# AP Statistics Curriculum 2007 Estim S Mean

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==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Estimating a Population Mean: Samll Samples== | ==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Estimating a Population Mean: Samll 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 [[AP_Statistics_Curriculum_2007_Estim_L_Mean |large-samples]]. | |

- | + | ||

- | + | ||

- | === | + | === Point Estimation of a Population Mean=== |

- | + | For any process, the population mean may be estimated by a (large) sample average. That is the sample average <math>\overline{X_n}={1\over n}\sum_{i=1}^n{X_i}</math>, constructed from a random sample of the procees {<math>X_1, X_2, X_3, \cdots , X_n</math>}, is an [http://en.wikipedia.org/wiki/Estimator_bias unbiased] estimate of the population mean <math>\mu</math>, if it exists! Note that the [[AP_Statistics_Curriculum_2007_EDA_Center | sample average may be susceptible to outliers]]. | |

- | + | ===Interval Estimation of a Population Mean=== | |

+ | For large 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> will be | ||

+ | : <math>CI(\alpha): \overline{x} \pm z_{\alpha\over 2} E,</math> | ||

- | = | + | * The '''margin of error''' E is defined as |

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

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

- | * | + | * The '''Standard Error''' of the estimate <math>\overline {x}</math> is obtained by replacing the unknown population standard deviation by the sample standard deviation: |

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

- | + | * <math>z_{\alpha\over 2}</math> is the [[AP_Statistics_Curriculum_2007_Normal_Critical | critical value]] for a [[AP_Statistics_Curriculum_2007_Normal_Std |Standard Normal]] distribution at <math>{\alpha\over 2}</math>. | |

- | * | + | |

- | === | + | ===Example=== |

- | + | Market researchers use the ''number of sentences per advertisement'' as a measure of readability for magazine advertisements. A random sample of the number of sentences found in 30 magazine advertisements is listed. Use this sample to find ''point estimate'' for the population mean <math>\mu</math>. | |

+ | <center> | ||

+ | {| class="wikitable" style="text-align:center; width:75%" border="1" | ||

+ | |- | ||

+ | | 16 || 9 || 14 || 11|| 17 || 12|| 99 || 18 || 13|| 12 || 5 ||9 ||17 || 6 || 11 || 17 || 18 ||20 || 6 ||14|| 7 ||11|| 12 || 5 || 18 || 6 || 4 || 13 || 11 || 12 | ||

+ | |} | ||

+ | </center> | ||

- | + | A ''confidence interval estimate'' of <math>\mu</math> 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. For example if you have a point estimate of 12. 25 with a margin of error of 1.75, then the interval estimate would be (10.5 to 14). | |

- | + | ||

- | + | ||

- | + | ||

- | * | + | 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 distribution]]: | ||

+ | :80% confidence (0.80), <math>\alpha=0.1</math>, z = 1.28 | ||

+ | :90% confidence (0.90), <math>\alpha=0.05</math>, z = 1.645 | ||

+ | :95% confidence (0.95), <math>\alpha=0.025</math>, z = 1.96 | ||

+ | :99% confidence (0.99), <math>\alpha=0.005</math>, z = 2.575 | ||

+ | |||

+ | ====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 <math>\sigma=16</math>). | ||

+ | |||

+ | * For <math>\alpha=0.1</math>, the <math>90% CI(\mu)</math> is constructed by: | ||

+ | <center> <math>\overline{x}\pm 1.28{16\over \sqrt{30}}=14.77 \pm 1.28{16\over \sqrt{30}}=[11.03;18.51]</math></center> | ||

+ | |||

+ | * For <math>\alpha=0.05</math>, the <math>90% CI(\mu)</math> is constructed by: | ||

+ | <center> <math>\overline{x}\pm 1.645{16\over \sqrt{30}}=14.77 \pm 1.645{16\over \sqrt{30}}=[9.96;19.57]</math></center> | ||

+ | |||

+ | * For <math>\alpha=0.005</math>, the <math>90% CI(\mu)</math> is constructed by: | ||

+ | <center> <math>\overline{x}\pm 2.575{16\over \sqrt{30}}=14.77 \pm 2.575{16\over \sqrt{30}}=[7.24;22.29]</math></center> | ||

+ | |||

+ | Notice the increase of the CI's (directly related to the decrease of <math>\alpha</math>) 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 273 as an estimate (so the sample standard deviation is <math>s=\hat{\sigma}=16.54</math>). | ||

+ | |||

+ | * For <math>\alpha=0.1</math>, the <math>90% CI(\mu)</math> is constructed by: | ||

+ | <center> <math>\overline{x}\pm 1.28{16\over \sqrt{30}}=14.77 \pm 1.28{16.54\over \sqrt{30}}=[10.90;18.63]</math></center> | ||

+ | |||

+ | * For <math>\alpha=0.05</math>, the <math>90% CI(\mu)</math> is constructed by: | ||

+ | <center> <math>\overline{x}\pm 1.645{16\over \sqrt{30}}=14.77 \pm 1.645{16.54\over \sqrt{30}}=[9.80;19.73]</math></center> | ||

+ | |||

+ | * For <math>\alpha=0.005</math>, the <math>90% CI(\mu)</math> is constructed by: | ||

+ | <center> <math>\overline{x}\pm 2.575{16\over \sqrt{30}}=14.77 \pm 2.575{16.54\over \sqrt{30}}=[6.99;22.54]</math></center> | ||

+ | |||

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

+ | |||

+ | ===Hands-on activities=== | ||

+ | *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]]. | ||

+ | <center>[[Image:SOCR_EBook_Dinov_Estimates_L_Mean_020208_Fig1.jpg|400px]] | ||

+ | [[Image:SOCR_EBook_Dinov_Estimates_L_Mean_020208_Fig2.jpg|400px]]</center> | ||

<hr> | <hr> | ||

+ | |||

===References=== | ===References=== | ||

- | |||

<hr> | <hr> |

## Revision as of 19:07, 3 February 2008

## Contents |

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

For any process, the population mean may be estimated by a (large) sample average. That is the sample average , constructed from a random sample of the procees {}, is an unbiased estimate of the population mean μ, if it exists! Note that the sample average may be susceptible to outliers.

### Interval Estimation of a Population Mean

For large samples, interval estimation of the population means (or Confidence intervals) are constructed as follows. Choose a confidence level (1 − α)100%, where α is small (e.g., 0.1, 0.05, 0.025, 0.01, 0.001, etc.). Then a (1 − α)100% confidence interval for μ will be

- The
**margin of error**E is defined as

- The
**Standard Error**of the estimate is obtained by replacing the unknown population standard deviation by the sample standard deviation:

- is the critical value for a Standard Normal distribution at .

### Example

Market researchers use the *number of sentences per advertisement* as a measure of readability for magazine advertisements. A random sample of the number of sentences found in 30 magazine advertisements is listed. Use this sample to find *point estimate* for the population mean μ.

16 | 9 | 14 | 11 | 17 | 12 | 99 | 18 | 13 | 12 | 5 | 9 | 17 | 6 | 11 | 17 | 18 | 20 | 6 | 14 | 7 | 11 | 12 | 5 | 18 | 6 | 4 | 13 | 11 | 12 |

A *confidence interval estimate* of μ 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. For example if you have a point estimate of 12. 25 with a margin of error of 1.75, then the interval estimate would be (10.5 to 14).

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), α = 0.1, z = 1.28
- 90% confidence (0.90), α = 0.05, z = 1.645
- 95% confidence (0.95), α = 0.025, z = 1.96
- 99% confidence (0.99), α = 0.005, z = 2.575

#### 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 σ = 16).

- For α = 0.1, the 90%
*C**I*(μ) is constructed by:

- For α = 0.05, the 90%
*C**I*(μ) is constructed by:

- For α = 0.005, the 90%
*C**I*(μ) is constructed by:

Notice the increase of the CI's (directly related to the decrease of α) 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 273 as an estimate (so the sample standard deviation is ).

- For α = 0.1, the 90%
*C**I*(μ) is constructed by:

- For α = 0.05, the 90%
*C**I*(μ) is constructed by:

- For α = 0.005, the 90%
*C**I*(μ) is constructed by:

Notice the increase of the CI's (directly related to the decrease of α) 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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