BIO 300 Lab Quiz 3

Question 1

correlation

Answer 1

strength of a linear association between 2 numerical variables

Question 2

correlation uses

Answer 2

correlation coefficient

Question 3

correlation coefficient

Answer 3

r
[-1,1]
unitless

Question 4

-r

Answer 4

as one variable increases the other decreases

Question 5

inferences from correlation

Answer 5

cannot infer causality

Question 6

regression

Answer 6

implies causality between 2 variables
used to predict value of response variable from explanatory variable
can determine how much of variability is due to relationship w/ explanatory variable

Question 7

regression statistic

Answer 7

R^2 = SS_regression / SS_total

[0,1]

Question 8

linear regression assumptions

Answer 8

• relationship between response (Y) and explanatory (X) is linear
• Y values at each value of X are normally distributed
• variance of Y values is same at all values of X
• Y measurements are sampled randomly from the population at each value of X

Question 9

are there any outliers

Answer 9

yes, can be seen by boxplot

Question 10

how to do regression with multiple groups (N/S)

Answer 10

“regression with groups”

then add total regression line by right clicking then going to regression fit

Question 11

do the data need to be transformed

Answer 11

are the data clumped in one corner of the scatterplot
is there greater spread in one section of the scatterplot
are there different orders of magnitude spanned in the variables

Question 12

how to SLR

Answer 12

stat– regression– regression– fit regression model

Question 13

options to check for regression

Answer 13

responses- Chl-a
continuous predictor- Log P
graphs- residuals vs. fits
results- everything but Durbin-Watson
storage- residuals

Question 14

SSregression

Answer 14

proportion of variation in response variable accounted for by the regression

Question 15

SSresidual

Answer 15

proportion of variation unexplained by regression

Question 16

MS

Answer 16

measure of variance, average of sums of squares: SS/df

Question 17

F

Answer 17

F-ratio

MSregression/MSresidual

Question 18

constant

Answer 18

y-intercept

note that this value will appear in the equation

Question 19

log P constant

Answer 19

the slope

will appear in equation

Question 20

better predictor of response variable

Answer 20

higher R^2

Question 21

multiple regression

Answer 21

stat– regression– fit regression model
responses- chl-a
continuous predictors- Log P, Log N

Question 22

dont forget

Answer 22

to label residual columns
check for normality
check for equal variance
check if assumptions are met
test residuals for normality

Question 23

when R^2 SLR ~ R^2 MR

Answer 23

possibly 2 explanatory variables are correlated

Question 24

if two explanatory variables are correlated

Answer 24

co-linearity

Question 25

why did Log N lose its significance in MR

Answer 25

the variation explained by Log N is already accounted for by Log P.. not much variation left that Log N can describe

Question 26

test for correlation

Answer 26

stat– basic stat– correlation– variables: logP, log N— ok

Question 27

stepwise multiple regression

Answer 27

looks at all combinations of explanatory variables to retain the ones that explain the most variation
eliminates explanatory variables that do not add any new explanatory power

Question 28

do you need different predictive equations for the 2 sampling locations

Answer 28

are the intercepts and slopes of the regression equations significantly different?

Question 29

test for significant differences in y-intercept of regression lines

Answer 29

ANCOVA

Question 30

ANCOVA

Answer 30

analysis of covariance

Question 31

ANCOVA assumes

Answer 31

equal slopes (parallel lines)

Question 32

testing for equal slopes

Answer 32

are the lines parallel? if they are then interactions are not significant
we’ll assume that they are

Question 33

if y-intercepts are not significantly different (p-value ≥ alpha)

Answer 33

free to use one regression equation for both locations

Question 34

running an ANCOVA

Answer 34

stat– anova– GLM– fit general linear model

Question 35

options in ANCOVA

Answer 35

responses-- chl-a
factors- location
covariates- log P
model- location, log P, location*log P
results: only ANOVA
storage: residuals

Question 36

ANCOVA output

Answer 36

• if interaction is not significant re-build model without interaction
• determine if effect of sampling location is important- decide if you need 2 equations or not

Question 37

if sampling location has significant effect

Answer 37

need to 2 equations

Question 38

how to get separate equations for sampling location

Answer 38

separate data by sampling location and start again from beginning for each location separately