Chapter 12
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Chapter 12. Comparing Many Treatments. Homework. LDI: 12.1, 12.2, 12.3, 12.4 Exercises: 12.34, 12.35, 12.36, 12.37. Example.

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Chapter 12

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Chapter 12

Chapter 12

Comparing Many Treatments


Homework

Homework

  • LDI: 12.1, 12.2, 12.3, 12.4

  • Exercises: 12.34, 12.35, 12.36, 12.37


Example

Example

  • Suppose we want to compare three teaching methods based on the mean test scores for each method. The competing theories would be:H0: There is no difference between the three teaching methods with respect to mean test scoreH1: There is a difference between the three teaching methods with respect to the mean test score


Scenario 1

Scenario 1

  • Consider these boxplots for the three methods test scores


Scenario 2

Scenario 2

  • Now what if they looked like this?


Think about it

Think About It

  • There is an obvious difference between Scenario 1 and Scenario 2. What is that difference?

  • Just looking at the boxplots, which of the two scenarios do you think would provide more evidence that at least one of the population means is different from the others? Explain why.


One way anova

One-Way ANOVA

  • Notice that there are two types of variation we are looking at, the between variability and the within variability. The test statistic we’ll use to judge if at least one of the means is significantly different is based on these measures.


One way anova1

One-Way ANOVA

  • Analysis of variance is a statistical technique for comparing the means for several populations, where the levels of a single explanatory variable define the populations.


Assumptions

Assumptions

  • 1. The populations have normal distributions.

  • 2. The populations have the same variance 2 (or standard deviation ).

  • 3. The samples are random and independent of each other.

  • 4. The different samples are from populations that are categorized in only one way.


The f distribution

The F-distribution

  • 1. The F-distribution is not symmetric; it is skewed to the right.

  • 2. The values of F can be 0 or positive, they cannot be negative.

  • 3. There is a different F-distribution for each pair of degrees of freedom for the numerator and denominator. Let I be the number of means (populations drawn from) and n = total sample size across all populations sampled.


The f distribution1

The F-distribution


Notation

Notation

  • Degrees of freedom numerator is given by: I – 1 (number of groups – 1)

  • Degrees of freedom denominator is given by: n – I (number of observations – number of groups)


F distribution

F-distribution

  • The distribution of the F statistic is the the F-distribution. The distribution is indexed by a pair of degrees of freedom, one for the numerator and one for the denominator.


Think about it1

Think About It

  • If all the sample means were the same, what would be the value of the numerator of the F-statistic?

  • If all of the sample means were very spread out and very different from each other, what would the magnitude of the variation be compared to your last answer?

  • What values of the F-statistic support the alternate hypothesis that at least one population has a different mean?


Let s do it

Let’s Do It

  • Page 751: LDI 12.1


Assumptions of anova

Assumptions of ANOVA

  • Page 753: Grey Box

  • Page 754: Example 12.4


Let s do it1

Let’s Do It

  • LDI 12.2 (Note how to calculate Fcdf)

  • LDI 12.3 (What is MSB? What is MSW?)


Definitions

Definitions

  • SSB = Sum of Squares Between

  • SSW = Sum of Squares Within

  • SST = Sum of Squares Total

  • Factor = Between

  • Error = Within


How to do an f test anova on the ti

How to do an F-test (ANOVA) on the TI

  • Example 12.8 and LDI 12.4

  • Use ANOVA(

  • Now, using the summary statistics for each treatment do the calculation by A1ANOVA on the TI.


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