'Regression slope' diaporamas de présentation

Chapter 4, 5, 24 Simple Linear Regression

The Basic Practice of Statistics 6 th Edition. Chapter 4, 5, 24 Simple Linear Regression. Chapter Goals. After completing this segment, you should be able to: Explain the simple linear regression model Obtain and interpret the simple linear regression equation for a set of data

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Inference for Regression

Inference for Regression. In Lesson 6, we modeled relationships by fitting a straight line to a sample of ordered pairs. The Nambe Mills regression line is Now we want to know, how useful is this model?. 16.1 The Population and the Sample.

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Bivariate regression

Bivariate regression. The slope, explained variance, residuals. What is the formula for a slope?. A. e = mc 2 B. Y i = a + bx i + e i C. ? = a + bx D. y ? x ? a. What information does the slope provide?. A. whether the relationship is statistically significant

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REGRESSION (CONTINUED)

LECTURE 4. REGRESSION (CONTINUED) Analysis of Variance; Standard Errors & Confidence Intervals; Prediction Intervals; Examination of Residuals. Supplementary Readings : Wilks , chapters 6,9;

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Statistical analyses in the real world

Statistical analyses in the real world. Paul Williams Lawrence Berkeley National Laboratory. Statistical significance. Theory: The probably of observing something by chance Practice: The accepted threshold P<0.05 is publishable P>0.05 is not publishable

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Fitting a Line to a Set of Points

Fitting a Line to a Set of Points. Scatterplot ? fitting a line. Least squares method Minimize the error term e. y (dependent). x (independent). n. S (y - ?) 2. n. S (y i - a - bx i ) 2. min a,b. min a,b. =. i = 1. i = 1. Minimizing the SSE ( Sum of Squared Errors ). n.

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Objectives (BPS chapter 24)

Objectives (BPS chapter 24). Inference for regression Conditions for regression inference Estimating the parameters Using technology Testing the hypothesis of no linear relationship Testing lack of correlation Confidence intervals for the regression slope Inference about prediction

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A Case Study Using the CMAQ Coupling with Global Dust Models

A Case Study Using the CMAQ Coupling with Global Dust Models. Youhua Tang, Pius Lee, Marina Tsidulko, Ho-Chun Huang, Sarah Lu, Dongchul Kim Scientific Applications International Corporation, Camp Springs, Maryland Jeffery T. McQueen, Geoffrey J. DiMego

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Education 793 Class Notes

Education 793 Class Notes. ANCOVA Presentation 11. Review: Analysis of Variance. ANOVA can be used to compare two or more means with one continuous dependent variables and two or more treatment levels:. Review: Simple Linear Regression. Simple Linear Regression. Assumptions independence

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Inference for Regression

Inference for Regression. Chapter 14. The Regression Model. The LSRL equation is ? = a + bx a and b are statistics; they are computed from sample data, therefore, we use them to estimate the true y-intercept, ? , and true slope, ? ? y = ? + ? x

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Watershed Watch 2013 :: Elizabeth City State University

Determination of an Empirical Model Relating Canopy Cover to NDVI Values in the Pasquotank Watershed, NC. Jimil Perkins Computer Science Norfolk State University. Michael Chamberlain Geophysics University of California, Berkeley. Dr. Stephen Hale Joan and James Leitzel Center

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Linear Regression and Correlation Analysis

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