Perform an analysis of variance (ANOVA) to determine if the differences in the percentage butterfat of the different breeds of cows is statistically significant. Guernsey Jersey Holstein 4.51 4.59 3.43 5.26 6.67 3.68 5.81 5.39 3.72 5.14 4.38 3.76 4.55 4.99 4.43 4.86 5.49 3.84 4.69 5.43 3.54 4.12 5.02 4.18 5.44 5.16 3.4 4.69 5.15 3.77 5.24 5.76 4.03 4.62 5.34 3.63 4.61 5.11 3.37 5.02 4.75 3.54 4.67 5.07 3.64 4.59 4.42 3.98 4.92 6.13 5.58 4.6 5.6 5.83 1. Compute the treatment sum of squares (SSTr) and the error sum of squares (SSE) and use them to complete the following ANOVA table. (Round your answers to 4 decimal places). Source S.S. df M.S. F Treatment Error Total 2. Compute the p-value. (Round your answer to 4 decimal places.) p-value =
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
Perform an analysis of variance (ANOVA) to determine if the differences in the percentage butterfat of the different breeds of cows is statistically significant.
| Guernsey | Jersey | Holstein |
|---|---|---|
| 4.51 | 4.59 | 3.43 |
| 5.26 | 6.67 | 3.68 |
| 5.81 | 5.39 | 3.72 |
| 5.14 | 4.38 | 3.76 |
| 4.55 | 4.99 | 4.43 |
| 4.86 | 5.49 | 3.84 |
| 4.69 | 5.43 | 3.54 |
| 4.12 | 5.02 | 4.18 |
| 5.44 | 5.16 | 3.4 |
| 4.69 | 5.15 | 3.77 |
| 5.24 | 5.76 | 4.03 |
| 4.62 | 5.34 | 3.63 |
| 4.61 | 5.11 | 3.37 |
| 5.02 | 4.75 | 3.54 |
| 4.67 | 5.07 | 3.64 |
| 4.59 | 4.42 | 3.98 |
| 4.92 | 6.13 | |
| 5.58 | 4.6 | |
| 5.6 | ||
| 5.83 |
1.
Compute the treatment sum of squares (SSTr) and the error sum of squares (SSE) and use them to complete the following ANOVA table. (Round your answers to 4 decimal places).
| Source | S.S. | df | M.S. | F |
| Treatment | ||||
| Error | ||||
| Total |
2. Compute the p-value. (Round your answer to 4 decimal places.)
p-value =
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