Transcript Math 2200 Chapter 13 Power Pointx

CHAPTER 13 F DISTRIBUTION AND 0NE-WAY ANOVA
This presentation is based on material and graphs from Open Stax and is copyrighted by Open Stax and Georgia
Highlands College.
INTRODUCTION
For hypothesis tests comparing averages between more than two groups, statisticians
have developed a method called "Analysis of Variance" (abbreviated ANOVA). In this
chapter, you will study the simplest form of ANOVA called single factor or one-way
ANOVA.
13.1 | ONE-WAY ANOVA
ONE-WAY ANOVA
The purpose of a one-way ANOVA test is to determine the existence of a statistically
significant difference among several group means.
The test actually uses variances to help determine if the means are equal or not.
In order to perform a one- way ANOVA test, there are five basic assumptions to be
fulfilled:
1. Each population from which a sample is taken is assumed to be normal.
2. All samples are randomly selected and independent.
3. The populations are assumed to have equal standard deviations(or variances).
4. The factor is a categorical variable.
5. The response is a numerical variable.
THE NULL AND ALTERNATIVE HYPOTHESIS
The null hypothesis is simply that all the group population means are the same.
The alternative hypothesis is that at least one pair of means is different.
For example, if there are k groups:
H0:μ1 =μ2 =μ3 = ... =μk
Ha: At least two of the group means μ1,μ2,μ3, ...,μk are not equal.
EXAMPLES OF NULL HYPOTHESIS BY BOX PLOTS
(a) H0 is true. All means are the same; the
differences are due to random variation.
(b) H0 is not true. All means are not the
same; the differences are too large to be
due to random variation.
13.2 | THE F DISTRIBUTION AND THE F-RATIO
THE F DISTRIBUTION AND THE F-RATIO
The distribution used for the hypothesis test is a new one.
It is called the F distribution, named after Sir Ronald Fisher, an English statistician.
The F statistic is a ratio (a fraction).
There are two sets of degrees of freedom; one for the numerator and one for the
denominator.
For example, if F follows an F distribution and the number of degrees of freedom for
the numerator is four, and the number of degrees of freedom for the denominator is
ten,
then F~F4,10.
ONE-WAY ANOVA RESULTS
Source of Variation
Sum of Squares
(SS)
Degrees of
Freedom (df)
Mean Square (MS)
F
Factor (Between)
SS (Factor)
k -1
MS (Factor) =
SS(Factor)/(k -1)
F=
MS(Factor)/MS(Error)
Error (Within)
SS (Error)
n-k
MS(Error) =
SS(Error)/(n-k)
Total
SS (Total)
n -1
F RATIO
To calculate the F ratio, two estimates of the variance are made.
1. Variance between samples: An estimate of σ² that is the variance of the sample
means multiplied by n (when the sample sizes are the same.). If the samples are
different sizes, the variance between samples is weighted to account for the different
sample sizes. The variance is also called variation due to treatment or explained
variation.
F RATIO
2. Variance within samples: An estimate of σ2 that is the average of the
sample variances (also known as a pooled variance). When the sample sizes
are different, the variance within samples is weighted. The variance is also
called the variation due to error or unexplained variation.
• SS between = the sum of squares that represents the variation among the
different samples
• SS within = the sum of squares that represents the variation within samples
that is due to chance.
F RATIO
To find a "sum of squares“ means to add together squared quantities that , in
some cases, may be weighted. We used sum of squares to calculate the sample
variance and the sample standard deviation in Descriptive Statistics.
MS means "mean square.“
MS between is the variance between groups
MS within is the variance within groups.
F = MS between /MS within
NEVER COMPLETE BY HAND. USE THE WEBSITE LISTED LATER IN THE
POWERPOINT.
NOTATION
The one-way ANOVA hypothesis test is always right-tailed because
larger F-values are way out in the right tail of the F-distribution
curve and tend to make us reject H0.
The notation for the F distribution is F~Fdf(num),df(denom)
where df(num) =dfbetween and df(denom) =dfwithin
The mean for the F distribution is µ = df(num) / df(denom)–1
ANALYSIS OF VARIANCE
ANOVA—ONE WAY
Using Internet Website
WEBSITE
http://turner.faculty.swau.edu/mathematics/math241/materials/anova/
EXAMPLE
A regional manager wants to know if there is a difference between the
mean amounts of time that customers wait in line at the drive through
window for the three stores in her region. She samples the wait times at
each store. Use an ANOVA test to determine if a difference between
the mean wait times for the three stores, at the 0.05 level of
significance.
Drive Through Wait Times (In Minutes)
Store
1
2.34
1.23
1.89
2.31
3.02
1.95
2.45
Store
2
2.87
1.94
2.36
1.85
1.75
2.82
3.32
Store
3
1.32
1.45
1.78
2.01
2.45
1.92
1.83
SETUP
Determine the number of “treatment” groups (categories)
Determine the “maximum” number of data values in any category
For the given example, there are 3 stores (aka “treatment groups”)
with 7 data values for each store.
Click “continue”
Enter treatment group labels
Enter the data values under the
appropriate column (category)
Select “yes” for display graph
Click “compute”
Results appear on screen. Focus on
table displayed below
Record the F-test
statistic given in the
table.
Record the p-value
given in the table
13.3 FACTS ABOUT THE F DISTRIBUTION
13.3 | FACTS ABOUT THE F DISTRIBUTION
Here are some facts about the F distribution.
1. The curve is not symmetrical but skewed to the right.
2. There is a different curve for each set of dfs.
3. The F statistic is greater than or equal to zero.
4. As the degrees of freedom for the numerator and for the denominator get larger,
the curve approximates the normal.
5. Other uses for the F distribution include comparing two variances and two-way
Analysis of Variance. Two-Way Analysis is beyond the scope of this chapter.
DIFFERENCE IN THE F DISTRIBUTIONS
These graphs show that as the degrees of freedom for the numerator and for the
denominator get larger, the curve approximates the normal.
HYPOTHESES
The null and alternative hypotheses are:
H0:μ1 =μ2 =μ3 =μ4 =μ5
Ha: Not all of the means μ1,μ2,μ3,μ4,μ5 are equal.
You may see the Ha written an few other ways but for the course, you can write it like
above.
SOLVING FOR A ONE- WAY ANOVA EXAMPLE
Let’s return to the slicing tomato exercise in Try It.
The means of the tomato yields under the five mulching conditions are represented by
μ1, μ2, μ3, μ4, μ5. We will conduct a hypothesis test to determine if all means are
the same or at least one is different. Using a significance level of 5%, test the null
hypothesis that there is no difference in mean yields among the five groups against
the alternative hypothesis that at least one mean is different from the rest.
Bare: n1 = 3
Ground Cover: n2 = 3
Plastic: n3 = 3
Straw: n4 = 3
Compost: n5 = 3
2625
5348
6583
7285
6277
2997
5682
8560
6897
7818
4915
5482
3830
9230
8677
RESULTS
Hypotheses:
Ho: all the means are the same. OR H0:μ1 =μ2 =μ3 =μ4 =μ5
Ha: Not all of the means μ1,μ2,μ3,μ4,μ5 are equal. OR µi ≠µj some i≠j
Run ANOVA
Distribution for F-Test: F4,10
Df(num)= 4
Df(denom) = 10
RESULTS
What is the p-value? 0.0248
P-value = P(F>4.481) = 0.0248
Make a decision p and alpha
0.0248 < 0.05, reject the Ho
Conclusion: At the 5% level of significance, there is strong evidence that differences in
the mean yields for the mulching conditions are unlikely due to chance alone. We may
conclude that at least some of the mulches led to different mean yields.
ANOTHER EXAMPLE
Four sororities took a random sample of sisters regarding their grade means for the past term.
The results are shown in the table below
Sorority 1
Sorority 2
Sorority 3
Sorority 4
2.17
2.63
2.63
3.79
1.85
1.77
3.78
3.45
2.83
3.25
4.00
3.08
1.69
1.86
2.55
2.26
3.33
2.21
2.45
3.18
Using a significance level of 1%, is there a difference in mean grades among the sororities?
RESULTS
Hypotheses
Ho: μ1 =μ2 =μ3 =μ4
Ha: Not all of the means μ1,μ2,μ3,μ4 are equal.
F distribution F3,16
F= 2.2303
P-value = P(F>2.2303) =0.1241
P and level of significance
0.1241> 0.01; I cannot reject the null hypothesis
There is no sufficient evidence to conclude that there is a difference among the mean
grades for the sororities.