Sampling, Data Presentation And Interpretation Flashcards

1
Q

What is the IV and DV

The total weight in a lift and the number of people in the lift

A

IV= number of people in the lift

DV= total weight in the lift

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2
Q

what is defined as an outlier in terms of SD?

A

± 2SD from mean

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3
Q

what is defined as an outlier in terms of quartiles?

A

+ 1.5 IQR above UQ

-1.5 IQR below LQ

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4
Q

given you have 10 values how many modes would you have to have for the data to be evenly spread?

A

close to 10 or 0

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5
Q

How do you calculate variance from SD?

A

SD^2

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6
Q

How is the area under a histogram linked to the frequecy?

A

Proportional

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7
Q

What is the difference between interpolation and extrapolation?

A
inter= estimating within data set 
extra = estimating outside data set
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8
Q

What are the different sampling techniques

A
Random
Stratified
Systematic
Cluster
Opportunity
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9
Q

Describe random sampling

A

Data chosen at random

Repeats ignored

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10
Q

Describe Systematic sampling

A

Choosing data at regular intervals

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11
Q

Describe cluster sampling

A

Data is grouped into clusters and you choose clusters

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12
Q

Describe Stratified sampling

A

Data is proportional (combinen with random to get best results) to population

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13
Q

Describe opportunity sampling

A

Data selected from population that are available and fit the criteria

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14
Q

Advantages of random sampling

A

Fair and unbiased

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15
Q

Advantages of Systematic sampling

A

Unlikely to be bias

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16
Q

Advantages of cluster sampling

A

Quick and efficient

17
Q

Advantages of Stratified sampling

A

Best way to fairly reflect pop

18
Q

Advantages of opportunity sampling

A

Can be done immediately

19
Q

Disadvantages of random sampling

A

Can be time consuming

Impractical on large scale

20
Q

Disadvantages of Systematic sampling

A

May not be random

21
Q

Disadvantages of cluster sampling

A

May be bias

22
Q

Disadvantages of Stratified sampling

A

Time consuming

Impractical on large scale

23
Q

Disadvantages of opportunity sampling

A

May be bias