Applied maths Flashcards

(43 cards)

1
Q

Definition of population

A

Whole set of items that are of interest e.g. items manufactured in a factory or people living in a town

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2
Q

Definition of raw data

A

Information obtained by a population

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3
Q

Definition of census

A

Observes or measures every member of a population

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4
Q

Definition of parameter

A

Memorable characteristic of a population e.g. mean or standard deviation

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5
Q

Definition of sample

A

Selection of observations taken from a subset of the population which is used to find out info about the population as a whole

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6
Q

Definition of statistic

A

Single measure of some attribute of a sample e.g. mean value

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7
Q

Advantage of census vs sample

A

Complete and more accurate result

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8
Q

Disadvantages of census vs sample

A

Time consuming
Expensive
Hard to process large quantities of data
Cannot be used when testing process destroys item

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9
Q

Advantages of sample vs census

A

Less time consuming
Cheaper
Easier to process
Fewer people have to respond

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10
Q

Disadvantages of sample vs census

A

Less reliable

Could be biased

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11
Q

Different types of sampling methods

A
Simple random
Systematic 
Stratified 
Cluster 
Opportunity
Quota 
Self-selected
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12
Q

Simple random sampling

A

Any sampling method in which very member has an equal chance of being selected

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13
Q

Examples of simple random sampling

A

Numbering the population and using a random number generator

Selecting names from a hat

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14
Q

Systematic sampling

A

For a population of size N, to find a sample of size n we first set k=N/n. We now take a random member of the first k members, then take the kth member after that

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15
Q

Stratified sampling

A

This is when a population is divided into subgroups (called strata). A sample is then taken from each group of a size proportional to the group size

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16
Q

Cluster sampling

A

Used when a population can be divided into subgroups which are each reasonably representative of the whole population. Then we take a sample from just a few of those subgroups

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17
Q

Example of cluster sampling

A

Researcher wants to survey academic performance of students. Population could be divided by city and within the cities perform simple random or systematic
sampling

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18
Q

Opportunity sampling

A

This is used when you are unable to list a population. Member of a population are chosen for the sample as you have access to them

19
Q

Example of opportunity sampling

A

Asking members of the public you see first

20
Q

Quota sampling

A

This is used if you are unable to list a population, but you want to represent distinct groups within the sample. Use opportunity sampling until you have the specified size of sample for each group (or stratum)

21
Q

Example of quota sampling

A

Interviewers meet and assess people before allocating them into the appropriate quota. This continues until all quotas have been filled - if someone refuses or their quotas is full you move onto the next person

22
Q

Self-selected sampling

A

This is where the individuals in a population choose to be in a sample

23
Q

Frequency density

A

Frequency/ class width

24
Q

When is a distribution roughly symmetrical

A

Q2 - Q1 = Q3 - Q2

25
When is a distribution positively skewed
Q2-Q1
26
When is a distribution negatively skewed
Q2 - Q1 > Q3 - Q2
27
Outliers
``` Marked on box plot as asterisk Smaller than (Q1 - 1.5 * IQR) Larger than (Q3 + 1.5 * IQR) ```
28
Cumulative frequency diagrams
``` Plotted above the upper class boundaries of the intervals Points joined by straight line ```
29
Linear interpolation
``` To find the median: Lower class boundary + ((median value - values preceding median)/ values in interval) * class width ```
30
Sample space
Set of all possible outcomes | Example - in a test with 70 questions, the sample space for correct answers is {0, 1, 2, ..., 70}
31
Event
Collection of some of the outcomes from an experiment | Example - getting >40 on the quiz
32
Relative frequency
No. of times event occurs/ number of times experiment is repeated
33
Mutually exclusive
If two events can’t occur at the same time
34
Independent events
If the occurrence of one has no effect on the probability on the second occurring
35
Conditional probability
Probability of event A happening given that event B has happened
36
Correlation
Measure of relationship
37
Variables in scatter graph
``` Independent variable (explanatory) is horizontal Dependent (response) variable is vertical ```
38
Correlation coefficient
-1 is perfect negative correlation 0 is no correlation 1 is perfect positive correlation
39
Discrete data
Can take any one of a finite set of categories or values, but nothing in between those values. Often the values are different categories
40
Continuous data
Always numbering and can take any value between two points on a number line
41
Probability distribution
Random experiment shows how the total probability of 1 is distributed between all the possible outcomes
42
Discrete distribution
Shown in a bar chart - height of each bar represents probability Total height of all bars = 0
43
Conditions for binomial distribution
Two possible outcomes in each trial Fixed number of trials (n) Independent trials The probability of a success (p) is constant