Part of an R output relating X (independent variable) and Y (dependent variable) is shown below. This analysis is based on 11 observations. Using what you know about how all of these values are related, fill in the remaining blanks. Round your answers to 4 decimals in Model Summary box. Round your answers to 2 decimals in the ANOVA table and beyond. Model Summary (4 decimal places each) RMSE R R-Squared Adj. R-Squared Pred. R-Squared Source Regression Residual ANOVA (2 decimal places each, where necessary) Sum of Squares Total X 0.515 Model (Intercept) 0.3632 1000 Parameter Estimates (2 decimal places each) Coef. Var. Beta df 5.7871 29.48 Std. Error Std. Beta 0.699 Mean Square -0.72 t F Sig. 3.79 0.0043 -3.091 0.0129 Sig 0.0129
Part of an R output relating X (independent variable) and Y (dependent variable) is shown below. This analysis is based on 11 observations. Using what you know about how all of these values are related, fill in the remaining blanks. Round your answers to 4 decimals in Model Summary box. Round your answers to 2 decimals in the ANOVA table and beyond. Model Summary (4 decimal places each) RMSE R R-Squared Adj. R-Squared Pred. R-Squared Source Regression Residual ANOVA (2 decimal places each, where necessary) Sum of Squares Total X 0.515 Model (Intercept) 0.3632 1000 Parameter Estimates (2 decimal places each) Coef. Var. Beta df 5.7871 29.48 Std. Error Std. Beta 0.699 Mean Square -0.72 t F Sig. 3.79 0.0043 -3.091 0.0129 Sig 0.0129
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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