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1 Chapter Seven Introduction to Sampling Distributions Section 3 Sampling Distributions for Proportions
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2 Key Points 7.3 Compute the mean and standard deviation for the proportion p hat = r/n Use the normal approximation to compute probabilities for proportions p hat = r/n Construct P-charts and interpret what they tell you
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3 Sampling Distributions for Proportions Allow us to work with the proportion of successes rather than the actual number of successes in binomial experiments.
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4 Sampling Distribution of the Proportion n= number of binomial trials r = number of successes p = probability of success on each trial q = 1 - p = probability of failure on each trial
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5 Sampling Distribution of the Proportion If np > 5 and nq > 5 then p-hat = r/n can be approximated by a normal random variable (x) with:
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6 The Standard Error for
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7 Continuity Correction When using the normal distribution (which is continuous) to approximate p- hat, a discrete distribution, always use the continuity correction. Add or subtract 0.5/n to the endpoints of a (discrete) p-hat interval to convert it to a (continuous) normal interval.
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8 Continuity Correction If n = 20, convert a p- hat interval from 5/8 to 6/8 to a normal interval. Note: 5/8 = 0.625 6/8 = 0.75 So p-hat interval is 0.625 to 0.75. Since n = 20,.5/n = 0.025 5/8 - 0.025 = 0.6 6/8 + 0.025 = 0.775 Required x interval is 0.6 to 0.775
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9 Suppose 12% of the population is in favor of a new park. Two hundred citizen are surveyed. What is the probability that between 10 % and 15% of them will be in favor of the new park?
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10 12% of the population is in favor of a new park. p = 0.12, q= 0.88 Two hundred citizen are surveyed. n = 200 Both np and nq are greater than five. Is it appropriate to the normal distribution?
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11 Find the mean and the standard deviation
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12 What is the probability that between 10 % and 15%of them will be in favor of the new park? Use the continuity correction Since n = 200,.5/n =.0025 The interval for p-hat (0.10 to 0.15) converts to 0.0975 to 0.1525.
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13 Calculate z-score for x = 0.0975
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14 Calculate z-score for x = 0.1525
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15 P(-0.98 < z < 1.41) 0.9207 -- 0.1635 = 0.7572 There is about a 75.7% chance that between 10% and 15% of the citizens surveyed will be in favor of the park.
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16 Control Chart for Proportions P-Chart
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17 Constructing a P-Chart Select samples of fixed size n at regular intervals. Count the number of successes r from the n trials. Use the normal approximation for r/n to plot control limits. Interpret results.
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18 Determining Control Limits for a P-Chart Suppose employee absences are to be plotted. In a daily sample of 50 employees, the number of employees absent is recorded. p/n for each day = number absent/50.For the random variable p-hat = p/n, we can find the mean and the standard deviation.
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19 Finding the mean and the standard deviation
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20 Is it appropriate to use the normal distribution? The mean of p-hat = p = 0.12 The value of n = 50. The value of q = 1 - p = 0.88. Both np and nq are greater than five. The normal distribution will be a good approximation of the p-hat distribution.
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21 Control Limits Control limits are placed at two and three standard deviations above and below the mean.
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22 Control Limits The center line is at 0.12. Control limits are placed at -0.018, 0.028, 0.212, and 0.258.
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23 Control Chart for Proportions Employee Absences 0.3 +3s = 0.258 0.2+2s = 0.212 0.1 mean = 0.12 0.0 -2s = 0.028 -0.1 -3s = -0.018
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24 Daily absences can now be plotted and evaluated. Employee Absences 0.3 +3s = 0.258 0.2+2s = 0.212 0.1 mean = 0.12 0.0 -2s = 0.028 -0.1 -3s = -0.018
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25 Calculator – Chapter 7 In this chapter use the TI-83 or TI-84 Plus graphing calculator to do any computations with formulas from the chapter. For example, computing the z score corresponding to a raw score from an x bar distribution.
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26 Calculator – Chapter 7 Example: If a random sample of size 40 is taken from a distribution with mean = 10 and standard deviation = 2, find the z score corresponding to x=9 We use the z formula: A Calculator is used to compute The result rounds to z= -3.16
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33 Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital. ~Aaron Levenstein
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