Introduction to Statistics Flashcards
1
Q
Statistics - 2
A
- The science of data
- Collecting, organising, analysing, interpreting and presenting data
2
Q
Population - 1
A
- Group of “objects” of which we are looking to gather information on
3
Q
Census - 1
A
- Collection of data from every member of population
4
Q
Sample - 3
A
- Subcollection of the population
- Different samples may lead to different conlusions about population
- Samples have to be representative and unbiased
5
Q
Statistical Study Phases - 3
A
- Prepare
- Analyse
- Conclude
6
Q
Prepare phase - 3
A
- Context
- Source of data
- Sampling method
7
Q
Analyse phase - 3
A
- Graph data, using appropriate graphs
- Explore data qualitatively and quantitatively
- Apply statistical methods
8
Q
Sampling Methods - 7
A
- Voluntary sampler response
- Random sample
- Simple random sample
- Systematic sampling
- Convenience sampling
- Stratified sampling
- Cluster sampling
9
Q
Voluntary sampler response - 2
A
- Subjects decide themselves to be included in sample
- Biased
10
Q
Random sample - 2
A
- Each member of population has equal probability of being included
- Unbiased
11
Q
Simple random sample - 3
A
- Each sample of size n has same probability of being selected
- Unbiased
- Difficult for large populations
12
Q
Systematic sampling - 2
A
- After a starting point select every k-th member
- Could be biased, by changing starting point
13
Q
Convenience sampling - 3
A
- Choose the most easily available sample
- Biased
- Not a good method but could be useful for first impressions
14
Q
Stratified sampling - 2
A
- Divide population in subgroups (strata) such that subjects in same subgroup have same char. then draw a random sample from each group
- Not biased, very representative
15
Q
Cluster sampling - 2
A
- Divide population in clusters and randomly select the entire cluster
- Could be biased in small datasets
16
Q
Variable - 1
A
- A quantity which can vary
17
Q
Cause and effect studies terms - 3
A
- Explanatory variable
- Response variable
- Confounding
18
Q
Explanatory variable - 2
A
- Independent
- Might cause the effect being studied
19
Q
Response variable - 2
A
- Dependent
- Represents the effect being studied
20
Q
Confounding - 1
A
- Influence of different explanatory variables mix and cannot be distinguished
21
Q
Types of study - 2
A
- Observational study
- Experiment
22
Q
Observational study - 2
A
- Subjects are observed but not modified
- Type of study defined by when the data is obtained
23
Q
Retrospective Observational - 1
A
- Data is collected from the past
24
Q
Cross-sectional Observational - 1
A
- Data is collected from one point in time
25
Prospective Observational - 1
- Data has to be collected
26
Experiment - 2
- A certain treatment is applied to the subjects
| - A control and treatment method can be applied
27
Control and Treatment Experiment - 2
- Single blind, the subject does not know which is treatment and which is placebo
- Double blind, subject and researcher do not know which is treatment and which is placebo
28
Types of data - 2
- Parameter
| - Statistic
29
Parameter data - 2
- Numerical measure which represents a a characteristic of a population
- Represented with greek symbols
30
Statistic data - 2
- Numerical measure which represents a characteristic of a sample
- Represented with small letters
31
Categories of data - 2
- Quantitative (Numerical), numbers
| - Qualitative (Categorical), names or labels
32
Discrete data - 2
- Numerical data
| - Number of possible values is countable
33
Continuos data - 2
- Numerical data
| - Collection of values is not countable
34
Level of measurement - 1
- Nominal
- Ordinal
- Interval
- Ratio
35
Nominal - 3
- Qualitative data level of measurement
- Cannot be ordered (Names, labels)
- Cannot be used for computations
36
Ordinal - 3
- Qualitative data level of measurement
- Can be ordered but no meaningful differences
- Cannot be used for computations
37
Interval - 3
- Quantitative data level of measurement
- No natural zero or starting point
- Ordering and difference between number are meaningful
38
Ratio - 3
- Quantitative data level of measurement
- There is a natural starting point
- Ordering is possible and differences are meaningful
