Researchers should always understand the distributions of the variables in their data set. Summary measures that are appropriate for normal distributions may be misleading when applied to non-normal distributions, regardless of sample size. When it comes to statistical testing, normality is often less critical. The t-test, ANOVA, and linear regression may be inappropriate for small samples with extreme deviations from normality. In these cases, the researcher may opt to transform the data, run a nonparametric test, or perform a bootstrap analysis. Discuss these methods of dealing with non-normal data. Question Eight Treatment 1 2 3 4 3.52 3.47 3.54 3.74 3.36 3.73 3.52 3.83 3.57 3.38 3.61 3.87 4.19 3.87 3.76 4.08 3.88 3.69 3.65 4.31 3.76 3.51 3.51 3.98 3.94 3.35 3.86 3.64 3.71 Children were given one of four different drugs at random, and the response measure was liver weight as a percentage of body weight. The responses were Compute the overall mean and treatment effects Compute the Analysis of Variance table for these data. What would you conclude about the four drugs
Researchers should always understand the distributions of the variables in their data set. Summary measures that are appropriate for
Question Eight
|
Treatment |
|||
|
1 |
2 |
3 |
4 |
|
3.52 |
3.47 |
3.54 |
3.74 |
|
3.36 |
3.73 |
3.52 |
3.83 |
|
3.57 |
3.38 |
3.61 |
3.87 |
|
4.19 |
3.87 |
3.76 |
4.08 |
|
3.88 |
3.69 |
3.65 |
4.31 |
|
3.76 |
3.51 |
3.51 |
3.98 |
|
3.94 |
3.35 |
|
3.86 |
|
|
3.64 |
|
3.71 |
Children were given one of four different drugs at random, and the response measure was liver weight as a percentage of body weight. The responses were
- Compute the overall mean and treatment effects
- Compute the Analysis of Variance table for these data.
What would you conclude about the four drugs
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