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Biostats > Exam 2 > Flashcards

Exam 2 Flashcards

1
Q

why would you used t instead of z?

A

z is a theoretical distribution

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2
Q

df

A

n-1

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3
Q

how do you add more uncertainty?

A

replace the funky o with an s in the SEM equation

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4
Q

what’s the other name for a t-distribution?

A

Student’s t-distributions

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5
Q

how are t and z-distributions different?

A

t has more area under the curve tp accommodate more uncertainty

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6
Q

how are t and z-distributions alike?

A

both have normal, bell-shaped curves

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7
Q

as df gets bigger how does that affect a t distribution?

A

looks more like a z distribution

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8
Q

how is a t table organized?

A

rows are df

columns are probabilities

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9
Q

single samples

A

no control group, one group of people, used to establish norms

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10
Q

paired samples

A

one group of people but use two different treatments

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11
Q

independent t-test

A

2 groups with different treatment but doesn’t assume equal variance

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12
Q

point estimator

A

difference between sample means (X1-X2)

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13
Q

what are the 2 ways to calculate degrees of freedom?

A

Welsh method and conservative method

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14
Q

f-test or Levene’s test

A

variance test to see if two samples are similar

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15
Q

what do you do if two samples are similar?

A

use an equation that uses a combined variance estimate (gives more degrees of freedom)

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16
Q

what do you do if two samples aren’t similar?

A

use less degrees of freedom

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17
Q

ANOVA

A

one-way analysis of variance

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18
Q

ANOVA definition

A

test group means for a significant difference

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19
Q

2 components of ANOVA

A

variance between groups and variance within groups

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20
Q

MBS

A

mean square between

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21
Q

MBS definition

A

quantifies the variance of group means around the group mean (variance between groups)

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22
Q

MSW

A

mean square within

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23
Q

MSW definition

A

quantifies the variability of data points in a group around its mean (estimate of the variance within groups)

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24
Q

f-statistic

A

ratio of the MSB and MSW

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25
Q

post hoc hypothesis

A

formal tests that are used in delineating

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26
Q

2 methods of post hoc hypothesis

A

least squares difference (LSD) method and bonferroni method

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27
Q

LSD method

A

only used after a significant ANOVA test and planned comparisons

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28
Q

Bonferroni’s method

A

ensures that the family-wise error rate is less than or equal to alpha after all possible pair-wise

29
Q

homoscedastic

A

equal in variance

30
Q

heteroscedastic

A

unequal in variance

31
Q

three methods of assessing group variances

A
  • graphical exploration
  • summary statistics
  • hypothesis tests of variance
32
Q

scedastic

A

variance of a random variable

33
Q

nonparametric tests

A

encompass a broad array of statistical techniques used to analyze data

34
Q

rank tests

A

class of nonparametric test that make fewer assumptions about distributional shape

35
Q

Kruskal-Wallis test

A

nonparametric analogue of one-way ANOVA

36
Q

family-wise error rate

A

probability of at least one false rejection of null hypothesis

37
Q

how can you increase the alpha error

A

multiple tests

38
Q

when do you reject the null hypothesis

A

> 0.05 or the range doesn’t include the null

39
Q

what test do you use if you don’t know the direction of the alternative hypothesis?

A

two-tailed

40
Q

confounded correlation

A

looks like correlation but there’s a 3rd thing that causes the correlation

41
Q

regression

A

how much x explains y

42
Q

LINE

A

linearity, independent observations, normality, equal

43
Q

what’s the slope if there’s no correlation?

A

0

44
Q

what does correlation only apply to?

A

linear relationships

45
Q

what do you split the Y value into?

A

residual and predicted

46
Q

explanatory variable (x)

A
  • independent variable
  • factor
  • treatment
  • exposure
47
Q

response variable (y)

A
  • dependent variable
  • outcome
  • response
  • disease
48
Q

correlation coefficient

A

r

49
Q

least squares regression line

A

y=a+bx

50
Q

simple regression

A

single explanatory variable (X) and response variable (Y)

51
Q

multiple regression

A

multiple explanatory variables (X1, X2 etc) in relation to a response variable (Y)

52
Q

k

A

number of explanatory variables

53
Q

standardized coefficients

A

predicted change in Y per unit increase in X

54
Q

residual

A

difference between observed response and response predicted by regression model

55
Q

why do we use multiple regression models?

A

helps to “adjust out” the effects of lurking variables

56
Q

what type of variable is an ANOVA?

A

categorical explanatory variable, quantitative response variable

57
Q

does correlation mean causation?

A

hell nah

58
Q

coefficient of determination

A

r^2

59
Q

CoD

A

amount of y that is explained by x

60
Q

distance of point to the line

A

residual error

61
Q

slope

A

change in y per unit of x

62
Q

when do you create dummy variables?

A

when there are 3+ levels

63
Q

how many dummy variables should there be?

A

number of levels - 1

64
Q

SEM

A

standard error of x-bars

65
Q

does the 95% CI get smaller as n increases?

A

ya

66
Q

when do you use a two-tailed test?

A

when you don’t know the direction of the alternative

67
Q

when is it easier to reject the null?

A

when the variances are equal

68
Q

when can you use a t-test?

A

when the data is normal and the n is large

69
Q

family-wise error rate

A

probability of making a type 1 error

Biostats (3 decks)

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