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Power point chapter 2 sections 6 through 9

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Power point chapter 2 sections 6 through 9

1. 1. Chapter 2D E S C R I P T I V E S TA T I S T I C S SECTIONS 6-9
2. 2. 2.6 Percentiles Quartiles are specific examples of percentiles. The first quartile is the same as the 25th percentile and the third quartile is the same as the 75th percentile. The nth percentile represents the value that is greater than or equal to n% of the data.
3. 3.  Jennifer just received the resultsEXAMPLE of her SAT exams. Her SAT Composite of 1710 is at the 73rdConsider each ofthe following percentile. What does this mean?statements aboutpercentiles.  Suppose you received the highest score on an exam. Your friend scored the second-highest score, yet you both were in the 99th percentile. How can this be?
4. 4. Number Frequency RF CRFEXAMPLE of Tickets 0 6 0.08 0.08The following data 1 18 0.24 0.32set shows thenumber of parking 2 12 0.16 0.48tickets received. 3 11 0.15 0.63 4 9 0.12 0.75 5 6 0.08 0.83 6 5 0.07 0.90 7 4 0.05 0.95 8 2 0.03 0.98 9 1 0.01 0.99 10 1 0.01 1
5. 5.  Find and interpret the 90thEXAMPLE percentile.The following dataset shows the  Find and interpret the 20thnumber of parkingtickets received. percentile.  Find the first quartile, the median, and the third quartile.  Construct a box plot.
6. 6. 2.6 IQR and outliers
7. 7. Number Frequency RF CRFEXAMPLE of Tickets 0 6 0.08 0.08The following data 1 18 0.24 0.32set shows thenumber of parking 2 12 0.16 0.48tickets received. 3 11 0.15 0.63 4 9 0.12 0.75 5 6 0.08 0.83 6 5 0.07 0.90 7 4 0.05 0.95 8 2 0.03 0.98 9 1 0.01 0.99 10 1 0.01 1
8. 8. EXAMPLE  Find the inner quartile range of the data set.The following dataset shows thenumber of parkingtickets received.  Do any of the data values appear to be outliers
9. 9. 2.7 Measures of Center
10. 10. EXAMPLEFind the mean 1. 4.5, 10, 1, 1, 9, 14, 4, 8.5, 6, 1, 9median and mode ofthe following dataset.Use technology tofind statisticalinformation.
11. 11. Number Frequency RF CRFEXAMPLE of Tickets 0 6 0.08 0.08The following data 1 18 0.24 0.32set shows thenumber of parking 2 12 0.16 0.48tickets received. 3 11 0.15 0.63Find the mean, 4 9 0.12 0.75median, and mode. 5 6 0.08 0.83Use technology to 6 5 0.07 0.90find statisticalinformation. 7 4 0.05 0.95 8 2 0.03 0.98 9 1 0.01 0.99 10 1 0.01 1
12. 12. 2.9 Measures of Spread The final statistics we would like to be able to find are measures that tell us how spread out the data is about the mean. The two statistics that are most commonly used to measure spread are standard deviation and variation. Standard deviation gives us another way to identify possible outliers: a data value might be an outlier if it is more than two standard deviations from the mean.
13. 13. 2.9 Calculating Standard Deviation and Variance
14. 14. EXAMPLEFind the standard 1. 4.5, 10, 1, 1, 9, 17, 4, 8.5, 5, 1, 9deviation andvariance of the dataset assuming that itis a sample.Use standarddeviation todetermine if anyvalues are possibleoutliers.Use technology tofind statisticalvalues.
15. 15. Number Frequency RF CRFEXAMPLE of Tickets 0 6 0.08 0.08The following data 1 18 0.24 0.32set shows thenumber of parking 2 12 0.16 0.48tickets received. 3 11 0.15 0.63Find the standarddeviation and 4 9 0.12 0.75variance of the data 5 6 0.08 0.83set assuming that itis a sample. 6 5 0.07 0.90Use standard 7 4 0.05 0.95deviation todetermine if any 8 2 0.03 0.98values are possible 9 1 0.01 0.99outliers. 10 1 0.01 1
16. 16. In 2000 the mean age of a sample of femalesExample in the U.S. population was 37.8 years with a standard deviation of 21.8 years and the mean age of a sample of males was 35.3 with a standard deviation of 18.4 years. In relation to the rest of their sex, which is older, a 48 year old woman or a 45 year old man?
17. 17. Characterizing a distribution1. Center, mean/median/mode2. Skew3. Spread
18. 18. Characterizing a Data Distribution
19. 19. Characterizing a Data Distribution
20. 20. Characterizing a Data Distribution
21. 21. Characterizing a Data Distribution
22. 22. Characterizing a Data Distribution
23. 23. Characterizing a Data Distribution
24. 24. Characterizing a Data Distribution
25. 25. Characterizing a Data Distribution
26. 26. Characterizing a Data Distribution
27. 27. Characterizing a Data DistributionExample: For each distribution described below, discuss the number of peaks, symmetry, and amount of variation you would expect to find.- The salaries of actors/actresses.- The number of vacations taken each year.- The weights of calculators stored in the math library – half are graphing calculators and half are scientific calculators.
28. 28. HOMEWORK2.13 #s 4a, b, c, 7, 10, 12, 13a, b, d, e, f, also construct a linegraph for the data from Publisher A and Publisher B, 16a parti and iii, 16b, 21, 29, 30, 31