Elements of Statistical Inference

Population and Sample
1. Representative Sample
  1. Without Bias (Sesgo)
2. Types of Sampling
  1. Simple
  2. Stratified
  3. Systematic
  4. Cluster
3. Aleatory
  1. It can be modeling
    1. Describe
    2. Limit (acotar)
4. Finite
5. Infinite
  1. Continuous Production
    1. The process flow does not stop
      1. Extract some products in one point of the process in order to make quality control
  2. Mass Production
Statistical Parameters
1. We use them to estimate statistical parameters of population
  1. Mean (µ)^
  2. Standard deviation (S)^
  3. Proportion of defects (p)^ in %
2. The symbol ^ is just to represent that comes from sample and not population
Probability Distribution
1. Normal
2. t Student
3. chi-quarter
4. F distriution
Estimation
1. Punctual
  1. We use Statistical Parameters
  2. If variation between two punctual estimation is high
    1. How close of parameters of population is my punctual estimation?
      1. Use Standard Error
        If we want a specific error
        Estimate Sample Size
2. By Intervals
  1. Do before Descriptive Analysis of Data
    1. Confidence Interval
      1. The amplitud of this depends on:
        Size of the sample
        Population
        Variance
        Level of confidence
      2. Mean (µ)^
      3. Proportion of defects (p)^ in %
        Normal distribution supported by binomal distribution
        n*p ≥ 5 and n*(1 − p) ≥ 5.
      4. Variance
Hypothesis Testing
1. Null Hypothesis
2. Statistical Test
  1. Unilateral Hypothesis
  2. Bilateral Hyptohesis
3. Rejection Criteria
4. Alternative Hypothesis

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Elements of Statistical Inference

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