- 113 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' Large Sample Tests – Non-Normal population' - bardia

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

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

- 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

- 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

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–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

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

- 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

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

Download Presentation

Connecting to Server..