biostats

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categorical data

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  • ordinal

  • not ordinal

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what is not ordinal data

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data you canNOT sort

  • blood type

  • geo location

  • sex

  • colors

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81 Terms

1

categorical data

  • ordinal

  • not ordinal

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2

what is not ordinal data

data you canNOT sort

  • blood type

  • geo location

  • sex

  • colors

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3

what is ordinal data

data you CAN sort

  • numbers

  • dates

  • size

  • time

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4

what is quantitative data

  • discrete

  • continuous

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5

what is discrete data

can list all possible values (finite)

  • # of eggs

  • colonies in agar

  • people in a room

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6

what is continuous data

you cannot count/list all possible values (this largely depends on how accurate data is)

  • age

  • repeating numbers (pi)

  • truly accurate weights/heights

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7

in a histogram do the bars touch

yes

represents continuity

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8

in a bar plot do the bars touch

no

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9

what kind of data does a histogram represent

continuous data only

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10

what are the types of distributions

  • symmetrical

  • long tail

  • skewed right

  • skewed left

  • exponential

  • bimodal (2 modes)

  • uniform

  • u shaped

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11

what is “Y”

random variable

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12

what is “y”

value of random variable

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13

what is “n”

sample size

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14

what is “yi”

value of y in the ith place in sample

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15

what is a minimun

lowest number in sample

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16

max

largest number in sample

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17

mode

most common # in sample

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median

middle # in sample after data had been ordered from lowest to highest

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mean

average

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range

max - min

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21

avg absolute deivation

how far away is data from average

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22

variance

how spread out data is from each other

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standard deviation

how spread out data is compared to mean

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24

when do you use median over mean

when a sample had MAJOR outliers

  • income in Seattle WA: mean=1.2 mill median=500 thousand

  • bill gates income skews data so mean is no longer accurate

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25

what are the steps to making a box plot

  1. find median

  2. find Q1 and Q1

    • split data into 2 parts at median.. find median of those 2 parts

  3. get IQR=(Q3-Q1)

  4. find 1.5x IQR

  5. Find upper fence (if data above this=outlier)

    • Q3 + 1.5xIQR

  6. find lower fence (if data below this=outlier)

    • Q1 - 1.5xIQR

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μ

pop mean

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σ

pop standard deviation

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p

population

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29

ȳ

sample mean

  • estimates μ

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30

s

sample standard devation

  • estimates σ

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31

sample population

  • estimates p

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32

random sampling

everyone has equal chance of being picked

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33

systematic sampling

measure values in set increments

  • every 20th person

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34

stratified sampling

sample proportional to data

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35

opperuntisitic sampling

sample everything as you come across it

  • can be biased

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36

what is basic probability

number of time event can occur / number of possible outcomes

number of time you WANT event to occur / number of time event CAN occur

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37

what is the range a prob can be

0-1

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38

what does P{E} mean

probability of event E

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39

what can do you if 2 probabilities are independent

  • Pr (H and roll 6)

multiply them

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40

does conditionally prob change outcomes. if so, how?

yes

states a condition that must happen

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41

what can you use the binomial coefficient for

finding how many different ways tings can be arranged

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42

what can the binomial distribution formula be used for

finding prob of an event

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43

what are the binomial distributions parameters

sample size

probability

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44

what makes a distribution

all possible outcomes add to one

parameters determine shape

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45

what are the parameters of the normal distrubtion

standard deviation (pop)

pop mean

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46

what is the central limit theorem (CLT)

calc prob using normal distribution no matter original distribution

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47

why do we use the normal distrbution

many things in bio are approx normal

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48

what do we need to make the standard normal curve

z

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49

what examples of continuous data distributions

T distrib

chi square

normal

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50

what are discrete data distrib

binomial

poisson

unifrom

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51

what do you need in order to calc the poisson distrib

pop mean

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52

how is ȳ typically distrubted

normal distrib

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53

what do you do if you don’t know the pop standard deviation

use sample standard deviation

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54

what is SEȳ

standard error for ȳ

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noise

Variation in the data we are interested in

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56

Statistics

a value calculated or derived from the data

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57

does a bar plot represent continuous data?

no— its discrete data

show by not touching bars

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58

how would you find Pr(z>-2.0)

a) 1- Pr(z>-2.0)

b) Pr(z< 2.0).. look up 2 rather than -2

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59

Know when statistics might be appropriate (or inappropriate). Be able to give an example of each.

appropriate: analyze medical data

inappropriate: to lie using false/unspecific stats

  • 90% effective with no parameters

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60

Be able to give an example of an application of statistics

weather forecasting

testing drug effectiveness

election polling

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61

What is a record ( = observational unit = case)?

individual entity or subject that data is collected on

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62

Why do we use n - 1 in the denominator for the variance (and standard deviation)? Why not just n?

provides a more accurate estimate of the population variance by correcting for the bias introduced when using the sample mean to estimate the population mean

  • samples tend to be closer to the center than pop data

  • add extra room to var to account for this error

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63

What is the difference between a sample and a population?

sample = section of pop

pop= whole

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64

When is sampling better than trying to measure everything?

more efficient/cost effective and just as accurate

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65

Why is it so important to define a population precisely?

students at gmu is very different than student at gmu taking biostats

  • defined pop helps narrow targeted group

  • minimize risk bias

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66

Why do we sometimes have to be careful with studies done in zoos or labs?

limited sampled size can lead to errors

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67

Know the difference between estimates and parameters

parameter: specific characteristic of an entire population

estimate: calc based on sample to approx true pop parameter

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68

What does the ^ (hat) symbol mean?

estimated value

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69

How would you do random sampling?

  1. define your population

  2. determine your desired sample size

  3. assign a unique number to each member of the population

  4. use a random number generator or lottery to select the individuals that will be included in your sample

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70

Are the random numbers generated by a computer truly random? Why, or why not?)

no. random number are given based on algorithm

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71

What is the effect of sample size?

larger than sample = more precise and higher confidence

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72

Why is probability important to statistics?

can determine the the probability of getting this result by chance.

If this probability is low, we say the experiment had an effect on the outcome

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what is a distribution

function that shows how values are spread out across a range of possible values

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74

How does the shape of a binomial distribution depend on n and p?

  • p = 0.5: Symmetrical distribution regardless of n. 

  • p < 0.5: Skewed right. 

  • p > 0.5: Skewed left. 

  • Large n: Even when p is not exactly 0.5, the distribution tends to appear more symmetrical due to the "central limit theorem" effect. 

  • Small n: The skewness caused by p value is more pronounced

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75

How does the shape of a normal distribution depend on μ and σ

μ:

  • increases= entire curve shifts to the right

  • decreases= curve shifts to the left

σ

  • larger= creates a wider, flatter curve

  • smaller= narrower, steeper curve

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How does area relate to probability (for a continuous distribution)?

area under a curve = prob{e}

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77

How do you use the normal distribution? What table do you use

z score table

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78

What is reverse lookup?

looking up the z area and finding the z score from that

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79

What is the mean (μ) of a binomial distribution? What is the standard deviation (σ)?

μ= np

σ= sqrt[np(1-p)]

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What is the mean (μ) of a normal distribution? What is the standard deviation (σ

mean=0

standard deviation= 1

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