Final Exam Flashcards

1
Q

Estimable function

A

expected differences between categories of a fixed effect are estimable; the expected averages for categories of a fixed effect are not estimable

a. Expected difference between HRFI and LRFI feed intake

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2
Q

confounding

A

occurs when one explanatory variable is related to the response of interest and to another (or more than one) explanatory variable so that it is impossible to separate the effects of two explanatory variables on the response

a. Non-estimable SAS errors
b. Missing combinations of fixed effects
c. Choose variable which you think is responsible for most of the variation
a. Day and week were confounded in my study

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3
Q

Collinearity

A

occurs when two variables are highly correlated

a. As one variable increases the other variable increases proportionally
b. This won’t tell us much because the variables are so similar and correlated
c. Wastes time, $, labor
a. Hot carcass weight and loin eye area

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4
Q

null hypothesis

A

no difference between treatment groups means

a. No difference between behaviors or HRFI and LRFI pigs

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5
Q

alternative hypothesis

A

the treatment group means are different (data do not conform to null hypothesis)

b. LRFI pigs will be less stressed during HAT and NOT than HRFI pigs

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6
Q

precision & accuracy

A

a. Precision- how well repeated observations agree with one another
b. Accuracy- how well the observed value of a quantity agrees with the true value
c. One can be precise without being accurate, so accuracy may be more important

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7
Q

power

A

how many experimental units (n) are needed to detect a statistical difference

a. Best to determine power for most variable trait knowing that the power will be higher for all other traits with less variation
b. Properly designed experiments ensure power will be high enough to detect departures from the null hypothesis
c. Power influences statistical test being performed, sample size, size of experimental effects, level of error
d. Anything that increases accuracy and consistency increases power

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8
Q

inference

A

drawing conclusions about a population using sample information

a. Can only make inference relative to the population from which experimental units were chosen

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9
Q

experimental unit

A

smallest unit of sample population treated alike

a. The experimental unit is what the treatments were randomly assigned to
b. The observational unit is what the response measurements were taken on
a. Pig, pen

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10
Q

fixed effects

A

contribute variation, repeatable in other study

a. Categorical, discrete variables
b. Treatments
a. Line, diet, line*diet

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11
Q

random effects

A

selection by chance, randomly chosen from a population, not repeatable in another study

a. Animal ID
a. Week, handler

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12
Q

nesting

A

factor levels within levels of another factor

a. Season within a year within a herd
a. Handler(pig)

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13
Q

covariates

A

contributes variability that should be accounted for

a. Continuous variables (can be any real number within a reasonable range)
b. Weight, temperature
a. BW, baseline cortisol concentration

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14
Q

standard deviation

A

measure of variability for an individual observation

a. Used to indicate how widely individuals in a group vary

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15
Q

standard error

A

measure of variability of the sample mean

a. SD/√n
b. Standard deviation of the sampling distribution
c. More variation, need more observations (higher n) in order to detect differences

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16
Q

P value

A

probability that the observed relationship (between variables) or a difference (between means) in a sample occurred by chance alone

a. Measure of statistical significance
b. The degree to which the result is true, the likelihood of observing certain data given the null hypothesis is true
c. P < 0.05 indicates 5% risk of the differences occurring without treatment effect
a. P < 0.05 can reject the null hypothesis

17
Q

TYpe I and Type II errors

A

a. Type I- accept null hypothesis when it is not true (false positive)
b. Type II- reject null hypothesis when it is actually true (false negative)

18
Q

F value

A

ANOVA, tests variance against a null hypothesis

a. Type I sum of squares- sequential sum of squares, tests main effects for factors followed by the interaction effect
b. Type II sum of squares- tests for the presence of an effect after all other effects have been accounted for, only valid if significant interactions are included in the model (better for unbalanced data)

19
Q

least square means

A

adjusted treatment means accounting for all other effects in the model

a. Least square means that the overall solution minimizes the sum of the squares of the errors made in the results of every single equation
b. Effects of predicted values of dependent variable for each level of the effect when all other effects are set to their mean values