Bonferroni Margin Error
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margin of error and standard deviation statisticsfun SubscribeSubscribedUnsubscribe50,01950K Loading... Loading... Working... Add to Want to watch this again later? bonferroni degrees of freedom Sign in to add this video to a playlist. bonferroni method example Sign in Share More Report Need to report the video? Sign in to report inappropriate bonferroni multiple comparisons test example content. Sign in Statistics 17,418 views 63 Like this video? Sign in to make your opinion count. Sign in 64 1 Don't like this bonferroni confidence interval video? Sign in to make your opinion count. Sign in 2 Loading... Loading... Loading... Rating is available when the video has been rented. This feature is not available right now. Please try again later. Uploaded on Jul 12, 2011In this tutorial I show the relationship standard deviation and margin
Margin Of Error
of error. I calculate margin of error and confidence intervals with different standard deviations.Playlist on Confidence Intervalshttp://www.youtube.com/course?list=EC...Like us on: http://www.facebook.com/PartyMoreStud...Created by David Longstreet, Professor of the Universe, MyBookSuckshttp://www.linkedin.com/in/davidlongs... Category Education License Standard YouTube License Show more Show less Loading... Advertisement Autoplay When autoplay is enabled, a suggested video will automatically play next. Up next How to calculate Margin of Error Confidence Interval for a population proportion - Duration: 8:04. statisticsfun 42,703 views 8:04 How to calculate Confidence Intervals and Margin of Error - Duration: 6:44. statisticsfun 154,308 views 6:44 How to calculate sample size and margin of error - Duration: 6:46. statisticsfun 64,488 views 6:46 Margin of Error Example - Duration: 11:04. drenniemath 36,919 views 11:04 Confidence Level and Margin of Error - Duration: 5:31. Rett McBride 6,562 views 5:31 7 videos Play all Standard Deviationstatisticsfun Standard Deviation and Z-scores - Duration: 20:00. Alge
is a simple method that allows many comparison statements to be made (or confidence intervals to be constructed) while still assuring an overall confidence coefficient is maintained. Applies for a finite number of contrasts This method applies to an ANOVA situation when
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the analyst has picked out a particular set of pairwise comparisons or contrasts or linear combinations in advance. This set is not infinite, as in the Scheffé case, but may exceed the set of pairwise comparisons specified in the Tukey procedure. Valid for both equal and unequal sample sizes The Bonferroni method is valid for equal and unequal sample sizes. We restrict ourselves to only linear combinations or comparisons of treatment level https://www.youtube.com/watch?v=9g2MHYYKpNM means (pairwise comparisons and contrasts are special cases of linear combinations). We denote the number of statements or comparisons in the finite set by \(g\). Bonferroni general inequality Formally, the Bonferroni general inequality is presented by: $$ P\left( \bigcap_{i=1}^g A_i \right) \ge 1 - \sum_{i=1}^g P[\bar{A_i}] \, , $$ where \(A_i\) and its complement \(\bar{A_i}\) are any events. Interpretation of Bonferroni inequality In particular, if each \(A_i\) is the event that a calculated http://www.itl.nist.gov/div898/handbook/prc/section4/prc473.htm confidence interval for a particular linear combination of treatments includes the true value of that combination, then the left-hand side of the inequality is the probability that all the confidence intervals simultaneously cover their respective true values. The right-hand side is one minus the sum of the probabilities of each of the intervals missing their true values. Therefore, if simultaneous multiple interval estimates are desired with an overall confidence coefficient \(1 - \alpha\), one can construct each interval with confidence coefficient \((1 - \alpha/g)\), and the Bonferroni inequality insures that the overall confidence coefficient is at least \(1 - \alpha\). Formula for Bonferroni confidence interval In summary, the Bonferroni method states that the confidence coefficient is at least \(1 - \alpha\) that simultaneously all the following confidence limits for the \(g\) linear combinations \(C_i\) are "correct" (or capture their respective true values): $$ \hat{C}_{i} \pm t_{1-\alpha/(2g), N-r} \,\, s_{\hat{C}_i} $$ where $$ s_{\hat{C}_i} = \hat{\sigma}_\epsilon \, \sqrt{\sum^{r}_{i=1} \frac{c^2_i}{n_i}} \, . $$ Example using Bonferroni method Contrasts to estimate We wish to estimate, as we did using the Scheffe method, the following linear combinations (contrasts): $$ \begin{eqnarray} C_1 & = & \frac{\mu_1 + \mu_2}{2} - \frac{\mu_3 + \mu_4}{2} \\ & & \\ C_2 & = & \frac{\mu_1 + \mu_3}{2} - \frac{\mu_2 + \mu_4}{2} \, ,
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