PPT-Lecture 6 : Multiple and Poly Linear Regression

Author : luna | Published Date : 2023-10-31

1 2 Office Hours More office hours schedule will be posted soon Online office hours are for everyone please take advantage of them Projects Project guidelines

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Lecture 6 : Multiple and Poly Linear ..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Lecture 6 : Multiple and Poly Linear Regression: Transcript

1 2 Office Hours More office hours schedule will be posted soon Online office hours are for everyone please take advantage of them Projects Project guidelines and project descriptions will be posted Thursday 925. Di64256erentiating 8706S 8706f Setting the partial derivatives to 0 produces estimating equations for the regression coe64259cients Because these equations are in general nonlinear they require solution by numerical optimization As in a linear model N is the process noise or disturbance at time are IID with 0 is independent of with 0 Linear Quadratic Stochastic Control 52 brPage 3br Control policies statefeedback control 0 N called the control policy at time roughly speaking we choo Jennifer Kensler. Laboratory for Interdisciplinary Statistical Analysis. Collaboration. . From our website request a meeting for personalized statistical advice. Great advice right now:. Meet with LISA . 1. 3.6 Hidden Extrapolation in Multiple Regression. In prediction, exercise care about potentially extrapolating beyond the region containing the original observations.. Figure 3.10. An example of extrapolation in multiple regression.. NBA 2013/14 Player Heights and Weights. Data Description / Model. Heights (X) and Weights (Y) for 505 NBA Players in 2013/14 Season. . Other Variables included in the Dataset: Age, Position. Simple Linear Regression Model: Y = . How to predict and how it can be used in the social and behavioral sciences. How to judge the accuracy of predictions. INTERCEPT and SLOPE functions. Multiple regression. This week. 2. Based on the correlation, you can predict the value of one variable from the value of another.. Model . the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed . data.. Formally, the model for multiple linear regression, given . 1. 2. 3. Outline. Jinmiao. Fu—Introduction and History . Ning. Ma—Establish and Fitting of the model. Ruoyu. Zhou—Multiple Regression Model in Matrix Notation. Dawei. . Xu. and Yuan Shang—Statistical Inference for Multiple Regression. Al M Best, PhD. Virginia Commonwealth University. Task Force on Design and Analysis . in Oral Health Research. Satellite Symposium, AADR. Boston, MA: March 10, 2015. Multivariable statistical modeling from 10,000 feet. 1. Correlation indicates the magnitude and direction of the linear relationship between two variables. . Linear Regression: variable Y . (criterion) . is predicted by variable X . (predictor) . using a linear equation.. What. is . what. ? . Regression: One variable is considered dependent on the other(s). Correlation: No variables are considered dependent on the other(s). Multiple regression: More than one independent variable. explore how to model an outcome variable in terms of input variable(s) using linear regression, principal component analysis and Gaussian processes. At the end of this class you should be able to . …. May 23, 2023. May 23, 2023. Arthur-Merlin Games. Delimiting # of rounds:. AM[k]. = Arthur-Merlin game with . k. rounds, Arthur (verifier) goes first. MA[k]. = Arthur-Merlin game with . k. rounds, Merlin (prover) goes first. Regression Trees. Characteristics of classification models. model. linear. parametric. global. stable. decision tree. no. no. no. no. logistic regression. yes. yes. yes. yes. discriminant. analysis.

Download Document

Here is the link to download the presentation.
"Lecture 6 : Multiple and Poly Linear Regression"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.