Analysis of trends Flashcards

1
Q

what is analysis of trends

A

seeing if there is a correlation - the effect of one variable on another

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2
Q

what is Pearsons correlation efficient

A

+1, 0, -1 = r - no units, numerical, range of values between -1 and 1

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3
Q

what are the three things a correlation can be

A

positive linear, negative linear or 0

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4
Q

what is a bell curve correlation

A

0 - there is a clear relationship, but its not linear so = 0

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5
Q

what does the Pearson calculation give you

A

r - the correlation coefficient - has to be between -1 and 1

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6
Q

what do you do to work out if r is significant

A

r squared - so its always positive

r^2/standard error = t

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7
Q

when is the Pearson calculation not significant

A

when the t value is below r = conclude there isn’t a significant relationship

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8
Q

what does the regression line do

A

minimises the sum of the squared deviation of the points from the line

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9
Q

what are the two quantities of the regression line

A

intercept - c

slope - b

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10
Q

what does the regression line assume - 3 things

A

random sampling, linear correlation, residuals normally distributed with a constant varience

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11
Q

what are the residuals on a graph

A

the points scattered around the regression line

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12
Q

if the three assumptions for the regression line are not met what can you do

A

transform to log - straightens the relationship between the variables - makes residuals normal

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13
Q

what is a ‘linear model’ - common in ecology

A

where the computer finds the best model for your data - estimates the effect of one variable on another

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14
Q

what is ‘overfitting’

A

the model can only get better by adding another variable - the more complex model the more fitting opportunity

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15
Q

what is the ‘minimum adequate model’

A

when you reduce the number of explanatory variables

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16
Q

what data wouldn’t the generalised linear model work for

A

proportions - as there is a strict upper and lower limit - the value can’t be more than 1 (100%)

17
Q

What is Bayes theorem

A

the probability of an event, based on prior knowledge of conditions that might be related to/affect event
eg. cancer related to age - knowledge of age might help in diagnosis bc there is a positive relationship between cancer and age.