MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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A new framework of data analysis where the new statistical methods are used for interpreting the results and analyzing the data is known as estimation in statistics.
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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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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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