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DESIGN DE EXPERIMENTOS (Bons Experimentos (Possuem REPLICAÇÕES para medir…

- - - - Continuous
        (ex.: kg)
        
        anything measured on a quantitative scale that could be any fractional number
      - Ordinal
        (ex.: poor, fair, good)
        
        are data that have a fixed, small (< 100) number of levels but are ordered
      - Categorical
        (ex.: sexo: homem, mulher)
        
        multiple categories, but they aren't ordered
      - Missing
        
        are unobserved and you don't know the
        mechanism. You should code missing values as NA
      - Censored
        
        you know the missingness mechanism on some level. Common examples are a measurement being below a detection limit or a patient being lost to follow-up. They should also be coded as NA when you don't have the data. But you should also add a new column to your tidy data called, "VariableNameCensored" which should have values of TRUE if censored and FALSE if not. In the code book you should explain why those values are missing. It is absolutely critical to report to the analyst if there is a reason you know about that some of the data are missing. You should also not impute/make up/ throw away missing observations.
  - - - Types of data summaries
        
        Relative Standing measures
        
        Desvio padrão
        
        Variation or Variability measures
        
        Média dos desvios quadrados dos valores da média
        
        Central Tendency measures
        
        Mean is best for symmetric distributions without outliers
        
        Median is useful for skewed distributions or data with
        outliers
        
        Interesting Theoretical Result
        
        Quartiles
        
        Percentiles
      - Types of variables
        
        Quantitative
        
        Continuous
        (uninterrupted)
        
        Discrete
        (numeric)
        
        Categorical
        
        Ordinal
        (order matter)
        
        Nominal
        (more categories)
        
        Binary
        (2 categories)
      - to generate hypotheses
      - to find violations of statistical assumptions
      - to see patterns in the data
      - to catch mistakes