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# Elem Stats ch 7

### A Brief Version: Elementary Statistics Ch 7

Question | Answer |
---|---|

Point Estimate | A specific value estimate of a parameter. The best point estimate of the population mean (mu) is the sample mean (X bar). |

Estimators | Sample measures are used to estimate population measures. |

What are the properties of a good estimator? | It should be an unbiased estimator. It should be a consistent estimator. It should be a relatively efficient estimator. |

Estimation | The process of estimating the value of a parameter from information obtained from a sample. |

Unbiased Estimator | The expected value or the mean of the estimates obtained from a given size is equal to the parameter being estimated. |

Consistent estimator | An estimator whose value approaches the value of the parameter estimated as the sample size increases. |

Relatively Efficient Estimator | An estimator that has the smallest variance from among all the statistics that can be used to estimate a parameter. |

Interval Estimate (of a parameter) | An interval of a range of values used to estimate the parameter. This estimate may or may not contain the value of the parameter being estimated. |

Confidence Level (of an interval estimate of a parameter) | The probability that the interval estimate will contain the parameter, assuming that a large number of samples are selected and that the estimation process on the same parameter repeated. |

Confidence Interval | A specific interval estimate of a parameter determined by using data obtained from a sample and by using the specific confidence level of the estimate. |

Maximum Error of the Estimate (E) | The maximum likely difference between the point estimate of a parameter and the actual value of the parameter. |

The t distribution is similar to the standard normal distribution in these ways: | It is bell shaped. It is symmetric about the mean. The mean, median, and mode are equal to 0 and are located at the center of the distribution. The curve never touches the x axis. |

The t distribution differs from the standard normal distribution in these ways: | The variance is greater than 1. A family of curves based on the concept of degrees of freedom, which is related to sample size. As the sample size increases the t distribution approaches the standard normal distribution. |

Degrees of Freedom (df) | The number of values that are free to vary after a sample statistic has been computed, and they tell the researcher which specific curve to use when a distribution consists of a family of curves. (n - 1) |

Proportion | A part of a whole, represented by a fraction, decimal, or a percentage. |

Chi-Square Distribution ("ki") | A probability distribution obtained from the values of (n-1)*s squared/ sigma squared when random samples are selected from a normally distributed population whose variance is sigma squared. |

Created by:
dengler