Chapter 7 Statistical Inference: Estimating a Population Mean

• View
222

0

Tags:

Embed Size (px)

Text of Chapter 7 Statistical Inference: Estimating a Population Mean

• Chapter 7Statistical Inference: Estimating a Population Mean

• Statistical InferenceStatistical inference is the process of reaching conclusions about characteristics of an entire population using data from a subset, or sample, of that population.

• Simple Random Sampling Simple random sampling is a sampling method which ensures that every combination of n members of the population has an equal chance of being selected.

• Figure 7.1 A Table Of Uniformly Distributed Random Digits

• Sample Data Sample Population Hrs. of Study Member ID Time(x) 1 1687 20.0 2 4138 14.5 3 2511 15.8 4 4198 10.5 5 2006 16.3. . .. . .. . .49 1523 12.6 50 0578 14.0

• Figure 7.2 Bar Chart Showing the Population Study Time Distributionx

• Figure 7.3 Sampling Distribution for for Our Small-Scale Illustration

P( )15.16720.16725.33330.16735.167

• Figure 7.4 Bar Chart Showing the Sampling Distribution of 15 20 25 30 35.167.333P( )Sample Mean Study Time (hrs)

• The Sampling Distribution of the Sample Mean The sampling distribution of the sample mean is the probability distribution of all possible values of the sample mean, , when a sample of size n is taken from a given population.

• Key Sampling Distribution PropertiesFor large enough sample sizes, the shape of the sampling distribution will be approximately normal.The sampling distribution is centered on m, the mean of the population.The standard deviation of the sampling distribution can be computed as the population standard deviation divided by the square root of the sample size.

• Figure 7.5 The Shape of the Sampling Distribution When Sample Size is Large (n > 30)

• Central Limit Theorem As sample size increases, the sampling distribution of the sample mean rapidly approaches the bell shape of a normal distribution, regardless of the shape of the parent population.

• Figure 7.6 Implications of the Central Limit Theoremn = 2n = 5n = 30x

• Small Samples In small sample cases (n
• Figure 7.7 The Center of the Sampling Distribution of the Sample Mean E( ) = m

• Standard Deviation of the (7.1) Sampling Distribution of the Sample Mean

• Figure 7.8 Standard Deviation of the Sampling Distribution of the Sample Meansm

• Figure 7.9 Sampling Distribution of the Sample Mean for Samples of Size n = 2, n = 8, and n = 20 Selected from the Same PopulationPopulation DistributionSampling Distributionn = 2

• Standard Deviation of the Sampling (7.2) Distribution of the Sample Mean (When sample size is a large fraction of the population size)s

• Interval Estimate of (7.3) a Population Mean z

• Factors Influencing Interval Width 1. Confidencethat is, the likelihood that the interval will contain m. A higher confidence level will mean a larger z, which, in turn, will mean a wider interval.

2. Sample size, n. A larger sample size will produce a tighter interval. 3. Variation in the population, as measured by. The greater the variation in the population values, the wider the interval.

• Figure 7.10 Intervals Built Around Various Sample Means from the Sampling Distribution mm

• Figure 7.11 Standard Error vs. Margin of Error + z sMargin of ErrorStandard Error

• Margin of Error The margin of error in an interval estimate of m measures the maximum difference we would expect between the sample mean and the population mean at a given level of confidence.

• Figure 7.12 General Comparison of the t and Normal Distributionst distributionNormal distribution

• Interval Estimate of m (7.4) When s Replaces s

• Figure 7.13 Comparison of the t and Normal Distributions as Degrees of Freedom Increase

• Basic Sample Size Calculator (7.5)

• Sample Size when (7.6) n/N > .05

Recommended

Documents
Documents
Documents
Documents
Documents
Documents
Documents
Documents
Documents
Documents
Data & Analytics
Documents