Large sample tests non normal population
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Large Sample Tests – Non-Normal population. Suppose we have a large sample from a non-normal population and we are interested in conducting a hypotheses test for a single mean. First, we need to assume that all the observations are independent

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Large Sample Tests – Non-Normal population

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Large sample tests non normal population

Large Sample Tests – Non-Normal population

  • Suppose we have a large sample from a non-normal population and

    we are interested in conducting a hypotheses test for a single mean.

  • First, we need to assume that all the observations are independent

    and identically distributed with finite mean and variance.

  • Then we can apply the CLT to the sample mean.

  • The test is conducted using the standard normal (Z) distribution.

  • Note, in this case we do not require σ to be known, since a large

    sample implies that the sample standard deviation s will be close to

    σ for most variables.

week 9


Example

Example

  • Do middle-aged male executives have different average blood

    pressure than the general population? The national center for Health

    Statistics reports that the mean systolic blood pressure for males 35

    to 44 years of age is 128. The medical director of a company looks

    at the medical records of 72 company executives in this age group

    and finds that the mean systolic blood pressure in this sample is

    and the standard deviation is 15. Is there evidence that the executives

    blood pressure differ from the national average?

week 9


Small sample tests for a single mean

Small Sample Tests for a Single Mean

  • Suppose we have a small sample and we are interested in conducting

    a hypotheses test for a single mean.

  • First, we need to assume that all the observations are independent

    and identically normally distributed with unknown finite mean and

    variance.

  • The CLT does not apply to the sample mean.

  • The test is conducted using the t distribution with n-1 degrees of

    freedom.

  • Note, to be confident in our test results we need to check the normality

    assumption.

week 9


Example1

Example

  • In a metropolitan area, the concentration of cadmium (Cd) in leaf

    lettuce was measured in 6 representative gardens where sewage sludge

    was used as fertilizer. The following measurements (in mg/kg of dry

    weight) were obtained.

    Cd 21 38 12 15 14 8

    Is there strong evidence that the mean concentration of Cd is higher than 12.

    Descriptive Statistics

    Variable N Mean Median TrMean StDev SE Mean

    Cd 6 18.00 14.50 18.00 10.68 4.36

  • The hypothesis to be tested are:

week 9


Large sample tests non normal population

  • The test statistics is:

    The degrees of freedom are:

    Conclusions: (Using RR and P-value)

week 9


Large sample tests normal population

Large Sample Tests–Normal population

  • Suppose we have a large sample from a normal population and we

    are interested in conducting a hypotheses test for a single mean.

  • First, we need to assume that all the observations are independent

    and identically normally distributed with unknown finite mean and

    variance.

  • The CLT is not necessary.

  • The test is conducted using the t distribution with n-1 degrees of

    freedom.

  • Note, if n is large the t distribution with n-1 degrees of freedom

    converges to the N(0,1) distribution.

week 9


Example2

Example

  • The GE Light Bulb Company claims that the life of its 2 watt bulbs

    normally distributed with a mean of 1300 hours. Suspecting that the

    claim is too high, Nalph Rader gathered a random sample of 161

    bulbs and tested each. He found the average life to be 1295 hours

    and the standard deviation 20. Test the company's claim using  = 0.01.

week 9


Large sample tests for a binomial proportion

Large Sample Tests for a Binomial Proportion

  • Suppose we have a large sample from a Bernoulli(θ) distribution.

  • That is, we assume that all the observations are independent and

    identically Bernoulli trails.

  • The sample proportion is in fact the sample mean.

  • The CLT applies to the sample proportions.

  • The test is conducted using the standard normal (Z) distribution.

week 9


Example3

Example

  • Statistics Canada records indicate that of all the vehicles undergoing

    emission testing during the previous year, 70% passed on the first

    try. A random sample of 200 cars tested in a particular county

    during the current year yields 124 that passed on the initial test.

    Does this suggest that the true proportion for this county during the

    current year differs from the previous nationwide proportion? Test

    the relevant hypothesis using α = 0.05.

week 9


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