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STTN Test 1
Ch 1-3
| Term | Definition |
|---|---|
| Statistics | Science of extracting info from data |
| Aspects of stats | Data collection Summarizing & graphical representation Drawing conclusions |
| Descriptive stats | Graphical & tabular methods to summarize and order data |
| Statistical interference | Methods to make conclusions about the population from sample data |
| Measurement | Assigning a numerical value to a property of an observed element |
| Validity of measurement | Leads to useful information concerning characteristics studied |
| Variable | Property of an observed element that varies from one element to the next |
| Discrete variable | Possible values are clearly distinguishable & disconnected from one another |
| Continuous variables | Values are not clearly distinguishable; can always find another value that lies between them |
| Types of scales | Nominal - Ordinal - Interval - Ratio |
| Nominal Scale | Classes or categories |
| Ordinal Scale | Classes or categories that have an order associated with them |
| Interval Scale | Ordinal Scale but with meaning attached to differences between values. Zero is arbitrary and does not imply absence. Ex time & temperature |
| Ratio Scale | Interval Scale but ratios between values have meaning. Ex mass, speed, height |
| Population | Complete group of items for which info is required (N) |
| Sample | Some subset of the population (n) |
| Probability samples | Based on principles of randomness or chance. Complex, more time consuming, but more reliable. Each element of N has a known probability of being included in the sample |
| Non-probability samples | Based on subjective considerations. Faster & cheaper but cannot be reliably measured. |
| Probability samples | Simple random samples, stratified random sampling, cluster sampling |
| Non-probability samples | Convenience sampling, judgement sampling, and quota sampling |
| Simple random sample | Each element not already in the sample, has an equal chance of being taken up in the next draw. List of all population elements, numbered & using mechanical method like table of random numbers or generating random numbers with technology. |
| Stratified random sample | N is divided into natural number of non-overlapping groups/strata, then elements are randomly selected from each group. |
| Proportionally stratified sample | n is proportional to N |
| Cluster sample | N is naturally grouped to form clusters; each cluster consists of heterogeneous elements. Random clusters are chosen and ALL Elements in that cluster is used |
| One stage cluster vs Two stage cluster | One-stage: All elements in clusters are used Two-stage: stratified sample of clusters are used |
| Convenience sample | Sample that is convenient for researcher, doesn't necessarily represent the population |
| Judgement sample | Uses "best" sample elements according to researchers judgement |
| Quota sample | Non-probability of stratified sampling. N is divided in segments and quotas of each segment is included |
| Errors & biases | Sample error Sampling observation error Sampling bias |
| Sample error | Inherent inability of a sample to provide accurate info. Dependent on n Cannot be avoided; minimized by a larger n |
| Sampling observation error | During data collection Faulty or inaccurate measuring instrument or unreliability of interviewer/respondent |
| Sampling bias | Certain parts of N is represented to lesser degree or not represented at all in n |
| Tabulation | (Same for discrete & continuous) Frequency table Cumulative frequencies Relative frequencies |
| Graphical | Discrete: Dot plot, bar chart, pie chart Continuous: Dot plot, histogram, frequency polygons |
| Frequency table | Table with classes of values and corresponding frequencies |
| Array | Data set that has been sorted in ascending order |
| Range (R) | Difference between largest & smallest observations |
| Sturge's rule (k) | k=1+1.4ln(n) rounded |
| Class width (w) | w=R/k |
| Class midpoint | Centre of each class |
| Cumulative frequency | F of point x is the number of observations in data set that is smaller than x |
| Relative frequencies | r=f/n divide frequencies by number or observations |
| Percentage frequencies | % of relative frequence (r*100) |
| Relative cumulative frequency (R) | R=F/n |
| Percentage cumulative frequencies | R*100 |
| Dot plot | Shows how data is distributed over possible values. When N is small. On line |
| Histogram | Graphical representation of frequency table |
| Frequency polygon | Frequency of each class interval is plotted against the class midpoint of class interval & joined with straight lines |
| Cumulative frequency polygon | Graphical representation of cumulative frequency table |
| Relative frequency polygon | Same as frequency polygon but with r instead of f, plotted against midpoints |
| Relative cumulative frequency polygon | Graphical representation of relative cumulative frequency table (R) |
| Bar chart | Graphical representation of frequency distribution of dicrete data |
| Pie chart | Graphical representation of relative frequencies of data sets, a circle divided into propotions |
Created by:
CARA.FAURIE
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