'Least squares regression line' diaporamas de présentation

Bivariate Statistics and Linear Regression

Bivariate Statistics and Linear Regression. Bivariate Statistics. (when two variables are involved). Basic Univariate Statistics. Mean Standard deviation Variance (you know this stuff). Reason for doing a correlation. To determine whether two variables are related

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Chapter 13

Chapter 13. Simple Linear Regression & Correlation Inferential Methods. Deterministic Models.

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Linear Regression

Linear Regression. William P. Wattles, Ph.D. Psychology 302. Correlation.

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Principles of Biostatistics Simple Linear Regression

Principles of Biostatistics Simple Linear Regression. PPT based on Dr Chuanhua Yu and Wikipedia . Terminology. Moments, Skewness, Kurtosis Analysis of variance ANOVA Response (dependent) variable Explanatory ( independent) variable Linear regression model

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Chapter 13 Introduction to Linear Regression and Correlation Analysis

Chapter 13 Introduction to Linear Regression and Correlation Analysis. Chapter Goals. To understand the methods for displaying and describing relationship among variables. Methods for Studying Relationships. Graphical Scatterplots Line plots 3-D plots Models Linear regression

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Linear Regression and Correlation Analysis

Linear Regression and Correlation Analysis. Chapter Goals. To understand the methods for displaying and describing relationship among two variables. Models Linear regression Correlations Frequency tables. Methods for Studying Relationships. Graphical Scatter plots Line plots 3-D plots.

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Correlation

Correlation. (and a bit on regression). Correlation (Linear). Scatterplots Linear Correlation / Covariance Pearson Product-Moment Correlation Coefficient Factors that Affect Correlation Testing the Significance of a Correlation Coefficient. Correlation.

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Sections 2.1-2.2

Sections 2.1-2.2. Looking at Data-Relationships. Data with two or more variables:. Response vs Explanatory variables Scatterplots Correlation Regression line. Association between a pair of variables.

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Simple linear regression

Simple linear regression. Linear regression with one predictor variable. What is simple linear regression?. A way of evaluating the relationship between two continuous variables . One variable is regarded as the predictor , explanatory , or independent variable (x).

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Chapter 15 (Ch. 13 in 2 nd Can.)

Chapter 15 (Ch. 13 in 2 nd Can.). Association Between Variables Measured at the Interval-Ratio Level: Bivariate Correlation and Regression. Introduction:. Scattergrams / Scatterplots Graphs that display relationships between two interval-ratio variables.

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Chapter 13

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Simple Linear Regression

Simple Linear Regression. 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. Advantages:

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Exploratory Analysis of Survey Data

Exploratory Analysis of Survey Data. Lisa Cannon Luke Peterson. Presentation Outline. Density Estimation Nonparametric kernel density estimates Properties of kernel density estimators Other methods Graphical Displays NHANES data. Three features that distinguish survey data:.

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PreCalculus

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Looking at data: relationships Least-squares regression

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Linear regression

Linear regression. Linear Regression . In this lesson you will learn: How to calculate a least squares regression line and use it to make predictions. How to use residuals How a regression equation is effected by a linear transformation of either of the variables.

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Least Squares Regression Line (LSRL)

Least Squares Regression Line (LSRL). Presentation 2-5. Introduction. Size of Diamond vs. Price. Many times the scatterplot shows some pattern in the data. For now, we will look at the analysis of data that falls in a straight line pattern. Introduction.

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Flashbacks

Flashbacks. Flashbacks. 2. The following are resistant: LSRL Correlation Coefficient Both the LSRL and the correlation coefficient Neither the LSRL nor the correlation coefficient. It depends. Flashbacks.

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