Answering "Why ln(length) is Better for ANOVA & F-Test Homework

  • Thread starter MaxManus
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In summary, the conversation discusses the use of ln(length) in an Anova analysis to determine if length is dependent on age. The question is why ln(length) is better than using length directly. Both methods seem to show a linear relationship, but ln(length) has a lower F-value. The questioner also mentions that both methods have a p-value of 2.16*10^-16 in R. The questioner then suggests using ln(age) instead and asks why ln(length) is better. The conversation also briefly touches on using linear regression on the two models and comparing R^2 values, with ln(length) showing a lower R^2 of 0.49.
  • #1
MaxManus
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Homework Statement

I am using Anova to check if length is dependent of age. The question is, "why is it better to use ln(length)?"

How should I answer this?
I think both seems linear on the two plots(attachment)
But ln(length) gives lower F-value and isn't that a bad thing so why is it better to use? Both has p-value = 2.16*10^-16 in R.

F-value ln(length) = 483
F-value length = 522
 

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  • #2
MaxManus said:
The question is, "why is it better to use ln(age)?"
You looked at ln(length) v age.

If that is the question, why didn't you look at answering the question?
 
  • #3
D H said:
You looked at ln(length) v age.

If that is the question, why didn't you look at answering the question?

Sorry, the question is why is ln(length) vs age better than length vs age
 
  • #4
Have tried to use a linear regression on the two models to see which is best by comparing R^2. On linear-linear I got 0.5678. To compute R^2 for log linear I first ran the regression ln(length) = b0 + b1*age
and then computed ^length^ = exp(^b0)exp(^b1^x) and estimated the R^2 between ^length^ and x. R^2 = 0.49, which is worse

where ^text^ means estimates for text
 

Related to Answering "Why ln(length) is Better for ANOVA & F-Test Homework

1. Why is ln(length) considered better for ANOVA and F-test homework?

The natural logarithm (ln) of length is considered better for ANOVA and F-test homework because it helps to transform the data into a more normal distribution. This is important because ANOVA and F-tests assume that the data is normally distributed, and using ln(length) can help to meet this assumption.

2. How does ln(length) help to normalize the data?

By taking the natural logarithm of the length data, the distribution of the data is shifted and stretched, resulting in a more symmetrical and bell-shaped curve. This helps to reduce the skewness of the data and make it more normally distributed.

3. Can other transformations be used instead of ln(length)?

Yes, there are other transformations that can be used to normalize data for ANOVA and F-tests, such as square root, reciprocal, and logarithm base 10. However, ln(length) is often preferred because it is a commonly used transformation and has been shown to be effective in normalizing data.

4. Are there any limitations to using ln(length) for ANOVA and F-test homework?

One limitation of using ln(length) is that it may not be suitable for data with extreme outliers. In these cases, other transformations or non-parametric tests may be more appropriate. Additionally, it is important to consider the underlying distribution of the data and whether a transformation is truly necessary before applying ln(length).

5. How can I determine if ln(length) is the best transformation for my data?

There are a few ways to determine if ln(length) is the best transformation for your data. One way is to visually inspect the data before and after the transformation and see if it appears more normally distributed. Another way is to use statistical tests, such as the Shapiro-Wilk test, to assess the normality of the data before and after the transformation. Ultimately, the best transformation will depend on the specific characteristics of your data and the assumptions of the statistical test you are using.

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