Statistics
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| Define population | The whole set of items that are of interest
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| Define sample | Some subset of the population intended to represent the population
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| Define sampling unit | Each individual thing in the population that can be sampled
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| Define sampling frame | Often sampling units of a population are individually named or numbered to for a list
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| Define census | Data collected from the entire population
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| Define simple random sample | -Each sample has an equal chance of selection
-Each item has number
-Random number generator
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| Advantages of simple random sample | -No bias
-Easy
-Cheap
-Equal selection chance
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| Disadvantages of simple random sample | -Not suitable for large population
-Sampling frame needed
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| Define systematic sample | -Elements ordered into list
-Every kth element
-k=pop size/samp size
-Start at random number between 1 and k
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| Advantages of systematic sample | -Simple
-Quick
-Suitable for large populations
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| Disadvantages of systematic sample | -Sampling frame needed
-Can introduce bias is sampling frame is not random
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| Define stratified sample | -Population divided into strata
-Simple random sample for each group
-Samp size/pop size sampled from each group
-Used when sample is large and divided into groups
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| Advantages of stratified sample | -Reflects population structure
-Proportional representation within population
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| Disadvantages of stratified sample | -Population clearly classified into strata
-Selection within strata suffer from same disadvantages as simple random
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| Define quota sample | -Population divided into groups according to sampling frame
-Interviewer selects quotas to reflect groups proportions
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| Advantages of quota sample | -Small sample is still representative
-Easy
-Cheap
-Comparable
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| Disadvantages of quota sample | -Can introduce bias
-Population divided into groups
-Non responses not recorded
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| Define opportunity sample | Sample taken from people at the time, who meet criteria
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| Advantages of opportunity sample | -Easy
-Cheap
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| Disadvantages of opportunity sample | -Not representative
-Dependant on researcher
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| What is the equation for a stratified sample | Strata size x sample size/total population
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| What is the difference between qualitative and quantitative data | Qualitative- Descriptive
Quantitative- Numerical
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| What is the difference between discreet and continuous data | Discreet- Only takes certain values
Continuous- Takes all values
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| How do we find outliers | Greater than Q3+k(Q3-Q1)
Less than Q1-k(Q3-Q1)
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| Define cleaning the data | Removing outliers
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| What do we plot for cumulative frequency diagrams | End point against cumulative frequency
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| What is the equation for frequency density | Frequency density=(Frequency x k)/Class width
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| When do we use a histogram | Continuous data
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| When do we use a bar chart | Discreet data
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| What do we comment on when comparing data | -Measure of location
-Measure of spread
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| What axis is the independent variable on | X
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| What axis is the dependent variable on | Y
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| Define bivariate | There are pairs of values for two variable
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| Define causal relationship | Change in one variables causes a change in the other
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| Define interpolation | Estimating a variable within the data range
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| Define extrapolation | Estimating a variable outside the data range
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| What is the purpose of a regression line | To minimise standard deviation
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| When can we use regression lines | For data within the data range
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| Define mutually exclusive | If one event happens the other events can't happen
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| If events are mutually exclusive: P(A or B)= | P(A)+P(B)
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| Define independent events | One event has no effect on the other
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| If events are independent P(A and B)= | P(A) x P(B)
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| Define random variable | A variable whose value depends on the outcome of a random event
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| Define discreet uniform distribution | All probabilities are the same
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| ΣP(X=x)= | 1
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| When can you model a random variable with a binomial distribution | -Fixed no of trials(n)
-2 possible outcomes
-Fixed probability of success (P)
-Trials are independent of each other
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| P(X<Y)= | P(X≤Y-1)
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| P(X≥Y)= | 1-P(X≤Y-1)
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| P(X>Y)= | 1-P(X≤Y)
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| Define test statistic | The result of the experiment or the statistic that is calculated
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| Define null hypothesis | The hypothesis you assume to be correct
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| Define alternate hypothesis | Tells you about the parameter if your assumption is wrong
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| Define critical region | A region of the probability distribution which if the test statistic falls within it would cause you to reject the null hypothesis
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| Define critical value | The first value to fall in the critical region
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| What is the actual significance level | The probability of incorrectly rejecting the null hypothesis
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| What are the steps of a one tailed hypothesis test | -Formulate a model for test statistic
-Identify suitable null and alternate hypotheses
-Calculate the probability of test statistic being observed assuming null hypothesis is true
-Compare to significance level
-Write conclusion in context of question
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| What must you do for a two tailed hypothesis test | Halve the significance level
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| If y+ax^n then logy= | loga+nlogx
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| If y=kb^x then logy= | logk+xlogb
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| What does the PMCC describe | The strength and direction of the correlation
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| When can the PMCC be used | If there is LINEAR correlation
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| If we are hypothesis testing for correlation what are the null and alternate hypothesis | H0: p=0
H1: p≠0
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| How do we write, the probability that B occurs given that A has already occurred | P(P|A)
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| What is the rule for independent events and conditional probability | P(A|B)=P(A|B')=P(A)
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| P(A)+P(B)-P(A∩B)= | P(A∪B)
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| (P(B∩A))/(P(A))= | P(B|A)
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| What are the probability symbols for and and or | And=∩
Or=∪
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Created by:
Liam P
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