Regression, Anovas, Generilized Linear Models

Question 1

Is ecological data normally normally distrubuted?

Answer 1

No

Question 2

What summerized stats assume normal distribution

Answer 2

CI’S, SD, mean, varience, SE

Question 4

Why do ecologist still use mean, standard deviation, standard error, and confidence interval, even though they may not have normally distributed data?

Answer 4

• All of these metrics are just ways of summarizing sample, not formal statistical test
• statistics use individual data points like raw data not summarize samples

Question 5

What is statistical robustness?

Answer 5

That the results of a given statistical test are still reliable, even though the underlying assumptions of the test are not fully met by the data

Question 6

What does a Levine test use for?

Answer 6

In an anova, it can be helpful to detect equal variances, but this can be overly sensitive because they often respond to non-normality

Question 7

Which is more important equal appearances or normality in parametric test?

Answer 7

Equal valances is more important than normality

Question 8

How can you improve equality of appearances?

Answer 8

They are often linked to non-normality such as data with positive skewness can have various increases with mean, we can use log transformation or for transformations to improve data and normality and equal variences

Question 9

How can we detect outliers?

Answer 9

In the ANOVA we can do box plots in exploratory analysis or in regression analysis we can use cooks D test

Question 10

How do we deal with outliers?

Answer 10

Always double check data entry. ask yourself if there are errors in the lab work and remove, but you need prior reasoning
- if outline is a combined with non-normality, then transforming the data can normally help
-run analysis with and without them and compare the conclusions
-

Question 11

Compare box plots, and histogram and what do they checking for?

Answer 11

Histograms allow checking for normality while box plots detect for non-normality, unequal variances, and outliers

Question 12

What is type i vs a type ii error? Which one is least common?

Answer 12

I) effect detected; none exists (False positive?)
II) effect not detected; but exists - (false negative?)

Question 13

What is statistical power?

Answer 13

The probability of detecting a given effect in a statisitcal population with our statistical sample (or samples) if it occurs in this population.
Simple version: the probability of detecting a ‘true’ effect when it exists in our data.
Power= 1- B (beta, probability of making a type ii error)
Should be >0.80 (B<0.20)

Question 14

What does stats power depend on? What increases power?

Answer 14

1) effect size - larger
2) sample size - larger
3) variance - less variation
4) significance level, a (alpha) - typically a= 0.05, power increases when power is set at eg. 0.10. These are not permanet values