Exam 7: Linear Regression

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In a linear regression model, the variable (or variables) used for predicting or explaining values of the response variable are known as the __________. It(they) is(are) denoted by x.

(Multiple Choice)

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Question 41

Fitting a model too closely to sample data, resulting in a model that does not accurately reflect the population is termed as

(Multiple Choice)

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Question 42

The ___________ is a measure of the goodness of fit of the estimated regression equation. It can be interpreted as the proportion of the variability in the dependent variable y that is explained by the estimated regression equation.

(Multiple Choice)

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Question 43

A procedure for using sample data to find the estimated regression equation is

(Multiple Choice)

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Question 44

The least squares regression line minimizes the sum of the

(Multiple Choice)

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Question 45

Given the partial Excel output from a multiple regression, formulate the regression model. Coefficients Standard Error Intercept 37,375.357 3,721.625 55.655 9.370 -5.750 3.575 0.213 0.373

(Essay)

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Question 46

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In a linear regression model, the variable (or variables) used for predicting or explaining values of the response variable are known as the __________. It(they) is(are) denoted by x.

Fitting a model too closely to sample data, resulting in a model that does not accurately reflect the population is termed as

The ___________ is a measure of the goodness of fit of the estimated regression equation. It can be interpreted as the proportion of the variability in the dependent variable y that is explained by the estimated regression equation.

A procedure for using sample data to find the estimated regression equation is

The least squares regression line minimizes the sum of the

Given the partial Excel output from a multiple regression, formulate the regression model. Coefficients Standard Error Intercept 37,375.357 3,721.625 55.655 9.370 -5.750 3.575 0.213 0.373

Introduction

Descriptive Statistics

Data Visualization

Descriptive Data Mining

Probability: an Introduction to Modeling Uncertainty

Statistical Inference

Time Series Analysis and Forecasting

Predictive Data Mining

Spreadsheet Models

Monte Carlo Simulation

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Decision Analysis

Filters

Exam 7: Linear Regression

In a linear regression model, the variable (or variables) used for predicting or explaining values of the response variable are known as the __________. It(they) is(are) denoted by x.

Fitting a model too closely to sample data, resulting in a model that does not accurately reflect the population is termed as

The ___________ is a measure of the goodness of fit of the estimated regression equation. It can be interpreted as the proportion of the variability in the dependent variable y that is explained by the estimated regression equation.

A procedure for using sample data to find the estimated regression equation is

The least squares regression line minimizes the sum of the

Given the partial Excel output from a multiple regression, formulate the regression model. ​ Coefficients Standard Error Intercept 37,375.357 3,721.625 55.655 9.370 -5.750 3.575 0.213 0.373 ​

Introduction

Descriptive Statistics

Data Visualization

Descriptive Data Mining

Probability: an Introduction to Modeling Uncertainty

Statistical Inference

Time Series Analysis and Forecasting

Predictive Data Mining

Spreadsheet Models

Monte Carlo Simulation

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Decision Analysis

Filters

Given the partial Excel output from a multiple regression, formulate the regression model. Coefficients Standard Error Intercept 37,375.357 3,721.625 55.655 9.370 -5.750 3.575 0.213 0.373