BIO 330 Lab Quiz 2

Question 1

analysis of variance tests

Answer 1

the null hypothesis that all groups/treatments have equal population means Ho: µ1 = µ2 = µ3 = ……

Question 2

ANOVA compares

Answer 2

2 estimated components of variation MS_error, MS_groups

Question 3

MS_error

Answer 3

Error mean square variation among samples in the same group- variance within group also MS_within

Question 4

MS_group

Answer 4

Group mean square variation among samples that belong to different groups- variance between groups also MS_between

Question 5

in null hypothesis is true

Answer 5

MS_error and MS_groups should be ~same F-ratio ~1

Question 6

F-ratio

Answer 6

MS_groups : MS_error

Question 7

MS_groups >> MS_error

Answer 7

F-ratio > 1 significant differences among the populations means, null hypothesis of no difference can be rejected

Question 8

MS_groups not significantly larger than MS_error

Answer 8

null hypothesis cannot be rejected

Question 9

ANOVA results p ≤ 0.05

Answer 9

at least one group differs from the others, does not tell us which group differs

Question 10

If p ≤ 0.05

Answer 10

use post-hoc tests to find out which groups are significantly different from which others ex. Tukey-Kramer test

Question 11

Squirrel study

Answer 11

red squirrel litter size decline w/ density due to: -reduced per capita food availability reduces fecundity -increased territorial interactions among individuals reduce surplus energy for reproduction

Question 12

explanatory variables in squirrel study

Answer 12

Treatments- squirrel removal, food addition, habitat type

Question 13

what were the levels of each treatment variable

Answer 13

squirrel removal (add, control) food addition (add, control) habitat type (douglas-fir, lodgepole pine)

Question 14

ANOVA

Answer 14

Analysis Of VAriance

Question 15

ANOVA uses what distribution

Answer 15

F-distribution to assess whether the calculated F-ratio is significant

Question 16

t =

Answer 16

square root of F

Question 17

simplest case of ANOVA

Answer 17

one-way/single factor ANOVA k ≥ 3, k = # of groups to compare 1 response variable, 1 treatment variable

Question 18

response variable

Answer 18

litter size

Question 19

is pseudoreplication an issue

Answer 19

there were multiple litter size measurements for each treatment, if we used every one that would be pseudoreplication, each of these points within one group are subsamples, we had to average them within each group

Question 20

how to enter data

Answer 20

each column is a factor (treatment and response) each factor is coded (1,2)

Question 21

How to run ANOVA

Answer 21

Stat- ANOVA- GLM- fit general linear model- resonse- mean litter- factors- habitat+food+squirrel model- all singles and combinations graphs- 4 in 1 storage- residuals

Question 22

options dialog box

Answer 22

enter adjusted (type 3)

Question 23

comparisons dialog box

Answer 23

enter pairwise comparisons activate Tukey, CL, test dialog boxes, post hoc?

Question 24

is there any point in doing a post hoc?

Answer 24

if there are only 2 levels than probably not

Question 25

storage dialog box

Answer 25

activate residuals

Question 26

factor plots dialog box

Answer 26

factor plots dialog box

Question 27

Factor plots

Answer 27

indication of strength of possible interactions enter variables in main effects plot box and interactions plot box

Question 28

testing homogeneity

Answer 28

Stat- ANOVA- test for equal variances- response data- residuals (stored from original ANOVA) - factors- habitat/squirrel/food- Levene's test

Question 29

How to state results

Answer 29

H\_o: There is no effect of habitat on mean squirrel litter size H\_a: There is an effect of habitat on mean squirrel litter size Result: (F = 100, DF = 1,17, P\<0.001) Conclusion: Reject null hypothesis and conclude that habitat has an effect on mean squirrel litter size

Question 30

how to split data up in excel

Answer 30

data- text to columns- delimited- next- comma- finish

Question 31

interpreting output

Answer 31

F-value is F-ratio (error within group vs. between) P-values- which interaction(s) are significant R-squared- fraction of variation explained by groups Coefficients- response increases/decreases by that factor for each treatment/combination of treatments

Question 32

R^2 =

Answer 32

SS\_group / SS\_total

Question 33

R^2 = 0.43

Answer 33

43% differences among groups in light of treatment 57% is error, variance unexplained by explanatory

Question 34

R^2 range

Answer 34

[0,1]

Question 35

R^2 = 0

Answer 35

group means very similar, most variability is within groups

Question 36

R^2 measures

Answer 36

fraction of variation in Y that is explained by group differences

Question 37

R^2 = 1

Answer 37

explanatory variable explains most of the variation in Y

Question 38

SS

Answer 38

separates 2 sources of variation in the data deviations btw each observation and groups mean deviations btw mean of groups and grand mean

Question 39

MS\_group =

Answer 39

SS\_groups/df\_groups df\_groups = k-1 k is number of groups represents variation among sampled individuals belonging to different groups

Question 40

MS\_error =

Answer 40

SS\_error / df\_error df\_error = N - k N = total # data points in all groups pooled sample variance, variation among individuals within same groups

Question 41

variance ratio

Answer 41

F = MS\_groups / MS\_error

Question 42

sources of variation

Answer 42

groups (treatments), error

Question 43

mean squares

Answer 43

group mean square, error mean square

Question 44

should we be concerned with small departures from normal

Answer 44

ANOVA is robust to deviations from normality, especially if sample size is large

Question 45

Tukey-Kramer tests

Answer 45

one pair of means at a time

Question 46

with only 2 levels of each treatment, is there any point in doing a post hoc comparison test?

Answer 46

no? because post hoc tests compare the means of every level.. we only have two to compare?