Statistics

Statistics

1. Basic Concepts

   • Random variables
     • Quantitative
       • Continuous
       • Discrete
     • Qualitative

2. Measurement Scales

   • Nomimal
     • Values assigned to variables represent a descriptive category, but have no inherent numerical value with respect to magnitude.
     • Properties: Identiy
   • Ordinal
     •    Each value on the ordinal scale has a unique meaning, and it has an ordered relationship to every other value on the scale.   
     • Properties: Identity and Magnitude
   • Interval
     • With an interval scale, you know not only whether different values are bigger or smaller, you also know how much bigger or smaller they are. 
     • Properties: Identity, magniyude and equal intervals
   • Ratio
     •    Each value on the weight scale has a unique meaning, weights can be rank ordered, units along the weight scale are equal to one another, and the scale has a minimum value of zero.   
     • Properties: Identity, magnitude, equal intervals and minium value of zero

3. Sampling Methods

   • Not probability samples
     •  With non-probability sampling methods, we do not know the probability that each population element will be chosen, and/or we cannot be sure that each population element has a non-zero chance of being chosen.
     • voluntarie
       •  A voluntary sample is made up of people who self-select into the survey. 
     • Convenience
       • A convenience sample is made up of people who are easy to reach. 
   • Probability samples
     • With probability sampling methods, each population element has a known (non-zero) chance of being chosen for the sample.
     • Simple random
       • With multistage sampling, we select a sample by using combinations of different sampling methods. 
     • Systematic Random
       • With systematic random sampling, we create a list of every member of the population. From the list, we randomly select the first sample element from the first k elements on the population list. Thereafter, we select every kth element on the list. 
     • Clutser
       • With cluster sampling, every member of the population is assigned to one, and only one, group. 
     • Stratified
       • With stratified sampling, the population is divided into groups, based on some characteristic. 

4. Visual displays of data

   • Frecuency Distribution
     • Frecuecy
       • When a data includes many repeated items.
     • Relative Frecuency
       • This is the fraction or percentage of data set represented by the item.
   • DISPLAYS
     • Histogram
     • Steam and Leaf
     • Bar Graphs
     • Circle Graphs
     • Line Graphs

5. Measures

   • Measures of Central Tendency
     • Mean
     • Weighted mean
     • Mode
       • The mode of data set is the value that occurs most often.
   • Measures of Dispersion
     • Range
     • Chebyshev's teorem
     • Standard derivation
     • Coefficient of variation

Images and files in the map