This preview shows pages 1–3. Sign up to view the full content.
136
Instructor's Manual
1
Chapter 7
Statistical Inference and
Sampling
CHAPTER OVERVIEW AND OBJECTIVES
Chapter 6 introduced several continuous probability distributions, including the very important
normal distribution.
In this chapter, the use of the normal distribution will be extended to problems
involving statistical estimation and statistical inference, that is, the estimation of population characteristics
(parameters) on the basis of sample information.
By the end of the chapter, the student should be able to:
1.
Define and distinguish between sample statistics and population parameters.
2.
Discuss the Central Limit Theorem and illustrate its use in statistical inference.
3.
Construct confidence intervals using both the normal distribution and the Student t
distribution.
4.
Describe different aspects of sampling and sampling techniques such as: sampling error,
finite population correction factor, systematic sampling, stratified sampling, and cluster
sampling.
136
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Instructor's Manual
Chapter 7 Glossary
Central Limit Theorem
.
A result that states that for large samples, the distribution of the sample mean, 3,
is approximately normal regardless of the shape of the sampled population.
cluster sample
.
A sample obtained by randomly selecting groups (clusters) of elements from the
population.
confidence interval
.
An interval believed to contain the corresponding population parameter with a
specified level of confidence.
confidence level
.
The confidence associated with the ability of a confidence interval to contain the true
value of the corresponding parameter.
degrees of freedom
.
A value that specifies which t curve (distribution) is being used from the family of t
curves.
When constructing a confidence interval for a population mean using the t distribution, the
degrees of freedom is n  1, where n is the sample size.
inference
.
The process of drawing conclusions about a population based on the results of a statistical
sample.
margin of error (E)
.
The amount that is added to and subtracted from the sample mean when constructing
a confidence interval for the
population mean.
parameter
.
A value that describes the population, such as the population mean (3).
point estimate
.
A single value of a sample statistic used as an estimate of a population parameter.
population
.
The set of all possible measurements of interest.
sampling distribution
.
The probability distribution of a sample statistic.
sample unit
.
A collection of elements (cluster) or an individual element selected from the population to be
included in a sample.
sampling design
.
A plan that specifies the manner in which the sampling units are to be selected for the
sample, such as simple random sampling, systematic sampling, stratified sampling or cluster
sampling.
sampling frame
This is the end of the preview. Sign up
to
access the rest of the document.
 Fall '10
 woolman

Click to edit the document details