Regression - Applications

Oct 23, 2019

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Regression - Applications. Stress vs Strain in a Composite Material. A Bone Scan. Radiation intensity from Technitium-99m. Mousetrap Car. Torsional Stiffness of a Mousetrap Spring. Trunnion-Hub Assembly. Thermal Expansion Coefficient Changes with Temperature?.

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Regression - Applications

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Regression - Applications http://nm.mathforcollege.com
Stress vs Strain in a Composite Material http://nm.mathforcollege.com
A Bone Scan http://nm.mathforcollege.com
Radiation intensity from Technitium-99m http://nm.mathforcollege.com
Mousetrap Car http://nm.mathforcollege.com
Torsional Stiffness of a Mousetrap Spring http://nm.mathforcollege.com
Trunnion-Hub Assembly http://nm.mathforcollege.com
Thermal Expansion Coefficient Changes with Temperature? http://nm.mathforcollege.com

Sampling algorithms for l 2 regression and applications

Sampling algorithms for l 2 regression and applications. Michael W. Mahoney Yahoo Research http://www.cs.yale.edu/homes/mmahoney (Joint work with P. Drineas and S. (Muthu) Muthukrishnan) SODA 2006. Regression problems. We seek sampling-based algorithms for solving l 2 regression.

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Regression

Quality Kitchens. What problems do they face?How can they overcome these problems?Will correlations help them?. Regression: What is it?. Find the best straight line through the data pointsTry to explain one variable (dependent) using other variables (independent).. Regression. Like correlation,

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Regression

BPS - 5th Ed.. Chapter 5. 2. Objective: To quantify the linear relationship between an explanatory variable (x) and response variable (y).We can then predict the average response for all subjects with a given value of the explanatory variable.. Linear Regression. BPS - 5th Ed.. Chapter 5. 3. Predic

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Regression

Regression . single and multiple. Overview. Defined: A model for predicting one variable from other variable(s). Variables: IV(s) is continuous, DV is continuous Relationship: Relationship amongst variables

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Regression. Correlation vs. Causation. Correlation. Causation. Means that one thing will cause the other. A statistical way to measure the relationship between two sets of data. Means that both things are observed at the same time.

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Multiple Regression Applications

Multiple Regression Applications. Lecture 14. Today’s plan. Relationship between R 2 and the F-test. Restricted least squares and testing for the imposition of a linear restriction in the model. ^. ^. R 2. We know. We can rewrite this as. Remember:

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Regression

Regression. What is regression to the mean? Suppose the mean temperature in November is 5 degrees What’s your best guess for tomorrow’s temperature? exactly 5? warmer than 5? colder than 5?. Regression. What is regression to the mean?

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REGRESSION

REGRESSION. What is Regression? What is the Regression Equation? What is the Least-Squares Solution? How is Regression Based on Correlation? What are the Assumptions for Using Regression?. What is Regression?. Predict future scores on Y based on measured scores on X

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Regression

Regression. Several Explanatory Variables. Example: Scottish hill races data . These data are made available in R as data(hills, package=MASS) They give record times (minutes) in 1984 of 35 Scottish hill races, against distance

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Multiple Regression Applications. Lecture 16. Phillips Curve example. Phillips curve as an example of a regime shift. Data points from 1950 - 1970: There is a downward sloping, reciprocal relationship between wage inflation and unemployment. W. U N. Phillips Curve example (2).

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Multiple Regression Applications

Multiple Regression Applications. Lecture 15. Today’s Plan. Two topics and how they relate to multiple regression Multicollinearity Dummy variables. Multicollinearity. Suppose we have the following regression equation: Y = a + b 1 X 1 + b 2 X 2 + e

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Regression Linear Regression Regression Trees

Regression Linear Regression Regression Trees. Characteristics of classification models. slide thanks to Greg Shakhnarovich (CS195-5, Brown Univ., 2006). slide thanks to Greg Shakhnarovich (CS195-5, Brown Univ., 2006). slide thanks to Greg Shakhnarovich (CS195-5, Brown Univ., 2006).

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Regression Linear Regression

Regression Linear Regression. slide thanks to Greg Shakhnarovich (CS195-5, Brown Univ., 2006). slide thanks to Greg Shakhnarovich (CS195-5, Brown Univ., 2006). slide thanks to Greg Shakhnarovich (CS195-5, Brown Univ., 2006). slide thanks to Greg Shakhnarovich (CS195-5, Brown Univ., 2006).

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Multiple Regression Applications III

Multiple Regression Applications III. Lecture 18. Dummy variables. Include qualitative indicators into the regression: e.g. gender, race, regime shifts. So far, have only seen the change in the intercept for the regression line.

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REGRESSION

REGRESSION. Want to predict one variable (say Y) using the other variable (say X) GOAL: Set up an equation connecting X and Y. Linear regression linear eqn: y= α + β x, α = y-intercept, β = slope. We fit a line, regression line, to the data (scatter plot). y= α + β x.

381 views • 14 slides

Applications of Regression to Water Quality Analysis

Applications of Regression to Water Quality Analysis. Unite 5: Module 18, Lecture 1. Statistics. A branch of mathematics dealing with the collection, analysis, interpretation and presentation of masses of numerical data Descriptive Statistics (Lecture 1) Basic description of a variable

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Regression

Regression. Normal Curve. Paranormal Curve. Correlation – again. Correlation provides an estimate of how well change in ‘ x ’ causes change in ‘ y ’. The relationship has a magnitude (the r value) and a direction (the sign on the r value).

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Regression

Regression. Topics for today Readings Jewell Chapters 12, 13, 14 & 15. Context. So far in the course, we have learned how to Quantify disease occurrence (prevalence, incidence etc)

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Multiple Regression Applications

Multiple Regression Applications. Lecture 15. Today’s plan. Relationship between R 2 and the F-test. Restricted least squares and testing for the imposition of a linear restriction in the model. ^. ^. R 2. We know. We can rewrite this as. Remember:

423 views • 22 slides

REGRESSION

166 views • 14 slides

Regression

Regression. Applications. Mousetrap Car. Torsional Stiffness of a Mousetrap Spring. Stress vs Strain in a Composite Material. A Bone Scan. Radiation intensity from Technitium-99m. Trunnion-Hub Assembly. Thermal Expansion Coefficient Changes with Temperature?. THE END.

409 views • 39 slides

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