The following results were obtained through a Multiple Linear Regression analysis. This dataset contains the measured above-ground biomass of oak seedlings (in grams) along with their measured basal diameters (in mm), measured heights (in cm), and number of leaves. Write the equation that describes the following Multiple Regression
The following results were obtained through a Multiple Linear Regression analysis. This dataset contains the measured above-ground biomass of oak seedlings (in grams) along with their measured basal diameters (in mm), measured heights (in cm), and number of leaves. Write the equation that describes the following Multiple Regression
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:The following results were obtained through a
Multiple Linear Regression analysis. This dataset
contains the measured above-ground biomass of
oak seedlings (in grams) along with their measured
basal diameters (in mm), measured heights (in cm),
and number of leaves. Write the equation that
describes the following Multiple Regression
Results. Note* Think carefully about the equation you are about to write. You earn
credit for the correct answer only.
> model1 = glm(biomass ~ ., data = oakData)
> summary(model1)
Call: glm(formula = biomass ~ ., data = oakData)
Deviance Residuals:
Min
1Q Median
3Q
Max
-0.33018 -0.04959 0.01250 0.07744 0.22787
Coefficients:
Estimate
Std. Error tvalue Pr(>It)
(Intercept) -0.286251 0.087679 -3.265 0.002974 **
basalDiam 1.848193 0.467631 3.952 0.000502 ***
height
-0.001376 0.014740 -0.093 0.926294
leaves
0.091190 0.014502 6.288 9.94e-07 ***
Signif. codes: 0***' 0,001 "**' 0.01 '*' 0,05 0.11
(Dispersion parameter for gaussian family taken to be 0.01617867)
Null deviance: 4.42217 on 30 degrees of freedom
Residual deviance: 0.43682 on 27 degrees of freedom
AIC: -34.154 Number of Fisher Scoring iterations: 2
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