Power point chapter 2 sections 6 through 9
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Power point chapter 2 sections 6 through 9

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Power point chapter 2 sections 6 through 9 Power point chapter 2 sections 6 through 9 Presentation Transcript

  • Chapter 2D E S C R I P T I V E S TA T I S T I C S SECTIONS 6-9
  • 2.6 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.
  •  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?
  • 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
  •  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.
  • 2.6 IQR and outliers
  • 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
  • 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
  • 2.7 Measures of Center
  • 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.
  • 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
  • 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.
  • 2.9 Calculating Standard Deviation and Variance
  • 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.
  • 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
  • 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?
  • Characterizing a distribution1. Center, mean/median/mode2. Skew3. Spread
  • Characterizing a Data Distribution
  • Characterizing a Data Distribution
  • Characterizing a Data Distribution
  • Characterizing a Data Distribution
  • Characterizing a Data Distribution
  • Characterizing a Data Distribution
  • Characterizing a Data Distribution
  • Characterizing a Data Distribution
  • Characterizing a Data Distribution
  • 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.
  • 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