Bistatistics Flashcards

1
Q

Statistics

A

The science dealing with the systematic gathering and analysis of data in medical forms

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2
Q

Bistatistics

A

Applications of statistics in biology, Medicine, and public health

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3
Q

Dichotomous

A

Two categories Example: Affected (positive) unaffected (Negative) Also known as binary

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4
Q

Ordinal

A

3 or more ranked categories Normal affected (high) severely affected (very high)

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5
Q

Nominal

A

Qualitative eye color, blood groups

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6
Q

Quantitative

A

Interval, ration

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7
Q

Pearson test

A

Two intervals Is there a linear relationship? -1< r < 1 spearman correlation compares two ordinal variables

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8
Q

Chi-square

A

2 nominal any number of groups

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9
Q

T-test

A

1 interval, 1 nominal 2 groups

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10
Q

One way Anova

A

1 interval, 1 nominal 2 or more groups, F-Statistic

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11
Q

Match pairs t-test

A

1 interval, 1 nominal 2 groups, linked data pairs

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12
Q

Repeated measures Anova

A

1 interval, 1 nominal more than 2 groups, linked data, F-statisitc

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13
Q

Standar deviation

A

Population mean = mu, sd = sigma, variance = sigma squared Sample X, sd, variance = s squared

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14
Q

Sample variance

A

s(squared) = 1/(n-1) * sum (Xi-X)squared

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15
Q

Standard Variable (Z)

A

measures the deviation of X from the mean in terms of standard deviation Population Z = X-mu/sigma Sample Z= X-Xmean/s

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16
Q

Frequency distribution

A

Qualitative or discrete: Bar Quantitative discrete: Bar, pie Continuous: Histogram, Bar Chart Tren: Quantitative: Line

17
Q

Probability (Mutually Exclusive)

A

Combine probabilities for mutual exclusive events by addition

18
Q

If two evens are not mutually exclusive

A

Combination of probabilities is accomplished by adding the two together and subtracting out the multiplied probabilities

19
Q

Combined probabilities for independent events by multiplication

A

Events are independent if the occurrence of one tells you nothing about the occurrence of another. The issue here is the intersection of two sets.

20
Q

if events are dependent

A

Multiply the probability of one event by the probability of the second, assuming that the first has occurred