Download presentation

Presentation is loading. Please wait.

Published byRicardo Dolliver Modified over 2 years ago

1
**Chapter 8 Estimation Understandable Statistics Ninth Edition**

By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania

2
**Estimating µ When σ is Known**

3
Point Estimate An estimate of a population parameter given by a single number.

4
Margin of Error Even if we take a very large sample size, will differ from µ.

5
Confidence Levels A confidence level, c, is any value between 0 and 1 that corresponds to the area under the standard normal curve between –zc and +zc.

6
Critical Values

7
**Common Confidence Levels**

8
**Recall From Sampling Distributions**

If we take samples of size n from our population, then the distribution of the sample mean has the following characteristics:

10
**A Probability Statement**

In words, c is the probability that the sample mean will differ from the population mean by at most

11
**Maximal Margin of Error**

Since µ is unknown, the margin of error | µ| is unknown. Using confidence level c, we can say that differs from µ by at most:

12
Confidence Intervals

14
**Critical Thinking Since is a random variable, so are the endpoints**

After the confidence interval is numerically fixed for a specific sample, it either does or does not contain µ.

15
Critical Thinking If we repeated the confidence interval process by taking multiple random samples of equal size, some intervals would capture µ and some would not! Equation states that the proportion of all intervals containing µ will be c.

16
**Multiple Confidence Intervals**

17
**Estimating µ When σ is Unknown**

In most cases, researchers will have to estimate σ with s (the standard deviation of the sample). The sampling distribution for will follow a new distribution, the Student’s t distribution.

18
The t Distribution

19
The t Distribution

20
The t Distribution Use Table 6 of Appendix II to find the critical values tc for a confidence level c. The figure to the right is a comparison of two t distributions and the standard normal distribution.

21
**Using Table 6 to Find Critical Values**

Degrees of freedom, df, are the row headings. Confidence levels, c, are the column headings.

22
**Maximal Margin of Error**

If we are using the t distribution:

24
**What Distribution Should We Use?**

25
**Estimating p in the Binomial Distribution**

We will use large-sample methods in which the sample size, n, is fixed. We assume the normal curve is a good approximation to the binomial distribution if both np > 5 and nq = n(1-p) > 5.

26
**Point Estimates in the Binomial Case**

27
Margin of Error The magnitude of the difference between the actual value of p and its estimate is the margin of error.

28
The Distribution of The distribution is well approximated by a normal distribution.

29
**A Probability Statement**

With confidence level c, as before.

31
Public Opinion Polls

32
Choosing Sample Sizes When designing statistical studies, it is good practice to decide in advance: The confidence level The maximal margin of error Then, we can calculate the required minimum sample size to meet these goals.

33
**Sample Size for Estimating μ**

If σ is unknown, use σ from a previous study or conduct a pilot study to obtain s. Always round n up to the next integer!!

34
**Sample Size for Estimating**

If we have no preliminary estimate for p, use the following modification:

35
Independent Samples Two samples are independent if sample data drawn from one population is completely unrelated to the selection of a sample from the other population. Occurs when we draw two random samples

36
Dependent Samples Two samples are dependent if each data value in one sample can be paired with a corresponding value in the other sample. Occur naturally when taking the same measurement twice on one observation Example: your weight before and after the holiday season.

37
**Confidence Intervals for μ1 – μ2 when σ1, σ2 known**

38
**Confidence Intervals for μ1 – μ2 when σ1, σ2 known**

40
**Confidence Intervals for μ1 – μ2 when σ1, σ2 unknown**

If σ1, σ2 are unknown, we use the t distribution (just like the one-sample problem).

42
What if σ1 = σ2 ? If the sample standard deviations s1 and s2 are sufficiently close, then it may be safe to assume that σ1 = σ2. Use a pooled standard deviation. See Section 8.4, problem 27.

44
**Summarizing Intervals for Differences in Population Means**

45
**Estimating the Difference in Proportions**

We consider two independent binomial distributions. For distribution 1 and distribution 2, respectively, we have: n1 p1 q1 r1 n2 p2 q2 r2 We assume that all the following are greater than 5:

46
**Estimating the Difference in Proportions**

48
Critical Thinking

Similar presentations

Presentation is loading. Please wait....

OK

Time for a BREAK! You have 45 Minutes.

Time for a BREAK! You have 45 Minutes.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on surface water contamination Ppt on recycling of wastes Ppt on natural disasters in hindi Ppt on abo blood groupings Ppt on steve jobs biography for children Ppt on sustainable development in india Ppt on indian sugar industry Ppt on website design and development Ppt on circuit breaker testing Ppt on solar power in india