statistics

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Population

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50 Terms

1

Population

The whole set of items that are of interest

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2

Census

Observes or measures every member of a population

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3

Advantages of a census

It should give a completely accurate result

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4

Disadvantage of a census

Time consuming and expensive Hard to process large quantity of data

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5

Sample

A selection of observations taken from a subset of the population which is used to find out information about the population as a whole

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6

Advantages of a sample

Less time consuming Fewer people have to respond Less data than a census

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7

Disadvantage of a sample

The data may not be as accurate The data may not be large enough toggle information about the population

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8

Sampling Units

Individual units of a population

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9

Sampling Frame

A list of individually named or numbered sampling units

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10

Simple Random Sample

A sample where every sampling unit has an equal chance of being chosen

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11

Advantages of Simple Random Sample

Free of bias Easy and cheap to implement Each sampling unit has equal chance of selection

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12

Disadvantage of Simple Random Sample

A sampling frame is needed Not suitable when the population size is very big

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13

Systematic Sampling

A method of sampling where the required elements are chosen at regular intervals from an ordered list

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14

Advantages of Systematic Sampling

Simple and quick to use Suitable for large samples

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15

Disadvantages of Systematic Sampling

A sampling frame is needed It can introduce bias if sampling frame is not random

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16

advantages of Stratified Sampling

Sample accuracy reflects the population structure guarantees proportional representation of groups within a population

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17

Disadvantages of Stratified Sampling

Population must be classified into distinct strata

Selection within each stratum can be time consuming and expensive if sample size is large

A sampling frame is needed

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18

Stratified Sampling

A method of sampling where the population is divided into mutually exclusive strata and a random sample is taken from each

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19

The number sampled in a stratum

(Number in stratum/number in population)* overall sample size

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20

Quota Sampling

A method of sampling where a researcher selects a sample that reflects the characteristics of the whole population

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21

Advantages of Quota Sampling

Allows a small sample to still represent the whole population No sampling frame required Quick, easy and inexpensive Allows for easy comparison between different groups in the population

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22

Disadvantages of Quota Sampling

Not random sampling can produce bias Population must be divided into groups which can be costly or inaccurate Non-responces are recorded as such

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23

Opportunity Sampling

A method of sampling where the people sampled are those who are available at the time the study is carried out and who fit the criteria you are looking for

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24

Advantages of Opportunity Sampling

Easy to carry out Inexpensive

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25

Disadvantages of Opportunity Sampling

Unlikely to provide a representative sample Highly dependent on individual researcher

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26

Quantitative Variables/Data

Variables or data associated with numerical observations

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27

Qualitative Variables/Data

Variables or data associated with non-numerical observations

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28

Continuous Variable

A variable that can take any value in a given range

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29

Discrete Variable

A variable that can take only specific values in a given range

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30

Class Boundaries

The maximum and minimum values that belong in each class

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31

Midpoint

The average of the class boundaries

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32

Class Width

The difference between the upper and lower class boundaries

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33

Interpercentile range

The difference between the values for 2 given percentiles.

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34

Cleaning the data

The process of removing anomalies from a data set.

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35

bivariate data

Data which has pairs of values for two variables

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36

Correlation

A measure of the linear relationship between two variables

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37

regression line

a line of best fit, y = a+bx

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38

mutually exclusive events

events that have no sample points in common , P(A or B) = P(A) + P(B)

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39

independent events

The outcome of one event does not affect the outcome of the second event , P(A and B) = P(A) x P(B)

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40

tree diagram

A diagram used to show the total number of possible outcomes

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41

probability distribution

Describes the probability of any outcome in the sample space.

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42

binomial distribution

When there are a fixed number of trials There are 2 possible outcomes (success or failure) There is a fixed probability of success The trials are independent of each other

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43

the null hypothesis

The hypothesis you assume to be correct

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44

alternative hypothesis

Tells us about the parameter if your assumption is wrong

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45

critical region

the area in the tails of the comparison distribution in which the null hypothesis can be rejected

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46

mutually exclusive

Events have no outcomes in common, they can't happen at the same time

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47

independent

When one event has no effect on another

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48

If events A and B are independent…

P(A intersection B)= P(A)x P(B)

P(A|B)=P(A)

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49

If events A and B are mutually exclusive

P(A intersection B)= 0

P(A union B)= P(A)+P(B)

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50

When A and B are not mutually exclusive, P(A union B)=

P(A)+ P(B)- P(A intersection B)

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