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Calculating Population Parameters versus Sample Statistics

Calculating Population Parameters versus Sample Statistics. Usually only the symbols are different. However, the population variance has a different formula from the sample variance. To calculate the population variance, the population variation is divided by N not (n-1). Populations.

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Calculating Population Parameters versus Sample Statistics

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  1. Calculating Population Parameters versusSample Statistics Usually only the symbols are different. However, the population variance has a different formula from the sample variance. To calculate the population variance, the population variation is divided by N not (n-1).

  2. Populations population parameters  (mu) is the population mean (sigma) is the population standard deviation Random sample sample statistics

  3. If you know what the mean and the standard deviation are for a population, you can answer lots of questions about that population. Two Population Rules: 1) The Empirical Rule and 2) Tchebysheff’s Theorem.

  4. The Empirical Rule If you have a population that is known to be bell-shaped (mound-shaped) and you know both the mean and standard deviation of the population then, Between  1 standard deviation of the mean there is about two-thirds (68%) of the population’s data; Between  2 standard deviations of the mean there is about 95% of the population’s data; Between  3 standard deviations of the mean there is virtually all (100%) of the population’s data.

  5. The Empirical Rule If you have a population that is known to be bell-shaped (mound-shaped) and you know both the mean and standard deviation of the population then, Between  1 standard deviation of the mean there is about two-thirds (68%) of the population’s data; Between  2 standard deviations of the mean there is about 95% of the population’s data; Between  3 standard deviations of the mean there is virtually all (100%) of the population’s data.

  6. The Empirical Rule If you have a population that is known to be bell-shaped (mound-shaped) and you know both the mean and standard deviation of the population then, Between  1 standard deviation of the mean there is about two-thirds (68%) of the population’s data;

  7. The Empirical Rule If you have a population that is known to be bell-shaped (mound-shaped) and you know both the mean and standard deviation of the population then, Between  1 standard deviation of the mean there is about two-thirds (68%) of the population’s data;

  8. The Empirical Rule If you have a population that is known to be bell-shaped (mound-shaped) and you know both the mean and standard deviation of the population then, Between  2 standard deviations of the mean there is about 95% of the population’s data;

  9. The Empirical Rule If you have a population that is known to be bell-shaped (mound-shaped) and you know both the mean and standard deviation of the population then, Between  3 standard deviations of the mean there is virtually all (100%) of the population’s data.

  10. Tchebysheff’s Theorem Not covered in Spring 2014 Regardless of the distribution of the population, (symmetric, skewed or whatever) at least of the data lies within  k standard deviation units of the mean (k > 1).

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