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What Is Anova? | Introduction To Analysis And Variance | Anova Explained |

In this What is Anova presentation, we are going to discuss the Analysis of variance and how is used in statistics. You will understand the basics of Anova, and how does it work? You will get an idea about the different terminologies used in Anova. Finally, you will look at an interesting video in Excel.<br><br>In this presentation, you will learn<br>1. What is ANOVA?<br>2. How does ANOVA work?<br>3. Important Terminologies<br>4. Real-World Example

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What Is Anova? | Introduction To Analysis And Variance | Anova Explained |

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  1. What’s in it for you? What is ANOVA? Types of ANOVA Terminologies How does ANOVA work? F-Statistics Real world example

  2. What is ANOVA?

  3. Click here to watch the video

  4. What is Analysis Of Variance? Analysis of variance (ANOVA) is a statistical test for detecting differences in the group means when there is one parametric dependent variable & one or more independent variable Specifically, we are interested in determining whether differences exist between the population means

  5. Types Of ANOVA

  6. Types Of ANOVA ANOVA One-way ANOVA Two-way ANOVA Two dependent variable One dependent variable One independent variable Two or more independent variables

  7. Terminologies

  8. Important Terminologies A general statement that states that there is no relationship between two measured phenomena or no association among groups Null Hypothesis OPTION 01 Contrary to the Null hypothesis, it states whenever something is happening, a new theory is preferred instead of an old one Alternative Hypothesis OPTION 01 The P value is the probability of finding the observed, or more extreme, results when the null hypothesis of a study question is true OPTION 01 P value OPTION 01 The alpha value is a criterion for determining whether a test statistic is statistically significant Alpha Value

  9. Important Terminologies Let’s assume that a new drug is developed with the goal of lowering the blood pressure more than the existing drug Null Hypothesis The new drug doesn’t lower the blood pressure more than the existing drug OPTION 01 Alternative Hypothesis The new drug does significantly lower the blood pressure more than the existing drug OPTION 01 OPTION 01 Results from evidences like medical trials showing positive results which will reject the null hypothesis OPTION 01 P value T value

  10. Important Terminologies Let’s assume that a new drug is developed with the goal of lowering the blood pressure more than the existing drug The extent of difference between the means of different medical trials F-Statistics OPTION 01 Sum of squares The variation from the mean of different medical trials OPTION 01 OPTION 01 Average of all the results from evidences like medical trials OPTION 01 Mean T value

  11. How does ANOVA Work?

  12. How does ANOVA work? If any of the group means is significantly different from the overall mean, then the null hypothesis is rejected ANOVA determines whether the groups created by the levels of the independent variable are statistically different by calculating whether the means of the different samples are different from the overall mean of the dependent variable

  13. F-Statistics

  14. F-Statistics An F Statistic is a value you get when you run an ANOVA test to find out if the means between two populations are significantly different Between-Group Variance Within-Group Variance

  15. F-Statistics Between-group variance is large relative to the within-group variance, so F statisticwill be larger & > critical value, thereforestatistically significant Between-Group Variance Within-Group Variance

  16. Real World Example

  17. Real World Example Suppose you are a marketing manager of a product company, and you want to know if the three different types of advertisement effect mean sales differently You use each type of advertisement at 20 different stores for one month and measure the total sales of each store for a month

  18. Real World Example To observe if there is statistically significant difference in the mean sales between these three types of advertisements, you can conduct a one-way ANOVA You will use types of advertisement as the factor and the sales as responsive variable

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