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# The Mighty Central Limit Theorem! - PowerPoint PPT Presentation

Find: a. the frequency distribution (histogram, too, if you’re feelin ’ saucy); b. the average ( ) ; and c. the standard deviation ( ) of the sum of your two tricky dice’s pips. The Mighty Central Limit Theorem!. “Typical” dice. Blue (skewed left) dice.

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## PowerPoint Slideshow about 'The Mighty Central Limit Theorem!' - walden

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Presentation Transcript

• Find:

• a. the frequency distribution (histogram, too, if you’re feelin’ saucy);

• b. the average () ; and

• c. the standard deviation ()

• of the sum of your two tricky dice’s pips.

The Mighty Central Limit Theorem!

• Blue (skewed left) dice

frequency

Sum of pips

frequency

Sum of pips

• Green(symmetric, wide) dice

frequency

Sum of pips

• Orange(bimodal, asymmetric) dice

frequency

Sum of pips

• Red (symmetric, skewed) dice

frequency

Sum of pips

(Quiz (IYL): Sampling Distributions)

Can Excel help here?

The Mighty Central Limit Theorem! the population you’re studying...now, suppose you want to find their

Suppose you want to study a population with an average of  and a standard deviation of  (neither of which you know). So, you take a random sample of size n.

The Mighty Central Limit Theorem! the population you’re studying...now, suppose you want to find their

Suppose you want to study a population with an average of  and a standard deviation of  (neither of which you know). So, you take a random sample of size n.

• 1. Your sample mean will have a normal sampling distribution (yay!) centered on  (yay!)2. This means that your sample mean is an accurate (unbiased) estimator of . In symbols,

The Mighty Central Limit Theorem! the population you’re studying...now, suppose you want to find their

Suppose you want to study a population with an average of  and a standard deviation of  (neither of which you know). So, you take a random sample of size n.

• 2. However, there is still variation in your sample mean (it doesn’t exactly equal ).

The Mighty Central Limit Theorem! the population you’re studying...now, suppose you want to find their

Suppose you want to study a population with an average of  and a standard deviation of  (neither of which you know). So, you take a random sample of size n.

• 3. But, your sample standard deviation s (which many articles call the standard error of the mean, or SE) shrinks as your sample size grows (yay!)3. In fact,

The Mighty Central Limit Theorem! the population you’re studying...now, suppose you want to find their

Suppose you want to study a population with an average of  and a standard deviation of  (neither of which you know). So, you take a random sample of size n.

• 4. This means that, on average,  will be within a “usual” range of values:

The Mighty Central Limit Theorem! the population you’re studying...now, suppose you want to find their

Suppose you want to study a population with an average of  and a standard deviation of  (neither of which you know). So, you take a random sample of size n.

• 5. Since SE is inversely proportional to the sample size, as the sample gets larger, the precision of our interval gets better (yay!)4.

The Mighty Central Limit Theorem! the population you’re studying...now, suppose you want to find their

Suppose you want to study a population with an average of  and a standard deviation of  (neither of which you know). So, you take a random sample of size n.

• 6. No need for huge samples to invoke the CLT…yay5! As n  , the bells become perfect, but an accepted minimum is n = 30, although, as you saw, far fewer can be fine.

• Homework for The Mighty Central

• Limit Theorem!