so-called 1.5*IQR criterion for outliers - know this!

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**Examples to find 5-# summary and Boxplot**

Eg1: Dataset: 3, 2, 1, 5, 6. Find the Median, Q1, Q3 and IQR. Find the 5-# summary. Draw a Boxplot for Eg1. Eg2: Dataset: 3, 2, 1, 5, 6, 8. Find the Median, Q1, Q3 and IQR. Find the 5-# summary. Draw a Boxplot for Eg1.

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**Measure of spread: the quartiles**

Definition, pg 35 Introduction to the Practice of Statistics, Sixth Edition © 2009 W.H. Freeman and Company

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**Measure of spread: the quartiles**

The first quartile, Q1, is the value in the sample that has 25% of the data at or below it ( it is the median of the lower half of the sorted data, excluding M). The third quartile, Q3, is the value in the sample that has 75% of the data at or below it ( it is the median of the upper half of the sorted data, excluding M). Q1= first quartile = 2.2 M = median = 3.4 Q3= third quartile = 4.35

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**Definition, pg 37 Definition, pg 38a © 2009 W.H. Freeman and Company**

Introduction to the Practice of Statistics, Sixth Edition © 2009 W.H. Freeman and Company Definition, pg 38a Introduction to the Practice of Statistics, Sixth Edition © 2009 W.H. Freeman and Company

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**Five-number summary and boxplot**

Largest = max = 6.1 BOXPLOT Q3= third quartile = 4.35 M = median = 3.4 Q1= first quartile = 2.2 Five-number summary: min Q1 M Q3 max Smallest = min = 0.6

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**Boxplots for skewed data**

Comparing box plots for a normal and a right-skewed distribution Boxplots remain true to the data and depict clearly symmetry or skew.

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**5-number summary: min. , Q1, median, Q3, max**

when plotted, the 5-number summary is a boxplot we can also do a modified boxplot to show outliers (mild and extreme). Boxplots have less detail than histograms and are often used for comparing distributions… e.g., Fig. 1.17, p.47 and below...

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**Suspected outliers: how to detect outliers**

Outliers are troublesome data points, and it is important to be able to identify them. One way to raise the flag for a suspected outlier is to compare the distance from the suspicious data point to the nearest quartile (Q1 or Q3). We then compare this distance to the interquartile range (distance between Q1 and Q3). We call an observation a suspected outlier if it falls more than 1.5 times the size of the interquartile range (IQR) above the first quartile or below the third quartile. This is called the “1.5 * IQR rule for outliers.” Add in a one other thing we know - the spread - the largest and smallest values, and make a box plot. Now, why would you want to make one of these?

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**Modified Boxplot Modified boxplot (helps detect outliers)**

Calculate 1.5*IQR Q1 – 1.5*IQR Q3+1.5*IQR Draw box and line (similar to before). Draw whiskers to minimum and maximum observation within (Q1 – 1.5*IQR, Q3+1.5*IQR). Observations outside this range should be plotted as dots separately.

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**Q1: Is there any suspected outliers? **

Modified Boxplot Q1: Is there any suspected outliers? Q2: If yes, then find the following values: Calculate 1.5*IQR; Lower bound = Q1 – 1.5*IQR; Upper bound = Q3+1.5*IQR; Find Min*=min within lower/upper bounds; Find Max*=max within lower/upper bounds; Q3: Can we verify any outliers? Q4: Now draw the Modified Boxplot: Draw Min* and Max*, Q1, Med, Q3. For all observations outside this range should be plotted as dots separately. Q3 = 4.35 Q1 = 2.2

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**Modified Boxplot Distance to Q3 7.9 − 4.35 = 3.55 Interquartile range**

8 Distance to Q3 7.9 − 4.35 = 3.55 Q3 = 4.35 Interquartile range Q3 – Q1 4.35 − 2.2 = 2.15 Q1 = 2.2 Individual #25 has a value of 7.9 years, which is 3.55 years above the third quartile. This is more than years, 1.5 * IQR. Thus, individual #25 is an outlier by our 1.5 * IQR rule.

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**Measure of spread: the standard deviation**

The standard deviation “s” is used to describe the variation around the mean. Like the mean, it is not resistant to skew or outliers. 1. First calculate the variance s2. 2. Then take the square root to get the standard deviation s. Boxplots are used to show the spread around a median - can use no matter what the distribution, and is a good way to contrast variables having different distributions. But if your distribution is symmetrical, you can use the mean as the center of your distribution, you can use a different (and more common) measure of spread around the mean - standard deviation. The Standard Deviation measures spread by looking at how far the observations are from their mean. Go through calc. This is women’s height data again, First, N is again the number of observations. From this we calculate the degrees of freedom, which is just n-1. Come back to this in a second. Take difference from mean, square it so all are positive, add them up. Then divide not by number of observations by by n-1 = df Although variance is a useful measure of spread, it’s units are units squared. So we like to take the square root and use that number, the SD, which has the same units as the mean. Height squared is not intuitive. Now, as to why dividing by n-1 instead of n. When we got the mean it was easy to imagine why we divided by N intuitively. But actually, what we are doing even there is dividing by the number of independent pieces of information that go into the estimate of a parameter. This number is called the degrees of freedom (df, and it is equal to the number of independent scores that go into the estimate minus the number of parameters estimated as intermediate steps in the estimation of the parameter itself. For example, if the variance, s2 , is to be estimated from a random sample of N independent scores, then the degrees of freedom is equal to the number of independent scores (N) minus the number of parameters estimated as intermediate steps (here, we have estimated the mean) and is therefore equal to N-1. But why the term “degrees of freedom”? When we calculate the s-square of a random sample, we must first calculate the mean of that sample and then compute the sum of the several squared deviations from that mean. While there will be n such squared deviations only (n - 1) of them are, in fact, free to assume any value whatsoever. This is because the final squared deviation from the mean must include the one value of X such that the sum of all the Xs divided by n will equal the obtained mean of the sample. All of the other (n - 1) squared deviations from the mean can, theoretically, have any values whatsoever. For these reasons, the statistic s-square is said to have only (n - 1) degrees of freedom. I know this is hard to understand. I don’t expect you to understand it completely. But in a second I will come back to it to show you the effect of dividing by n-1 rather than n, and perhaps that will make is easier to accept. Mean ± 1 s.d.

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**Example 1: to calculate sample SD**

Calculations … For data: 1, 2, 3, 4, 5. Q: Find the sample variance and sample SD. 1 Order i Mean = 3 Sum of squared deviations from mean = 10 Degrees freedom (df) = (n − 1) = 4 s2 = sample variance = 10/4 = 2.5 s = sample standard deviation = √2.5 = 1.58 Make sure to know how to get the standard deviation using your calculator.

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**Example 2: Use hand to calculate sample SD for the following data set: 3, 4, 5, 8.**

1. First calculate the variance s2. 2. Then take the square root to get the standard deviation s. Make sure to know how to get the standard deviation using your calculator.

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**How to use calculator to find statistics…**

In order to find sample mean, sample SD, and 5-# summary, we can use calculator to help as following: Stat Edit choose 1: Edit… input your data into L1; Stat Calc choose 1: 1-Var Stats Enter Enter. Read your outputs carefully. Note: X-bar means sample mean; Sx means sample SD; n means sample size. Q: find the sample mean, sample SD, and 5-# summary for the following data: Example1: Data are: 3, 4, 5, 8. Example 2: Data are: 1, 3, 5, 6, 7, 8.

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**Definition, pg 43a © 2009 W.H. Freeman and Company**

Introduction to the Practice of Statistics, Sixth Edition © 2009 W.H. Freeman and Company

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**How to perform data analysis:**

ALWAYS PLOT DATA BEFORE DECIDING ON A NUMERICAL SUMMARY. How to choose summary statistics? Use: 5-number summary is better than the mean and s.d. for skewed data; Use mean & s.d. for symmetric data.

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