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Matlab: Linear Methods, QuantilesPresentation Transcript
Quantile Quantiles are points taken at regular intervals from the cumulative distribution function (CDF) of a random variable. Dividing ordered data into n essentially equal-sized data subsets is the motivation for n-quantiles; the quantiles are the data values marking the boundaries between consecutive subsets.
Quantile Some quantiles have special names: The 2-quantile is called the median The 3-quantiles are called tertiles or terciles -> T The 4-quantiles are called quartiles -> Q The 5-quantiles are called quintiles -> QU The 9-quantiles are called noniles (common in educational testing)-> NO The 10-quantiles are called deciles -> D The 12-quantiles are called duo-deciles -> Dd The 20-quantiles are called vigintiles -> V The 100-quantiles are called percentiles -> P The 1000-quantiles are called permillages -> Pr
Quantile Y = quantile(X,p) returns quantiles of the values in X. p is a scalar or a vector of cumulative probability values. When X is a vector, Y is the same size as p, and Y(i) contains the p(i)thquantile. When X is a matrix, the ith row of Y contains the p(i)thquantiles of each column of X. For N-dimensional arrays, quantile operates along the first nonsingleton dimension of X.
Quantile Examples: y = quantile(x,.50); % the median of x y = quantile(x,[.025 .25 .50 .75 .975]); % Summary of x
Least Squares Fitting Least squares fitting is a mathematical procedure for finding the best-fitting curve to a given set of points by minimizing the sum of the squares of the offsets ("the residuals") of the points from the curve.
Least Squares Fitting
Least Squares Fitting In practice, the vertical offsets from a line (polynomial, surface, hyper-plane, etc.) are almost always minimized instead of the perpendicular offsets.
mldivide, mrdivide mldivide(A,B) and the equivalent AB perform matrix left division (back slash). A and B must be matrices that have the same number of rows, unless A is a scalar, in which case AB performs element-wise division — that is, AB = A.B.
mldivide, mrdivide mrdivide(B,A) and the equivalent B/A perform matrix right division (forward slash). B and A must have the same number of columns.
Generalized Linear Models Linear regression models describe a linear relationship between a response and one or more predictive terms. Many times, however, a nonlinear relationship exists. Nonlinear Regression describes general nonlinear models. A special class of nonlinear models, known as generalized linear models, makes use of linear methods.
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