Type the regression equation for “Area” and “Biomass” in context into your document. Interpret the slope of the regression line (in context of this data set) for “Area” and “Biomass”.is it meaningful to interpret the y-intercept for “Area” and “Biomass”? Why or why not? Simple linear regression results:Dependent Variable: BiomassIndependent Variable: AreaBiomass = 111.02418 + 1.9367237 AreaSample size: 29R (correlation coefficient) = 0.63737458R-sq = 0.40624635%3D%3DEstimate of error standard deviation: 50.521904Parameter estimates:Parameter EstimateStd. Err.Alternative DFT-StatP-valueIntercept111.0241874.67702327 1.48672480.1487Slope1.9367237 0.4506031227 4.29806990.0002Analysis of variance table for regression model:Source DFSSMSF-statP-valueModel1 47152.678 47152.678 18.4734050.0002Error27 68916.495 2552.4628Total28 116069.17

Here we have, The dependent variable: Biomass (y) The independent variable: Area (x)

Type the regression equation for “Area” and “Biomass” in context into your document. Interpret the slope of the regression line (in context of this data set) for “Area” and “Biomass”. is it meaningful to interpret the y-intercept for “Area” and “Biomass”? Why or why not?

Type the regression equation for “Area” and “Biomass” in context into your document. Interpret the slope of the regression line (in context of this data set) for “Area” and “Biomass”. is it meaningful to interpret the y-intercept for “Area” and “Biomass”? Why or why not?

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 12CR

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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

Type the regression equation for “Area” and “Biomass” in context into your document.
Interpret the slope of the regression line (in context of this data set) for “Area” and “Biomass”.
is it meaningful to interpret the y-intercept for “Area” and “Biomass”? Why or why not?

Simple linear regression results:
Dependent Variable: Biomass
Independent Variable: Area
Biomass = 111.02418 + 1.9367237 Area
Sample size: 29
R (correlation coefficient) = 0.63737458
R-sq = 0.40624635
%3D
%3D
Estimate of error standard deviation: 50.521904
Parameter estimates:
Parameter Estimate
Std. Err.
Alternative DF
T-Stat
P-value
Intercept
111.02418
74.677023
27 1.4867248
0.1487
Slope
1.9367237 0.45060312
27 4.2980699
0.0002
Analysis of variance table for regression model:
Source DF
SS
MS
F-stat
P-value
Model
1 47152.678 47152.678 18.473405
0.0002
Error
27 68916.495 2552.4628
Total
28 116069.17

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