# AP Statistics Curriculum 2007 Estim L Mean

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 Revision as of 18:57, 14 June 2007 (view source)IvoDinov (Talk | contribs)← Older edit Revision as of 03:17, 3 February 2008 (view source)IvoDinov (Talk | contribs) Newer edit → Line 1: Line 1: ==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Estimating a Population Mean: Large Samples== ==[[AP_Statistics_Curriculum_2007 | General Advance-Placement (AP) Statistics Curriculum]] - Estimating a Population Mean: Large Samples== - === Estimating a Population Mean: Large Samples=== + === Point Estimation of a Population Mean=== - Example on how to attach images to Wiki documents in included below (this needs to be replaced by an appropriate figure for this section)! + For any process, the population mean may be estimated by a (large) sample average. That is the smaple average \overline{X_n}={1\over n}\sum_{i=1}^n{X_i}[/itex], constructed from a random sample of the procees {$X_1, X_2, X_3, \cdots , X_n$}, is an [http://en.wikipedia.org/wiki/Estimator_bias unbiased] estimate of the population mean $\mu, if it exists! Note that the [[AP_Statistics_Curriculum_2007_EDA_Center | sample average may be susseptible to outliers]]. - [[Image:AP_Statistics_Curriculum_2007_IntroVar_Dinov_061407_Fig1.png|500px]] + - ===Approach=== + ===Interval Estimation of a Population Mean=== - Models & strategies for solving the problem, data understanding & inference. + For large samples, interval estimation of the population means (or Confidence intervals) are constructed as follows. Choose a confidence level [itex](1-\alpha)100%$, 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 $\mu$ will be + : $CI(\lapha): \overline{x} \pm z_{\alpha\over 2} E,$ - * TBD + * The '''margine of error''' E is defined as + $E = \begin{cases}{\sigma\over\sqrt{n}},& \texttt{for-known}-\sigma,\\ + {SE},& \texttt{for-unknown}-\sigma.\end{cases}$ - ===Model Validation=== + * The '''Standard Error''' of the estimate $\overline {x}$ is defined by $SE(\overline {x}) = {1\over \sqrt{n} \sum_{i=1}^n{(x_i-\overline{x})^2\over n-1} - Checking/affirming underlying assumptions. + - * TBD + * [itex]z_{\alpha\over 2}$ is the [[AP_Statistics_Curriculum_2007_Normal_Critical | critical value]] for a [[AP_Statistics_Curriculum_2007_Normal_Std |Standard Normal]] distribution at ${\alpha\over 2}$. - ===Computational Resources: Internet-based SOCR Tools=== + ===Example=== - * TBD + 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 $\mu$. +
+ {| 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 + |} +
- ===Examples=== + 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.  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). - Computer simulations and real observed data. + - * TBD + Before you find an interval estimate, you should first determine how confident you want to be that your interval estimate contains the population mean. - + - ===Hands-on activities=== + - Step-by-step practice problems. + - * TBD + * [[AP_Statistics_Curriculum_2007_Normal_Critical | Recall these critical values for Standard Normal distribution]]: + :80% confidence (0.80), $\alpha=0.1$, z = 1.28 + :90% confidence (0.90), $\alpha=0.05$, z = 1.645 + :95% confidence (0.95), $\alpha=0.025$, z = 1.96 + :99% confidence (0.99), $\alpha=0.005$, 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 $\sigma=16$). + + * For $\alpha=0.1$, the $90% CI(\mu)$ is constructed by: +
$\overline{x}\pm 1.28{16\over \sqrt{30}}=14.77 \pm 1.28{16\over \sqrt{30}}=[11.03;18.51]$
+ + * For $\alpha=0.05$, the $90% CI(\mu)$ is constructed by: +
$\overline{x}\pm 1.645{16\over \sqrt{30}}=14.77 \pm 1.645{16\over \sqrt{30}}=[9.96;19.57]$
+ + * For $\alpha=0.005$, the $90% CI(\mu)$ is constructed by: +
$\overline{x}\pm 2.575{16\over \sqrt{30}}=14.77 \pm 2.575{16\over \sqrt{30}}=[7.24;22.29]$
+ + 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 273 as an estimate (so the sample standard deviation is $\sigma=16.54$). + + * For $\alpha=0.1$, the $90% CI(\mu)$ is constructed by: +
$\overline{x}\pm 1.28{16\over \sqrt{30}}=14.77 \pm 1.28{16.54\over \sqrt{30}}=[10.90;18.63]$
+ + * For $\alpha=0.05$, the $90% CI(\mu)$ is constructed by: +
$\overline{x}\pm 1.645{16\over \sqrt{30}}=14.77 \pm 1.645{16.54\over \sqrt{30}}=[9.80;19.73]$
+ + * For $\alpha=0.005$, the $90% CI(\mu)$ is constructed by: +
$\overline{x}\pm 2.575{16\over \sqrt{30}}=14.77 \pm 2.575{16.54\over \sqrt{30}}=[6.99;22.54]$
+ + 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_EduMaterials_Activities_CoinfidenceIntervalExperiment | SOCR Confidence Interval Experiment]].

===References=== ===References=== - * TBD

## General Advance-Placement (AP) Statistics Curriculum - Estimating a Population 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 smaple average $\overline{X_n}={1\over n}\sum_{i=1}^n{X_i}$, constructed from a random sample of the procees {$X_1, X_2, X_3, \cdots , X_n$}, is an unbiased estimate of the population mean μ, if it exists! Note that the sample average may be susseptible 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

Failed to parse (unknown function\lapha): CI(\lapha): \overline{x} \pm z_{\alpha\over 2} E,

• The margine 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 defined by Failed to parse (syntax error): SE(\overline {x}) = {1\over \sqrt{n} \sum_{i=1}^n{(x_i-\overline{x})^2\over n-1}

• $z_{\alpha\over 2}$ is the critical value for a Standard Normal distribution at ${\alpha\over 2}$.

### 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%CI(μ) is constructed by:
$\overline{x}\pm 1.28{16\over \sqrt{30}}=14.77 \pm 1.28{16\over \sqrt{30}}=[11.03;18.51]$
• For α = 0.05, the 90%CI(μ) is constructed by:
$\overline{x}\pm 1.645{16\over \sqrt{30}}=14.77 \pm 1.645{16\over \sqrt{30}}=[9.96;19.57]$
• For α = 0.005, the 90%CI(μ) is constructed by:
$\overline{x}\pm 2.575{16\over \sqrt{30}}=14.77 \pm 2.575{16\over \sqrt{30}}=[7.24;22.29]$

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 σ = 16.54).

• For α = 0.1, the 90%CI(μ) is constructed by:
$\overline{x}\pm 1.28{16\over \sqrt{30}}=14.77 \pm 1.28{16.54\over \sqrt{30}}=[10.90;18.63]$
• For α = 0.05, the 90%CI(μ) is constructed by:
$\overline{x}\pm 1.645{16\over \sqrt{30}}=14.77 \pm 1.645{16.54\over \sqrt{30}}=[9.80;19.73]$
• For α = 0.005, the 90%CI(μ) is constructed by:
$\overline{x}\pm 2.575{16\over \sqrt{30}}=14.77 \pm 2.575{16.54\over \sqrt{30}}=[6.99;22.54]$

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