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Intro to LSP 121

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Title: Intro to LSP 121


1
LSP 121
  • Intro to LSP 121
  • Normal Distributions

2
Welcome to LSP 121
  • Quantitative Reasoning and Technological Literacy
    II
  • Continuation of concepts from LSP 120
  • Topics we feel you will need to make it through
    college and into a career
  • Normal distributions
  • Descriptive statistics and correlation
  • Probability and risk
  • Databases
  • Algorithms
  • If you feel you know this material, take the test
  • See Syllabus under Prerequisites

3
What is a Normal Distribution?
  • Very common, very special type of distribution
  • Most data values are clustered near the mean (a
    single peak)
  • Distribution is symmetric
  • Tapering tales as you move away from the mean
  • Looks like a bell curve

4
The 68-95-99.7 Rule
  • About 68 (68.3), or just over 2/3, of the data
    points fall within 1 standard deviation ( or -)
    of the mean
  • About 95 (95.4) of the data points fall within
    2 standard deviations of the mean
  • About 99.7 of the data points fall within 3
    standard deviations of the mean

5
Pop-Quiz How many percent lie between mean -1
standard deviation and mean 1 standard
deviation? 68 How many percent lie between
mean 1 stdev and mean 3 stdev? 15.85 Ho
w many percent lie greater than mean 3 stdev?
0.15
6
Example
  • In the real world, SAT exams typically produce
    normal distributions with a mean of 500 and a
    standard deviation of 100.
  • Thus, 68 of the students score between 400 and
    600
  • 95 of the students score between 300 and 700
  • 99.7 score between 200 and 800
  • What if someone scored 720 on the SAT? What
    percentage of students scored less than or equal
    to 720?
  • Use Excels NORMDIST function
  • In a cell type NORMDIST(X, mean, stdev, true)
  • For our problem NORMDIST(720, 500, 100, TRUE)
  • Answer 0.986097, or 98.6097
  • What percentage scored greater than 720?

7
Another Example
  • A survey finds that prices paid for two-year-old
    Ford Explorers are normally distributed with a
    mean of 16,500 and a standard deviation of 500.
    Consider a sample of 10,000 people who bought
    two-year-old Ford Explorers.
  • How many people paid between 16,000 and 17,000?
  • NORMDIST(16000,16500,500,true) yields 0.158655
  • NORMDIST(17000, 16500, 500, true) yields
    0.841345
  • Subtract 0.841345 0.158655 yields 0.682689
  • Or use the graph two slides back

8
Another Example
  • How many paid less than 16,000?
  • NORMDIST(16000, 16500, 500, true) yields
    0.158655, or 15.8655
  • Or use the graph
  • What is another way of saying What percentage of
    values are less than or equal to some value X?
    (see next slide)

9
Percentiles
  • The nth percentile of a data set is the smallest
    value in the set with the property that n of the
    data values are less than or equal to it.
  • In a normal distribution, a z score of 0 is the
    mean. At the mean, 50 (or 0.50) of all the
    values are less than or equal to the mean. The
    mean is the 50th percentile.

10
Example
  • Cholesterol levels in men 18 to 24 years of age
    are normally distributed with a mean of 178 and a
    standard deviation of 41.
  • In what percentile is a man with a cholesterol
    level of 190?
  • Using Excels normdist function
  • normdist(190,178,41,true) returns 0.61, or 61st
    percentile

11
Standard Scores
  • The standard score is the number of standard
    deviations a value lies above or below the mean.
  • aka Standard score, z-score, z
  • The standard score of the mean is z0
  • Recall that mean is a better word for average
  • Example The standard score of a data value 1.5
    standard deviations above the mean is z1.5
  • Example What is the standard score for a student
    who scores 300 on an exam with a mean of 400,
    standard deviation of 100?
  • This student scored exactly 1 SD below the mean,
    so z -1

12
Standard Scores
  • The standard score of a data value 2.4 standard
    deviations below the mean is z -2.4
  • In general
  • z (data value mean) / standard deviation
  • the data value is typically called x

13
Example
  • The Stanford-Binet IQ test is designed so that
    scores are normally distributed with a mean of
    100 and a standard deviation of 16. What are the
    z-scores for IQ scores of 95 and 125?
  • z (95 - 100) / 16 -0.31
  • z (125 - 100) / 16 1.56 Thus, an IQ score of
    125 lies 1.56 standard deviations above the mean.

14
Inverse Normal Distribution Function
  • What if you know the mean, standard deviation,
    and percentile, and want to know the actual value
    (X)?
  • Recall z (x mean) / standard deviation
  • You can also use Excels NORMINV
  • Know how to use BOTH. On an exam, youll use the
    formula.
  • Example If a set of exam scores has a mean of
    76, a standard deviation of 12, and one score is
    at the 86th percentile, what was the students
    exact numeric score?
  • Answer x 88.9
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