Chapter 23: The Accuracy of Averages What can we say about the - - PowerPoint PPT Presentation

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Chapter 23: The Accuracy of Averages What can we say about the - - PowerPoint PPT Presentation

Chapter 23: The Accuracy of Averages What can we say about the average of the draws? The expected value for the average of the draws is EVave = ave box The standard error for the average of the draws is SEave = SEsum number of draws For a


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Chapter 23: The Accuracy of Averages

What can we say about the average of the draws? The expected value for the average of the draws is EVave = avebox The standard error for the average of the draws is SEave = SEsum number of draws

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For a large number of draws, the average of the draws will follow the normal curve with average EVave and standard deviation SEave. In particular, 68% of the time the average of the draws will be between EVave – SEave and EVave + SEave. 95% of the time the average of the draws will be between EVave – 2 (SEave) and EVave + 2(SEave). We can use the normal curve to find the chance that the average of the draws is in a region of

  • interest. We use EVave and SEave to get standard

units.

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Note:

  • It does not matter what is in the box.
  • The histogram for the tickets in the box does not

have to follow the normal curve.

  • The average of the draws will follow the normal

curve, even if the tickets in the box are 0’s and 1’s!

  • In fact, we don’t even need to know what is in the

box, we just need to know the average and the SD

  • f the box.
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Example 1. HANES women 18-24 have an average height of 64.3” with an SD of 2.6”. Suppose we take a random sample of 100 of these

  • women. What is the expected value of the average height of the

women in the sample? It’s SE?

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As with the percentage, multiplying the number of draws by some number divides the SEave by the square root of that number. e.g. for 100 women, SEave = .26 for 400 women, SEave = .13 for 900 women, SEave = .087

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Example 2. HANES women 18-24 have an average height of 64.3” with an SD of 2.6”. Suppose we take a random sample of 100 of these women. a) What’s the chance the sample average will be more than 64.5”? b) What percentage of the women are taller than 64.5”?

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Example 3. For a certain gas station, the average amount of gas sold is 9.84 gallons, with an SD of 3.92 gallons. If we take a simple random sample of 100 purchases from this gas station, what is the chance that the average amount of gas for these 100 purchases is more than 10 gallons?

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Example 3a. For a large population of fulltime workers, the average income is $32,500 with an SD of $20,000. a) If I take a simple random sample of 400 of these workers, what is the chance the average income for my sample is more than $35,000? b) Do I need to know the incomes follow the normal curve? c) Can we figure out what percentage of the incomes are greater than $35,000?

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The Bootstrap When we do not know what is in the box, we estimate the SD of the box by the SD of the sample. Confidence Intervals A 95% confidence interval for the population average is given by Sample average ± 2(SEave) The confidence interval is valid if the number of draws is large enough.

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Example 4. A lake contains a large number of fish of a particular type. A simple random sample of 300 of these fish gives an average weight

  • f 4.13 pounds with an SD of 2.1 pounds. Find a 95% confidence

interval for the average weight of all the fish in the lake.

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Example 5. A nutrition student takes a simple random sample of 100 people from a large population and carefully monitors their caloric intake for 1 day. The average caloric intake is 2000, with an SD of 400. a) Find a 95% confidence interval for the average caloric intake for the population. b) Is your confidence interval valid if the histogram for caloric intake is not normal?

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Example 6. A university has 12,000 students. A simple random sample

  • f 500 students has average age 22.3 years with an SD of 4.1 years.

Find a 90% confidence interval for the average age of all students at the university.

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Reminder

Normal curve calculations, including confidence intervals, are valid if the number

  • f draws is large enough.

How large is “large enough”? It depends on the box. If the box is a long way from normal (e.g. a box with lots of 0s and very few 1s) then the number of draws needs to be quite large.