'Linear model' presentation slideshows

Mixed Models

1.26k views • 89 slides

Unsupervised Morphological Segmentation With Log-Linear Models

Unsupervised Morphological Segmentation With Log-Linear Models. Hoifung Poon University of Washington Joint Work with Colin Cherry and Kristina Toutanova. Machine Learning in NLP. Machine Learning in NLP. Unsupervised Learning. Machine Learning in NLP. Unsupervised Learning.

1.06k views • 72 slides

CHAPTER

764 views • 48 slides

DAGs intro with exercises 3h D irected A cyclic G raph

DAGs intro with exercises 3h D irected A cyclic G raph. Hein Stigum http://folk.uio.no/heins/ courses. Nov-15. Nov-15. Nov-15. Nov-15. Nov-15. H.S. H.S. H.S. H.S. 1. 1. 1. 1. 1. Agenda. Background DAG concepts Association, Cause Confounder, Collider, Mediator

597 views • 44 slides

454 views • 32 slides

Logistic Regression

1.25k views • 32 slides

Multiplication and Division of Fractions: Helping Increase the Certainty of Understanding

468 views • 32 slides

Chaos in Easter Island Ecology

502 views • 31 slides

Introduction

Simple Linear Regression 1. review of least squares procedure 2. inference for least squares lines. Introduction. We will examine the relationship between quantitative variables x and y via a mathematical equation. The motivation for using the technique:

663 views • 31 slides

Analysis of Covariance

Analysis of Covariance. Goals:. Reduce error variance. Remove sources of bias from experiment. Obtain adjusted estimates of population means. Analysis of Covariance. Y. Y. X .1. X . X .2. X. X .1. X. X .2. X . Random assignment conditional distribution is less variable.

523 views • 29 slides

Multiple Imputation : Handling Interactions

Multiple Imputation : Handling Interactions. Michael Spratt. Introduction. Missing data is considerable problem Complete case analysis will generally lead to systematic bias Have to make some assumption Most commonly used is Missing at Random (MAR) MNAR uses different assumptions

706 views • 23 slides

IT Applications in Business Analytics

827 views • 22 slides

Control of Experimental Error

Control of Experimental Error. Blocking - A block is a group of homogeneous experimental units Maximize the variation among blocks in order to minimize the variation within blocks Reasons for blocking To remove block to block variation from the experimental error (increase precision)

783 views • 21 slides

Planning rice breeding programs for impact

Planning rice breeding programs for impact. Experimental design. Learning objectives. Review the features of modern experimental designs that allow separation of the effects of genotype and environment

526 views • 21 slides

Nonlinear Models

382 views • 21 slides

Beta

537 views • 15 slides

Regression

Regression : Mathematical method for determining the best equation that reproduces a data set Linear Regression : Regression method applied with a linear model (straight line) Uses Prediction of new X,Y values Understanding data behavior Verification of hypotheses/physical laws.

623 views • 14 slides

Linear Programming II

Linear Programming II. Pioneers of LP. George Dantzig 1947 Narendra Karmarkar. Computer Solution of the LP Problem. Simplex Method A step-by-step mathematical procedure for finding the optimal solution to a LP problem.

342 views • 13 slides

Logistic Regression

Logistic Regression. The logistic function: The logistic function is useful because it can take as an input any value from negative infinity to positive infinity, whereas the output is confined to values between 0 and 1.

202 views • 5 slides

Log-linear Analysis

Log-linear Analysis. Log-linear analysis is used when the outcome and explanatory variables are both categorical. It is an extension of the ? 2 test for two by two contingency table.

802 views • 2 slides