A study was conducted to see if a person's income will affect their well-being. We want to create a model with Y = well-being index, and X = income in $1,000. The regression model is: O y = beta0 + beta1*x O y = beta0 + beta1*x + error O y_hat = beta0_hat + beta1_hat*x + error O y_hat = beta0_hat + beta1_hat*x

A study was conducted to see if a person's income will affect their well-being. We want to create a model with Y = well-being index, and X = income in $1,000. The regression model is: O y = beta0 + beta1x O y = beta0 + beta1x + error O y_hat = beta0_hat + beta1_hatx + error O y_hat = beta0_hat + beta1_hatx

Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015

1st Edition

ISBN:9781680331141

Author:HOUGHTON MIFFLIN HARCOURT

Publisher:HOUGHTON MIFFLIN HARCOURT

Chapter4: Writing Linear Equations

Section: Chapter Questions

Problem 14CR

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

A study was conducted to see if a person's income will affect their well-being. We want to create a model with Y = well-being index, and X =
income in $1,000. The regression model is:
y = beta0 + beta1*x
%D
y = beta0 + beta1*x + error
%3D
y_hat = beta0_hat + beta1_hat*x + error
O y_hat = beta0_hat + beta1_hat*x

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