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STA/MTH 379

STA/MTH 379. Dr. Ananda Bandulasiri Assistant Professor of Statistics SHSU. Chapter 7 Population Distributions. Definitions. A variable associates a value with each individual or object in a population. A variable can be either categorical or numerical .

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STA/MTH 379

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  1. STA/MTH 379 Dr. Ananda Bandulasiri Assistant Professor of Statistics SHSU

  2. Chapter 7Population Distributions

  3. Definitions • A variableassociates a value with each individual or object in a population. A variable can be eithercategoricalornumerical. • The distribution of all values of a numerical variable or all the categories of a categorical variable including probabilities is called apopulationdistribution.

  4. Numerical Variables & Probability • Adiscrete numerical variableis one whose possible values are isolated points along the number line. • Acontinuous numerical variableis one whose possible values form an interval along the number line. • Thepopulation distribution for a discrete numerical variablecan be summarized by arelative frequency histogram. Thepopulation distributionfor acontinuous numerical variableis summarized by adensity histogram.

  5. x 0 1 2 3 4 5 6 p(x) 0.20 0.35 0.15 0.12 0.10 0.05 0.03 Example The number of items a given salesman sells per customer is a variable, say x. The table below applies to a specific salesman (Sam) in a clothing store in the mall. The population distribution of x is given below: Note: 0 ≤p(x) ≤ 1 for each x p(x) = 1 (the sum is over all values of x)

  6. x 0 1 2 3 4 5 6 p(x) 0.20 0.35 0.15 0.12 0.10 0.05 0.03 Example - continued The probability that he sells at least three items to a randomly selected customer is P(X≥ 3) = 0.12 + 0.10 + 0.05 + 0.03 = 0.30 The probability that he sells at most three items to a randomly selected customer is P(X≤ 3) = 0.20 + 0.35 +0.15 + 0.12 = 0.82 The probability that he sells between (inclusive) 2 and 4 items to a randomly selected customer is P(2 ≤X≤ 4) = 0.15 + 0.12 + 0.10 = 0.37

  7. Probability Histogram

  8. Amount measured to nearest hundredths of an ounce. Amount measured to nearest ten thousands of an ounce. Limiting density curve as the measurement accuracy increases. Continuous Probability Distributions If one looks at the distribution of the actual amount of water (in ounces) in “one gallon” bottles of spring water, the following might be seen.

  9. Continuous Probability Distributions • We must usedensity histograms (or density curves)when describingcontinuousvariables.This preserves proportions. • The probability that x takes a value in any interval is the appropriatearea under the density histogram (or density curve).

  10. a a P(x < a) P(x < a) Some Illustrations Notice that for a continuous random variable x, P(x = a) = 0 for any specific value a because the “area above a point” under the density curve is a line segment and hence has 0 area. Specifically, this means P(x < a) = P(x≤a).

  11. a a b b P(a < x < b) P(a < x < b) P(a < x < b) a b Illustrations Note: P(a < x < b) = P(a ≤ x < b) = P(a < x ≤ b) = P(a ≤ x ≤ b)

  12. Population Parameters • The mean value of a numerical variablex, denotedby , describes where the populationdistribution of x iscentered. • The standard deviation of a numerical variable x, denotedby, describes variability in the populationdistribution. When it is close to zero, the values of x tend to be close to the mean. When it is large, there is more variability in the population of x values.

  13. Larger mean Illustrations • Two distributions with thesame standard deviationwithdifferent means.

  14. Smaller standard deviation Illustrations • Two distributions with thesame meananddifferent standard deviations.

  15. Example with Density Distributions • Suppose that we are interested in the weight of Priority Mail Packages so that x = package weight (in pounds) • After careful study, we decide that the following continuous probability distribution provides a good model for the package weights.

  16. y =.5x Density x = weight of package

  17. The probability that a randomly selectedpackage weighs more than 1 poundis the area under the curve to the right of 1. P(x > 1) = 1 - area of small triangle = 1 - .25 = .75

  18. The Normal Distribution • Normaldistributionsare continuous probability distributions that arebell shapedandsymmetric. Normal distributions are referred to as normalcurves. • Normal curvesprovide reasonable approximations to the distributions of many different variables. • Normal curvesplay a central role in many procedures in inferential statistics.

  19. Normal Distributions Two characteristic values (parameters) completely determine a normal distribution Population Mean = µ Population Standard Deviation =   (spread) µ center

  20. Seven Cardinal Numbers Associated with every Normal Distribution.

  21. U.S. ARMY • The U.S. Army reports that the distribution of head circumference among its soldiers is approximately normal with mean m = 22.8 inches and standard deviation s =1.1 inches. • Draw an appropriate Normal curve and mark off theseven cardinal numbersfor this scenario. • In your own words, state precisely what the68-95-99.7 Ruleimplies here.

  22. Major Principle The proportion or percentage of a normally distributed population that is in an interval depends only on how many standard deviations the endpoints are from the mean.

  23. Standard Normal Distribution A normal distribution withmean = 0andstandard deviation = 1, is called the StandardNormal (Z) distribution.

  24. Standard NormalTables

  25. Standard NormalTables

  26. Column labeled 0.06 Row labeled 0.4 P(z < 0.46) = 0.6772 Using the Normal Tables Find P( z < 0.46)

  27. Column labeled 0.04 P(z < -2.74) = 0.0031 Row labeled -2.7 Using the Normal Tables Find P(z < -2.74)

  28. P(z < 1.83) = 0.9664 P(z > 1.83) = 1 – P(z < 1.83) = 1 – 0.9664 = 0.0336 Calculations Using the Standard Normal Distribution Using the standard normal tables, find the proportion of observations (z values) from a standard normal distribution that satisfy each of the following: P( z < 1.83)andP( z > 1.83).

  29. P( z > -1.83) = 1 – P(z < -1.83) = 1 – 0.0336= 0.9664 P( z < -1.83) = 0.0336 Sample Calculations Using the standard normal tables, find the proportion of observations (z values) from a standard normal distribution that satisfies each of the following:P(z < -1.83)andP( z > -1.83)

  30. Symmetry Property From the preceding examples that it becomes obvious that P( z > z*) = P( z < -z*) P(z > -2.18) = P(z < 2.18) = 0.9854

  31. P(Z<2.34)=0.9904 P(Z<-1.37)=0.0853 P(-1.37 < z < 2.34) = 0.9904 - 0.0853 = 0.9051 Sample Calculations Using the Standard Normal Distribution Using the standard normal tables, find the proportion of observations (z values) from a standard normal distribution that satisfies -1.37 < z < 2.34; that is, find P(-1.37 < z < 2.34).

  32. P(Z<1.61)=0.9463 P(Z<.54)=0.7054 P(0.54 < z < 1.61) = 0.9463 - 0.7054 = 0.2409 Sample Calculations Using the Standard Normal Distribution Using the standard normal tables, find the proportion of observations (z values) from a standard normal distribution that satisfies 0.54 < z < 1.61; that is, find P(0.54 < z < 1.61).

  33. P(Z<-0.93)=0.1762 P(Z<-1.42)=0.0778 P(-1.42 < z < -0.93) = 0.1762 - 0.0778 = 0.0984 Sample Calculations Using the Standard Normal Distribution Using the standard normal tables, find the proportion of observations (z values) from a standard normal distribution that satisfy -1.42 < z < -0.93; that is, find P(-1.42 < z < -0.93).

  34. Example Calculation Using the standard normal tables, in each of the following, find the z* values that satisfy : The point z* with 98% of the observations falling below it. The closest entry in the table to 0.9800 is 0.9798 corresponding to a z* value of 2.05. So, P(z* < 2.05) = .9798.

  35. Example Calculation Using the standard normal tables, in each of the following, find the z* values that satisfy : The point z* with 90% of the observations falling above it. The closest entry in the table to 0.1000 is 0.1003 corresponding to a z value of -1.28. So, P( z* > -1.28) = .8997.

  36. Finding Normal Probabilities To calculate probabilities for ANY normal distribution, standardize the relevant values and then use the standard normal table. More specifically, if x is a variable whose behavior is described by a normal distribution with mean m and standard deviation s, (i.e. x ~ N(m , s ), then P(x < b) = p(z < b*) P(x > a) = P(z > a*) where z is a variable whose distribution is standard normal and

  37. If a variable X has a normal distribution with any mean  and any standard deviation , then the standardized variable has the normal distribution with mean 0 and standard deviation 1. Standard Normal Distribution Revisited This is called thestandard normal distribution.

  38. The formula gives the number of standard deviations that x is from the mean, where  is the true population mean and s is the true population standard deviation. Conversion to N(0,1)

  39. Example 1 A Company produces “20 ounce” jars of a picante sauce. The true amounts of sauce in the jars of this brand sauce follow a normal distribution. Suppose the company’s “20 ounce” jars actually follow a N(20.2,0.125) distribution curve. (i.e., The contents of the jars are normally distributed with a true mean= 20.2ounces with a true standard deviation = 0.125 ounces.)

  40. Example 1 • What proportion of the jars are under-filled (i.e., have less than 20 ounces of sauce)? Looking up the z value -1.60 on the standard normal table we find the value 0.0548. The proportion of the sauce jars that are under-filled is 0.0548 (i.e., 5.48% of the jars contain less than 20 ounces of sauce.)

  41. The resulting difference 0.7881-0.0548 = 0.7333 is the proportion of the jars that contain between 20 and 20.3 ounces of sauce. Example 1 What proportion of the sauce jars contain between 20 and 20.3 ounces of sauce. Looking up the z values of -1.60 and 0.80, we find the areas (proportions) 0.0548 and 0.7881.

  42. Example 1 99% of the jars of this brand of picante sauce will contain more than what amount of sauce? When we try to look up 0.0100 in the body of the table we do not find this value. We do find the following The entry closest to 0.0100 is 0.0099 corresponding to the z value -2.33. Since the relationship between the measurement scale (x) and the z scale is z = (x-)/, we solve for x getting x =  + z X = 20.2 + (-2.33)(0.125) = 19.90875 = 19.91

  43. The relationship connecting x, z, and  is . If you solve this equation for x, you get x =  + z , so Example 1 X = 20.2+(-2.33)(0.125)= 19.90875=19.91

  44. Example 2 The weight of the cereal in a box is a normal variable with mean 12.15 ounces, and standard deviation 0.2 ounce. What percentage of the boxes have contents that weigh under 12 ounces?

  45. Example 2 If the manufacturer claims there are 12 ounces in a box, does this percentage cause concern? If so, what could be done to correct the situation? • The machinery could be reset with a higher or larger mean. • The machinery could be replaced with machinery that has a smaller standard deviation of fills. • The label on the box could be changed. Which would probably be cheaper immediately but might cost more in the long run? • In the long run, it might be cheaper to get newer more precise equipment and not give away as much excess cereal.

  46. Homework • Read pp. 308-315. • Work problems 7.24, 7.25, 7.26, 7.27, 7.28 • 7.29, 7.30, 7.31, and 7.32 on pp. 316-317.

  47. Normal Plots A normalprobabilityplot is a scatter plot of the (normal score*, observation) pairs. A substantially linear pattern in a normal probability plot suggests that population normality is plausible. A systematic departure from a straight-line pattern (such as curvature in the plot) casts doubt on the legitimacy of assuming a normal population distribution.

  48. Normal Probability Plot Example Ten randomly selected shut-ins were each asked to list how many hours of television they watched per week. The results are 82 66 90 84 75 88 80 94 110 91 SPSS obtained the normal probability plot on the following slide.

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