Chapter 7 introduction to sampling distributions
This presentation is the property of its rightful owner.
Sponsored Links
1 / 46

Chapter 7 Introduction to Sampling Distributions PowerPoint PPT Presentation


  • 86 Views
  • Uploaded on
  • Presentation posted in: General

Chapter 7 Introduction to Sampling Distributions. Hmm…déjà vu?. Statistic is a numerical descriptive measure of a sample Parameter is a numerical descriptive measure of a population. So…let’s do this. What are the symbols for Statistic mean, variance, standard deviation?. Something new.

Download Presentation

Chapter 7 Introduction to Sampling Distributions

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


Chapter 7 introduction to sampling distributions

Chapter 7 Introduction to Sampling Distributions


Hmm d j vu

Hmm…déjà vu?

  • Statistic is a numerical descriptive measure of a sample

  • Parameter is a numerical descriptive measure of a population


So let s do this

So…let’s do this

  • What are the symbols for Statistic mean, variance, standard deviation?


Something new

Something new

  • Proportion


Chapter 7 introduction to sampling distributions

Note:

  • We are going from raw data distribution to a sampling distribution


Note 2

Note #2:

  • We often do not have access to all the measurements of an entire population because of constraints on time, money, or effort. So we must use measurements from a sample.


Type of inferences

Type of inferences

  • 1) Estimation: In this type of inference, we estimate the value of a population parameter

  • 2) Testing: In this type of inference, we formulate a decision about the value of a population parameter

  • 3) Regression: In this type of inference, we make predictions or forecasts about the value of a statistical variable


Sampling distribution

Sampling distribution

  • It is a probability distribution of a sample statistic based on all possible simple random samples of the same size from the same population


Group work

Group Work:

  • 1) What is population parameter? Give an example

  • 2) What is sample statistic? Give an example

  • 3) What is a sampling distribution?


Answer

Answer

  • 1) Population parameter is a numerical descriptive measure of a population

  • 2) A sample statistic or statistic is a numerical descriptive measure of a sample

  • 3) A sampling distribution is a probability distribution for the sample statistic we are using


Read page 295 298

Read page 295-298


Homework practice

Homework Practice

  • Pg 298-299 #1-9


Central limit theorem

Central Limit Theorem


Central limit theorem1

Central Limit Theorem

  • For a Normal Probability Distribution:

  • Let x be a random variable with a normal distribution whose mean is and whose standard deviation is . Let be the sample mean corresponding to random samples of size n taken from the x distribution. Then the following are true:

    • A) The distribution is a normal distribution

    • B) The mean of the distribution is

    • C) The standard deviation of the distribution is


Chapter 7 introduction to sampling distributions

Note:

  • We conclude from the previous theorem that when x has a normal distribution, the distribution will be normal for any sample size n. Furthermore, we can convert the distribution to the standard normal z distribution by using these formulas:

  • Where n is the sample size

  • is the mean of the distribution, and

  • is the standard deviation of the x distribution


Example

Example

  • Suppose a team of biologist has been studying the height in human. Let x be the height of a single person. The group has determined that x has a normal distribution with mean and standard deviation feet .

  • A) What is the probability that a single person taken at random will be in between 4.7 and 6.5 feet tall?

  • B) What is the probability that the mean length of 5 people taken at random is between 4.7 and 6.5 feet tall?


Answer1

Answer

  • A)

  • )

  • B)=


Group work1

Group Work

  • Suppose a team of feet analysts has been studying the size of man’s foot for particular area. Let x represent the size of the foot. They have determined that the size of the foot has a normal distribution with and standard deviation inches.

  • A) What’s the probability of the foot of a single person taken at random will be in between 6 and 8 inches?

  • B) What’s the probability that the mean size of 8 people taken at random will be in between 7 and 9 inches?


Standard error

Standard error

  • Standard error is the standard deviation of a sampling distribution. For the sampling distribution,

  • Standard error =


Using central limit theorem to convert the distribution to the standard normal distribution

Using Central limit theorem to convert the distribution to the standard normal distribution

  • Where n is the sample size ,

  • is the mean of the x distribution, and

  • is the standard deviation of the x distribution


Group work central limit theorem

Group Work: Central Limit Theorem

  • A) Suppose x has a normal distribution with population mean 18 and standard deviation 3. If you draw random samples of size 5 from the x distribution and x bar represents the sample mean, what can you say about the x bar distribution? How could you standardize the x bar distribution?

  • B) Suppose you know that the x distribution has population mean 75 and standard deviation 12 but you have no info as to whether or not the x distribution is normal. If you draw samples of size 30 from the x distribution and x bar represents sample mean, what can you say about the x bar distribution? How could you standardize the x bar distribution?

  • C) Suppose you didn’t know that x had a normal distribution. Would you be justified in saying that the x bar distribution is approximately normal if the sample size were n=8?


Answer2

Answer

  • A) Since you are given it to be normal, the x bar distribution also will be normal even though sample size is much less than 30.

  • B) Since sample size is large enough, the x bar distribution will be an approximately normal distribution.

  • C) No, sample size is too small. Need to be 30 or more


Chapter 7 introduction to sampling distributions

Note:

  • A sample statistic is unbiased if the mean of its sampling distribution equals the values of the parameter being estimated

  • The spread of the sampling distribution indicates the variability of the statistic. The spread is affected by the sampling method and the sample size. Statistics from larger random samples have spread that are smaller.


Read 304 and 305

Read 304 and 305


Homework practice1

Homework Practice

  • Pg 306-308 #1-18 eoe


Sampling distributions for proportions

Sampling Distributions for Proportions


Think back to section 6 4

Think Back to Section 6.4

  • We dealt with normal approximation to the binomial.

  • How is this related to sampling distribution for proportions?

  • Well in many important situations, we prefer to work with the proportion of successes r/n rather than the actual number of successes r in binomial experiments.


Sampling distribution for the proportion

Sampling distribution for the proportion

  • Given

  • n= number of binomial trials (fixed constant)

  • r= number of successes

  • p=probability of success on each trial

  • q=1-p= probability of failure on each trial

  • If np>5 and nq>5, then the random variable can be approximated by a normal random variable (x) with mean and standard deviation


Standard error1

Standard Error

  • The standard error for the distribution is the standard deviation


Where do these formula comes from

Where do these formula comes from?

  • Remember is as known as expected value or average.

  • So (r is number of success)


How to make continuity corrections to intervals

How to Make continuity corrections to intervals

  • If r/n is the right endpoint of a interval, we add 0.5/n to get the corresponding right endpoint of the x interval

  • If r/n is the left endpoint of a interval, we subtract 0.5/n to get the corresponding left endpoint of the x interval


Example1

Example:

  • Suppose n=30 and we have a interval from 15/30=0.5 and 25/30=.83 Use the continuity correction to convert this interval to an x interval


Answer3

Answer

  • 0.5/30 = 0.02 (approx)

  • So x interval is .5-.02 and .83+.02 which is .48 to .85

  • interval: .5 to .83

  • x interval: .48 to .85


Group work2

Group Work

  • Suppose n=50 and interval is .64 and 1.58. Find the x interval


Word problem

Word Problem

  • Annual cancer rate in L.A. is 209per 1000 people. Suppose we take 40 random people.

  • A) What is the probability p that someone will get cancer and what’s the probability q that they won’t get cancer?

  • B) Do you think we can approximate with a normal distribution? Explain

  • C) What are the mean and standard deviation for ?

  • D) What is the probability that between 10% and 20% of the people will be cancer victim? Interpret the result


Answer4

Answer

  • A)

  • B)

  • Since both are greater than 5, we can approximate with a normal distribution

  • C)

  • D) Since probability is we need to convert into x distribution

  • Continuity correction=0.5/n=0.5/40=0.0125, so we subtract .01 and add .01 from the interval

  • So , then convert this into z value.


Group work3

Group work

  • In the OC, the general ethnic profile is about 47% minority and 53% Caucasian. Suppose a company recently hired 78 people. However, if 25% of the new employees are minorities then there is a problem. What is the probability that at most 25% of the new fires will be minorities if the selection process is unbiased and reflect the ethnic profile? (Follow the last example’s footstep)


Answer5

Answer

  • Since both are greater than 5, so normal approximation is appropriate

  • Continuity correction 0.5/n = 0.5/78 = .006

  • Since it is to the right endpoint, you add .006

  • P(


Remember control chart

Remember Control Chart?

  • Sketch how a control chart looks like


P chart

P-Chart

  • It is just like the control chart


How to make a p chart

How to make a P-Chart?

  • 1. Estimate

  • 2. The center like is assigned to be

  • 3. Control limits are located at and

  • 4. Interpretation: out-of-control signals

    • A) any point beyond a 3 standard deviation level

    • B) 9 consecutive points on one side of the center line

    • C) at least 2 out of 3 consecutive points beyond a 2 standard deviation level

  • Everything is in control if no out-of-control signals occurs


Example situation

Example situation:

  • Civics and Economics is taught in each semester. The course is required to graduate from high school, so it always fills up to its maximum of 60 students. The principal asked the class to provide the control chart for the proportion of A’s given in the course each semester for the past 14 semesters. Make the chart and interpret the result.


Example2

Example:


Answer6

Answer

  • (this is pooled proportion of success)

  • n=60

  • Control Limits are: .077 and .273 (2 standard deviation)

  • .028 and .322 (3 standard deviation)

  • Then graph it! Y-axis is proportion, and x-axis is sample number


Group work4

Group Work

  • Mr. Liu went on a streak of asking 30 women out a month for 11 months. Complete the table and make a control chart for Mr. Liu and interpret his “game”.


Homework practice2

Homework Practice

  • P317-320 #1-13 eoo


  • Login