What Is Linear Regression And Correlation


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Linear Regression And Correlation Coefficient


Linear Regression And Correlation Coefficient

Author: Karena Leischner

language: en

Publisher:

Release Date: 2021-07


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Linear regression is a way of predicting an unknown variable using results that you do know. If you have a set of x and y values, you can use a regression equation to make a straight line relating the x and y. The reason you might want to do this is if you know some information, and want to estimate other information. For instance, you might have measured the fuel economy in your car when you were driving 30 miles per hour, when you were driving 40 miles per hour, and when you were driving 75 miles per hour. What Is In This Book? There are a number of examples shown in this book, they include: -How to do a correlation calculation -An example of correlation on the stock price of 10 different big-name stocks, such as Coke and Pepsi -How having uncorrelated investments can give you better returns at lower risk. -How to do linear regression with two variables -How to do multiple linear regression with any number of independent variables -A regression analysis to predict the number of viewers in future episodes of the television show 'Modern Family' -How to evaluate the quality of your regression analysis using R-squared or adjusted R-squared -How to do regression on exponential data, and recreate Moore's law

Applying Regression and Correlation


Applying Regression and Correlation

Author: Jeremy Miles

language: en

Publisher: SAGE

Release Date: 2000-11-24


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This book takes a fresh look at applying regression analysis in the behavioural sciences by introducing the reader to regression analysis through a simple model-building approach. The authors start with the basics and begin by re-visiting the mean, and the standard deviation, with which most readers will already be familiar, and show that they can be thought of a least squares model. The book then shows that this least squares model is actually a special case of a regression analysis and can be extended to deal with first one, and then more than one independent variable. Extending the model from the mean to a regression analysis provides a powerful, but simple, way of thinking about what students believe are the more complex aspects of regression analysis. The authors gradually extend the model to include aspects of regression analysis such as non-linear regression, logistic regression, and moderator and mediator analysis. These approaches are often presented in terms that are too mathematical for non-statistically inclined students to deal with. Throughout the book maintains a conceptual, non-mathematical focus. Most equations are placed in an appendix, where a detailed explanation is given, to avoid disrupting the flow of the main text. This book will be indispensable for anyone using regression and correlation from undergraduates doing projects to postgraduate and researchers.

An Introduction to Linear Regression and Correlation


An Introduction to Linear Regression and Correlation

Author: Allen Louis Edwards

language: en

Publisher: W.H. Freeman

Release Date: 1976


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Linear relationships; Some simple nonlinear relationship that can be transformed into linear relationships; The regression line of Y on X; The correlation coefficiente; Correlation and regression with standardized variables; Factors influencing the magnitude of the correlation coefficient; Special cases of the correlation coefficient; Tests of significance for correlation coefficientes; Tests of significance for special cases of the correlation coefficient; Tests of significance for regression coefficients; Coefficients for orthogonal polynomials; Tests of significance using coefficients for orthogonal polynomials; Analysis of variance for a simple repeated measure design; Multiple correlation and regression.