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Regression, Anovas, Generilized Linear Models Flashcards

(14 cards)

1
Q

Is ecological data normally normally distrubuted?

A

No

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2
Q

What summerized stats assume normal distribution

A

CI’S, SD, mean, varience, SE

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3
Q
A
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4
Q

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

A
  • 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
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5
Q

What is statistical robustness?

A

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

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6
Q

What does a Levine test use for?

A

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

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7
Q

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

A

Equal valances is more important than normality

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8
Q

How can you improve equality of appearances?

A

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

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9
Q

How can we detect outliers?

A

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

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10
Q

How do we deal with outliers?

A

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
-

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11
Q

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

A

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

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12
Q

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

A

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

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13
Q

What is statistical power?

A

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)

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14
Q

What does stats power depend on? What increases power?

A

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

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