# Difference between revisions of "AP Statistics Curriculum 2007 Estim S Mean"

(→Interval Estimation of a Population Mean) |
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===Interval Estimation of a Population Mean=== | ===Interval Estimation of a Population Mean=== | ||

− | For small samples, interval estimation of the population means (or '''Confidence | + | For small samples, interval estimation of the population means (or '''Confidence Intervals''') are constructed as follows. Choose a confidence level <math>(1-\alpha)100%</math>, where <math>\alpha</math> is small (e.g., 0.1, 0.05, 0.025, 0.01, 0.001, etc.). Then a <math>(1-\alpha)100%</math> confidence interval for <math>\mu</math> is defined in terms of the T-distribution: |

: <math>CI(\alpha): \overline{x} \pm t_{\alpha\over 2} E.</math> | : <math>CI(\alpha): \overline{x} \pm t_{\alpha\over 2} E.</math> | ||

− | * The ''' | + | * The '''Margin of Error''' E is defined as |

<math>E = \begin{cases}{\sigma\over\sqrt{n}},& \texttt{for-known}-\sigma,\\ | <math>E = \begin{cases}{\sigma\over\sqrt{n}},& \texttt{for-known}-\sigma,\\ | ||

{SE},& \texttt{for-unknown}-\sigma.\end{cases}</math> | {SE},& \texttt{for-unknown}-\sigma.\end{cases}</math> | ||

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<math>SE(\overline {x}) = {1\over \sqrt{n}} \sqrt{\sum_{i=1}^n{(x_i-\overline{x})^2\over n-1}}</math> | <math>SE(\overline {x}) = {1\over \sqrt{n}} \sqrt{\sum_{i=1}^n{(x_i-\overline{x})^2\over n-1}}</math> | ||

− | * <math>t_{\alpha\over 2}</math> is the [[AP_Statistics_Curriculum_2007_StudentsT | | + | * <math>t_{\alpha\over 2}</math> is the [[AP_Statistics_Curriculum_2007_StudentsT |Critical Value for the T(df=sample-size -1) distribution at <math>{\alpha\over 2}</math>]]. |

===Example=== | ===Example=== | ||

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Before you find an interval estimate, you should first determine how confident you want to be that your interval estimate contains the population mean. | Before you find an interval estimate, you should first determine how confident you want to be that your interval estimate contains the population mean. | ||

− | * [[AP_Statistics_Curriculum_2007_Normal_Critical | Recall these critical values for Standard Normal | + | * [[AP_Statistics_Curriculum_2007_Normal_Critical | Recall these critical values for Standard Normal Distribution]]: |

:80% confidence (0.80), <math>\alpha=0.1</math>, [http://socr.ucla.edu/Applets.dir/T-table.html t<sub>(df=9)</sub> = 1.383] | :80% confidence (0.80), <math>\alpha=0.1</math>, [http://socr.ucla.edu/Applets.dir/T-table.html t<sub>(df=9)</sub> = 1.383] | ||

:90% confidence (0.90), <math>\alpha=0.05</math>, [http://socr.ucla.edu/Applets.dir/T-table.html t<sub>(df=9)</sub> = 1.833] | :90% confidence (0.90), <math>\alpha=0.05</math>, [http://socr.ucla.edu/Applets.dir/T-table.html t<sub>(df=9)</sub> = 1.833] | ||

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Notice the increase of the CI's (directly related to the decrease of <math>\alpha</math>) reflecting our choice for higher confidence. | Notice the increase of the CI's (directly related to the decrease of <math>\alpha</math>) reflecting our choice for higher confidence. | ||

− | ====Unknown | + | ====Unknown Variance==== |

Suppose that we do '''not''' know the variance for the ''number of sentences per advertisement'' but use the sample variance 737.88 as an estimate (so the sample standard deviation is <math>s=\hat{\sigma}=27.16390579</math>). | Suppose that we do '''not''' know the variance for the ''number of sentences per advertisement'' but use the sample variance 737.88 as an estimate (so the sample standard deviation is <math>s=\hat{\sigma}=27.16390579</math>). | ||

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Notice the increase of the CI's (directly related to the decrease of <math>\alpha</math>) reflecting our choice for higher confidence. | |||

− | ===Hands-on | + | ===Hands-on Activities=== |

*See the [[SOCR_EduMaterials_Activities_CoinfidenceIntervalExperiment | SOCR Confidence Interval Experiment]]. | *See the [[SOCR_EduMaterials_Activities_CoinfidenceIntervalExperiment | SOCR Confidence Interval Experiment]]. | ||

* Sample statistics, like the sample-mean and the sample-variance, may be easily obtained using [http://socr.ucla.edu/htmls/SOCR_Charts.html SOCR Charts]. The images below illustrate this functionality (based on the '''Bar-Chart''' and '''Index-Chart''') using the 30 observations of the number of sentences per advertisement, [[AP_Statistics_Curriculum_2007_Estim_L_Mean#Example | reported above]]. | * Sample statistics, like the sample-mean and the sample-variance, may be easily obtained using [http://socr.ucla.edu/htmls/SOCR_Charts.html SOCR Charts]. The images below illustrate this functionality (based on the '''Bar-Chart''' and '''Index-Chart''') using the 30 observations of the number of sentences per advertisement, [[AP_Statistics_Curriculum_2007_Estim_L_Mean#Example | reported above]]. |

## Revision as of 18:25, 1 March 2008

## Contents

## General Advance-Placement (AP) Statistics Curriculum - Estimating a Population Mean: Small Samples

Now we discuss estimation when the sample-sizes are small, typically < 30 observations. Naturally, the point estimates are less precise and the interval estimates produce wider intervals, compared to the case of large-samples.

### Point Estimation of a Population Mean

The population mean may also be estimated by the sample average using a small sample. That is the sample average \(\overline{X_n}={1\over n}\sum_{i=1}^n{X_i}\), constructed from a random sample of the process {\(X_1, X_2, X_3, \cdots , X_n\)}, is an unbiased estimate of the population mean \(\mu\), if it exists! Note that the sample average may be susceptible to outliers.

### Interval Estimation of a Population Mean

For small samples, interval estimation of the population means (or **Confidence Intervals**) are constructed as follows. Choose a confidence level \((1-\alpha)100%\), where \(\alpha\) is small (e.g., 0.1, 0.05, 0.025, 0.01, 0.001, etc.). Then a \((1-\alpha)100%\) confidence interval for \(\mu\) is defined in terms of the T-distribution:
\[CI(\alpha): \overline{x} \pm t_{\alpha\over 2} E.\]

- The
**Margin of Error**E is defined as

\(E = \begin{cases}{\sigma\over\sqrt{n}},& \texttt{for-known}-\sigma,\\ {SE},& \texttt{for-unknown}-\sigma.\end{cases}\)

- The
**Standard Error**of the estimate \(\overline {x}\) is obtained by replacing the unknown population standard deviation by the sample standard deviation\[SE(\overline {x}) = {1\over \sqrt{n}} \sqrt{\sum_{i=1}^n{(x_i-\overline{x})^2\over n-1}}\]

- \(t_{\alpha\over 2}\) is the Critical Value for the T(df=sample-size -1) distribution at \({\alpha\over 2}\).

### Example

To parallel the example in the large sample case, we consider again the *number of sentences per advertisement* as a measure of readability for magazine advertisements. A random sample of the number of sentences found in 10 magazine advertisements is listed. Use this sample to find *point estimate* for the population mean \(\mu\) (**sample-mean=22.1, sample-variance=737.88 and sample-SD=27.16390579**).

16 | 9 | 14 | 11 | 17 | 12 | 99 | 18 | 13 | 12 |

A *confidence interval estimate* of \(\mu\) is a range of values used to estimate a population parameter (interval estimates are normally used more than point estimates because it is very unlikely that the sample mean would match exactly with the population mean) The interval estimate uses a margin of error about the point estimate.

Before you find an interval estimate, you should first determine how confident you want to be that your interval estimate contains the population mean.

- 80% confidence (0.80), \(\alpha=0.1\), t
_{(df=9)}= 1.383 - 90% confidence (0.90), \(\alpha=0.05\), t
_{(df=9)}= 1.833 - 95% confidence (0.95), \(\alpha=0.025\), t
_{(df=9)}= 2.262 - 99% confidence (0.99), \(\alpha=0.005\), t
_{(df=9)}= 3.250

Notice that for a fixed \(\alpha\), the *t-critical values* (for any degree-of-freedom) exceeds the corresponding normal z-critical values, which are used in the large-sample interval estimation.

#### Known Variance

Suppose that we know the variance for the *number of sentences per advertisement* example above is known to be 256 (so the population standard deviation is \(\sigma=16\)).

- For \(\alpha=0.1\), the \(80% CI(\mu)\) is constructed by:

- For \(\alpha=0.05\), the \(90% CI(\mu)\) is constructed by:

- For \(\alpha=0.005\), the \(99% CI(\mu)\) is constructed by:

Notice the increase of the CI's (directly related to the decrease of \(\alpha\)) reflecting our choice for higher confidence.

#### Unknown Variance

Suppose that we do **not** know the variance for the *number of sentences per advertisement* but use the sample variance 737.88 as an estimate (so the sample standard deviation is \(s=\hat{\sigma}=27.16390579\)).

- For \(\alpha=0.1\), the \(80% CI(\mu)\) is constructed by:

- For \(\alpha=0.05\), the \(90% CI(\mu)\) is constructed by:

- For \(\alpha=0.005\), the \(99% CI(\mu)\) is constructed by:

Notice the increase of the CI's (directly related to the decrease of \(\alpha\)) reflecting our choice for higher confidence.

### Hands-on Activities

- See the SOCR Confidence Interval Experiment.
- Sample statistics, like the sample-mean and the sample-variance, may be easily obtained using SOCR Charts. The images below illustrate this functionality (based on the
**Bar-Chart**and**Index-Chart**) using the 30 observations of the number of sentences per advertisement, reported above.

### References

- SOCR Home page: http://www.socr.ucla.edu

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