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7.4.1.6 - Example: Difference in Mean Commute Times.7.4.1.4 - Example: Proportion of Women Students.7.4.1.3 - Example: Proportion NFL Coin Toss Wins.7.4.1.2 - Video Example: Correlation Between Printer Price and PPM.7.4.1.1 - Video Example: Mean Body Temperature.7.3 - Minitab: Finding Values Given Proportions.7.2.3.1 - Example: Proportion Between z -2 and +2.7.2 - Minitab: Finding Proportions Under a Normal Distribution.6.6 - Confidence Intervals & Hypothesis Testing.5.5.4 - Correlation Example: Quiz & Exam Scores.5.5.3 - Difference in Means Example: Exercise by Biological Sex.5.5.1 - Single Proportion Example: PA Residency.5.5 - Randomization Test Examples in StatKey.5.3.1 - StatKey Randomization Methods (Optional).5.1 - Introduction to Hypothesis Testing.4.6 - Impact of Sample Size on Confidence Intervals.4.4.2.2 - Example: Difference in Dieting by Biological Sex.4.4.2.1 - Example: Correlation Between Quiz & Exam Scores.4.4.1.2 - Example: Difference in Mean Commute Times.4.4.1.1 - Example: Proportion of Lactose Intolerant German Adults.4.3.2 - Example: Bootstrap Distribution for Difference in Mean Exercise.4.3.1 - Example: Bootstrap Distribution for Proportion of Peanuts.4.2.1 - Interpreting Confidence Intervals.4.2 - Introduction to Confidence Intervals.4.1.1.2 - Coin Flipping (One Proportion).3.5 - Relations between Multiple Variables.3.4.2.2 - Example of Computing r by Hand (Optional).3.4.2.1 - Formulas for Computing Pearson's r.3.3 - One Quantitative and One Categorical Variable.2.2.6 - Minitab: Central Tendency & Variability.2.2.1 - Graphs: Dotplots and Histograms.2.1.3.2.5.1 - Advanced Conditional Probability Applications.2.1.3.2.1 - Disjoint & Independent Events.2.1.2.1 - Minitab: Two-Way Contingency Table.1.2.2.1 - Minitab: Simple Random Sampling.1.1.2 - Explanatory & Response Variables.
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1.1.1 - Categorical & Quantitative Variables.Book traversal links for 10.1 - Introduction to the F Distribution Note: When you conduct an ANOVA in Minitab, the software will compute this p-value for you. The area beyond an F-value of 2.57 with 3 and 246 degrees of freedom is 0.05487. Fill in the Numerator degrees of freedom with 3 and the Denominator degrees of freedom with 246.Select Graph > Probability Distribution Plot > View Probability.We want to shade the area in the right tail. The numerator df (\(df_1\)) is 3 and the denominator df (\(df_2\)) is 246. Scenario: An F test statistic of 2.57 is computed with 3 and 246 degrees of freedom. Within groups is also referred to as error.
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Minitab will call these the numerator and denominator degrees of freedom, respectively.
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The F distribution has two different degrees of freedom: between groups and within groups. Later in this lesson we will see that this area is the p-value. For the F distribution we will always be looking for a right-tailed probability. The video below gives a brief introduction to the F distribution and walks you through two examples of using Minitab to find the p-values for given F test statistics. The steps for creating a distribution plot to find the area under the F distribution are the same as the steps for finding the area under the \(z\) or \(t\) distribution. The F test statistic can be used to determine the p-value for a one-way ANOVA. Similarly, in this lesson you are going to compute F test statistics. You computed \(z\) and \(t\) test statistics and used those values to look up p-values using statistical software. Earlier in this course you learned about the \(z\) and \(t\) distributions. Within groups is also referred to as error.One-way ANOVAs, along with a number of other statistical tests, use the F distribution. One-way ANOVAs, along with a number of other statistical tests, use the F distribution.