Inferential staistics

Inferential staistics

1. Sampling

   • Types
     • Probabilistic
       • The sample is chosen randomly
     • No-probabilistic
       • The sample is chosen by preference and facilities
   • Representative part of the total population
     • Useful to not study the hole population
   • Random sampling
     • Simple random sampling
       • Number for each part of the population
         • Write subjects in a piece of paper to choose what homework you are going to do first, take one and you will know which subject
     • Systematic random sampling
       • k=N/n
         • N= Population
           • n= sample
         • Ex. You have a population of 100 dogs and the sample is 20:
           • k= 100/20
             • k=50
           • You will choose random the first element and then to select the second you are going to add 50
             • First element: 5

2. Types od stadistics

   • Descriptive stadistics
     • Total population
   • Inferential stadistics
     • Sample

3. Random sampling

   • Simple
     • Number assigned to elements of population
       • Ex. Write on a paper names of friends to determine who is going to give who in an exchange
   • Cluster
     • Population is spared in groups and only one sample of the groups is chosen
       • Ex. At ITESM, you divide the generation into 4 groups of 20 students but you take into account only group 1
   • Systematic
     • Number assigned to population but the second sample is determined by a k constant
       • k=N/n
         • Ex. You have a population of 100 dogs and you need 20
           • k=100/20 = 50
             • You select random the first element (4) and then you add 50 for the second element
               • 4+50 = 54
         • N=population
           • n= sample
   • Stratified
     • Divides population in subgroups based on characteristics. The selection of sample consider elements of each stratum of the population in proportional number.
       • Ex. You want to make a survey about dogs who eat croquettes. You randomly select dogs who eat Dog Chow and is the 5% of the total dogs, the number of dogs selected in this brand should represent 6% of the sample

4. Hypotesis

   • States of a parameter to say if it is true or false
   • Analysis unit: units or elements we are gonna use asa sample
   • Logic: Defines what we are analyzing, it is a phrase."... is lower than..."
   • Variables: What we are going to analyze about the elements in our sample

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