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Bio Stats > Test 1 > Flashcards

Test 1 Flashcards

1
Q

Descriptive Statistics

A

Graphs and numerical summaries

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2
Q

Inferential Statistics

A

Draw conclusions

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3
Q

Process of Statistics:

A
  • Present question
  • Gather data
  • Summarize data
  • Draw conclusions
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4
Q

Categorical/Qualitative Variable

A

Places an individual into one of several groups or categories based on some quality of the individual

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5
Q

Quantitative Variable

A

Takes numerical values for which arithmetic operations such as adding and averaging make sense
- Usually recorded with a unit of measurement

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6
Q

Discrete Variable

A

A quantitative variable that only takes on a limited, finite number of values.
- Often when something is counted
- Can be subdivided

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7
Q

Continuous Variable

A

A quantitative variable that can take on any real numerical value over an interval
- Often when things can be measured
- Decimals

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8
Q

Nominal Variable

A

A categorical variable in which the categories cannot be ordered
- Independent
- Ex.) Color

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9
Q

Ordinal Variable

A

A categorical variable in which the categories can be ordered, ranked, or have a relationship to one another

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10
Q

Experiment

A

A study in which the researcher imposes conditions on the subjects of the study

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11
Q

Observational Study

A

A study in which the researcher collects data without imposition of specific conditions

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12
Q

Sampling Design

A

Describes exactly how to choose a sample from a population

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13
Q

Probability Design

A

A sample chosen by chance

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14
Q

Simple Random Sample (SRS)

A

A sample of size (n) that consists of (n) amount of individuals from the population, chosen in such a way that:
- Every individual in the population has an equal chance of being selected
- Every subset has the same chance of being selected

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15
Q

How to take an SRS?

A
  • 1) Assign each member of the sampling group a unique numerical label
  • 2) Use a random number generator to select individuals
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16
Q

Stratified Samp

A
  • Divide the population into subgroups (strata) that have some common characteristic
  • For each stratum, obtain a SRS of size that is proportional to the size of the stratum
  • Use all individuals obtained in step 2 as the sample
  • Use when groups are homogenous
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17
Q

Cluster Sample

A
  • Divide the population into subgroups (clusters) that share some common characteristic
  • Obtain an SRS of the clusters (the group in entirety)
  • Use all members of the clusters selected in step 2 as the sample
  • Use when groups are heterogeneous
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18
Q

Response Variable (Dependent)

A

Measure the outcome of the study
- What is changed

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19
Q

Explanatory Variable (Independent)

A

May explain or influence changes in the response variable
- What is being adjusted in order to measure the outcome

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20
Q

Observations can only reveal what?

A

Associations

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21
Q

Well-designed experiments can reveal what?

A

Causations

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22
Q

Lurking Variable

A

A variable that is not an explanatory or response variable but still may impact the relationship between the explanatory and response variables
- Ex) A scientist is studying the effect of a good diet and exercise on heart rate and blood pressure, however, whether or not the person being studied is a smoker and their stress levels could be lurking factors

23
Q

Confounding Variables

A

When the effects of two variables on the response variable cannot be distinguished from each other

24
Q

Factor(s)

A

The explanatory variables controlled by the experimenter

25
Q

Treatment

A

Any specific experimental condition applied to the subjects
- If an experiment has more than one factor, a treatment is a combination of the specific values of each factor

26
Q

Designs must do what?

A

Compare something
- A control and experimental group

27
Q

Replication

A

The repetition of an experimental condition (treatment) so that the variability associated with the phenomenon can be estimated
- The # of replicates is the number of experimental units to which the treatment is applied

28
Q

Randomized Comparative Experiment

A

An experiment that uses both comparison of two or more treatments and random assignment of subjects to treatments

29
Q

Statistically Significant

A

An observed effect so large that it would rarely occur by chance
- Evidence that the result seen in the sample also exists in the population

30
Q

Block

A

A group of individuals with some common characteristic thought to have a significant impact on the response

31
Q

Completely Randomized Designs

A

All of the individuals are allocated at random among the treatments
- It is not necessary, but often done, to assign the same number of individuals to each treatment

32
Q

Randomized Complete Block Design

A

The random assignment of individuals to treatments is carried out separately within each treatment

33
Q

Matched Pairs Design

A

Uses a form of blocking to compare just two treatments (often control/trt)
- Pairs of subjects (experimental units) are chosen such that they are as closely matched as possible

34
Q

Distribution

A

Tells us what values the variable takes and how often it takes those values

35
Q

Categorical/Qualitative Graphs

A

Bar graphs and Pie charts

36
Q

Single-Variable Quantitative Graphs

A
  • Boxplots
  • Histograms
  • Dot-plot
  • Stem-plot
37
Q

Two-Variable Quantitative Graphs

A
  • Scatterplots
  • Time Series Plots
38
Q

Right Skew

A

Graph moves to the left

39
Q

Left Skew

A

Graph moves to the right

40
Q

Center

A

Described using the mean and median

41
Q

Spread (Variation)

A

Described using range, IQR, and variance/standard deviation

42
Q

Mode

A

The vale in a dataset that occurs most often

43
Q

Storing in R

A

x = c(#, #, #, . . . #)

44
Q

Mean in R

A

mean(x)

45
Q

Median in R

A

median(x)

46
Q

Resistance

A

When a statistic is not sensitive to the influence of a few extreme outliers

47
Q

Is the median resistant to outliers?

A

Yes

48
Q

Is the mean resistant to outliers?

A

No

49
Q

Range

A

The difference between the maximum and minimum values in the dataset

50
Q

Interquartile Range (IQR)

A

The range for the center half of the data
- The difference between the third and first quartiles

51
Q

Sample Variance

A

Used to find the variation about them mean
- 1/n-1 (SUM (Xi - X)^2

52
Q

Sample Standard Deviation

A

Measures the spread about the mean and should only be sued when the mean is chosen as the measure of center
- The square root of the sample variance

53
Q

Empirical Rule

A

For a symmetrical, ‘bell-shaped’ distribution, approximately 68%, 95%, and 99.7% of the observations fall within one, two or, three standard deviations respectively on either side of the mean

54
Q
A

Bio Stats (2 decks)

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