'Random component' presentation slideshows

Home Range Territories for the California Spotted Owl

Home Range Territories for the California Spotted Owl. Ross Gerrard (UCSB Geography Department) November 17, 2005 Dos Pueblos High School. Modern Geography. Somewhat difficult to define Cuts across many disciplines Divides usually into Physical geography (earth sciences)

916 views • 75 slides

LECTURE 5 ECONOMETRIC MODELS OF DYNAMICS

LECTURE 5 ECONOMETRIC MODELS OF DYNAMICS. Plan. 5.1 Basic concepts and preliminary analysis of the time series. 5.1.1 The notion of time series. 5.1.2 Main characteristics of the dynamics of the time series (self-directed learning).

810 views • 61 slides

Analyzing Non-Normal Data with Generalized Linear Models 2010 LISA Short Course Series

Analyzing Non-Normal Data with Generalized Linear Models 2010 LISA Short Course Series. Sai Wang, Dept. of Statistics. Presentation Outline. 1. Introduction to Generalized Linear Models 2. Binary Response Data - Logistic Regression Model Ex. Teaching Method

732 views • 58 slides

Forecast based on mononomial trend

370 views • 28 slides

Galactic Magnetic Field Research with LOFAR

453 views • 26 slides

Trend analysis: methodology

Trend analysis: methodology. Victor Shatalov. Meteorological Synthesizing Centre East. Main topics. Trend analysis of annual averages of concentration/deposition fluxes Trend analysis of monthly averages (with seasonal variations). Trend analysis: generalities. Residue. Trend.

436 views • 24 slides

Formulas

Formulas. Simple linear regressions, Multiple and correlation. Korelasi. Regresi Linear Berganda. Simple Linear Regression. Musiman dengan regresi dummy. Trend value at period 3, T 3. Step 1: Isolating the Trend Component. Smooth the time series to remove random effects and seasonality.

414 views • 21 slides

Tree methods , and the detection of vortical structures in the vortex filament method

337 views • 20 slides

Regression Model Building

Regression Model Building. Setting: Possibly a large set of predictor variables (including interactions). Goal: Fit a parsimonious model that explains variation in Y with a small set of predictors Automated Procedures and all possible regressions: Backward Elimination (Top down approach)

198 views • 15 slides

ECO 610-401

ECO 610-401. Monday, December 8 th Performance Measures Readings, Brickley et al., 16; 19:575-591 Extended Assignment 3 due. Principal-Agent Problems. Recall the principle-agent problem: Output by agent (employee) depends on her effort and some other random (and unknown) factors:

275 views • 13 slides

Generalized Linear Models

Generalized Linear Models. Generalized Linear Models (GLM). General class of linear models that are made up of 3 components: Random, Systematic, and Link Function Random component: Identifies dependent variable ( Y ) and its probability distribution

543 views • 12 slides

Generalized Linear Models

Generalized Linear Models. Generalized Linear Models (GLM). General class of linear models that are made up of 3 components: Random, Systematic, and Link Function Random component: Identifies dependent variable ( Y ) and its probability distribution

309 views • 12 slides