• pqrs
• visual summaries

• Goal: inference - conclusion or opinion formed from evidence
• PQRS
  • P - population
  • Q - question - 2 types
    1. hypothesis driven - does a new drug work
    2. discovery driven - find a drug that works
  • R - representative data colleciton
    • simple random sampling = SRS
      • w/ replacement: $var(bar{X}) = sigma^2 / n$
      • w/out replacement: $var(bar{X}) = (1 - frac{n}{N}) sigma^2 / n$
  • S - scrutinizing answers

visual summaries

• numerical summaries
  • mean vs. median
  • sd vs. iq range
• visual summaries
  • histogram
  • kernel density plot - Gaussian kernels
    • with bandwidth h $K_h(t) = 1/h K(t/h)$
• plots
  1. box plot / pie-chart
  2. scatter plot / q-q plot
     • q-q plot = probability plot - easily check normality
     • plot percentiles of a data set against percentiles of a theoretical distr.
     • should be straight line if they match
  3. transformations = feature engineering
     • log/sqrt make long-tail data more centered and more normal
     • delta-method - sets comparable bw (wrt variance) after log or sqrt transform: $Var(g(X)) approx [g’(mu_X)]^2 Var(X)$ where $mu_X = E(X)$
     • if assumptions don’t work, sometimes we can transform data so they work
     • transform x - if residuals generally normal and have constant variance
     • corrects nonlinearity
       - transform y - if relationship generally linear, but non-constant error variance
     • stabilizes variance
       - if both problems, try y first
       - Box-Cox: Y’ = $Y^l : if : l neq 0$, else log(Y)
  4. least squares
     • inversion of pxp matrix ~O(p^3)
     • regression effect - things tend to the mean (ex. bball children are shorter)
     • in high dims, l2 worked best
  5. kernel smoothing + lowess
     • can find optimal bandwidth
     • nadaraya-watson kernel smoother - locally weighted scatter plot smoothing
     • $g_h(x) = frac{sum K_h(x_i - x) y_i}{sum K_h (x_i - x)}$ where h is bandwidth
       - loess - multiple predictors / lowess - only 1 predictor
     • also called local polynomial smoother - locally weighted polynomial
     • take a window (span) around a point and fit weighted least squares line to that point
     • replace the point with the prediction of the windowed line
     • can use local polynomial fits rather than local linear fits
  6. silhouette plots - good clusters members are close to each other and far from other clustersf
     1. popular graphic method for K selection
     2. measure of separation between clusters $s(i) = frac{b(i) - a(i)}{max(a(i), b(i))}$
     3. a(i) - ave dissimilarity of data point i with other points within same cluster
     4. b(i) - lowest average dissimilarity of point i to any other cluster
     5. good values of k maximize the average silhouette score
  7. lack-of-fit test - based on repeated Y values at same X values