• Save
Matlab: Linear Methods, Quantiles
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share

Matlab: Linear Methods, Quantiles

  • 4,481 views
Uploaded on

Matlab: Linear Methods, Quantiles

Matlab: Linear Methods, Quantiles

More in: Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
4,481
On Slideshare
4,452
From Embeds
29
Number of Embeds
3

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 29

http://www.dataminingtools.net 14
http://www.slideshare.net 13
http://dataminingtools.net 2

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Matlab:Linear Methods
  • 2. 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.
  • 3. 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
  • 4. 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.
  • 5. Quantile
    Examples:
    y = quantile(x,.50); % the median of x
    y = quantile(x,[.025 .25 .50 .75 .975]); % Summary of x
  • 6. 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.
  • 7. Least Squares Fitting
  • 8. 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.
  • 9. 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.
  • 10. 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.
  • 11. 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.
  • 12. Visit more self help tutorials
    Pick a tutorial of your choice and browse through it at your own pace.
    The tutorials section is free, self-guiding and will not involve any additional support.
    Visit us at www.dataminingtools.net