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Pick a reasonable value for your independent variable to make a prediction of the dependent variable. How do you know this is a reasonable value?Using the excel equation and your chosen value, make a prediction for your dependent variable – MAKE SURE TO SHOW WORK. Using your chosen value and the predicted value, explain what information this tells you. Based on all of the previous tasks, do you feel that your prediction is reliable – why or why not

Pick a reasonable value for your independent variable to make a prediction of the dependent variable. How do you know this is a reasonable value?Using the excel equation and your chosen value, make a prediction for your dependent variable – MAKE SURE TO SHOW WORK. Using your chosen value and the predicted value, explain what information this tells you. Based on all of the previous tasks, do you feel that your prediction is reliable – why or why not

MATLAB: An Introduction with Applications
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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Pick a reasonable value for your independent variable to make a prediction of the dependent variable. How do you know this is a reasonable value?Using the excel equation and your chosen value, make a prediction for your dependent variable – MAKE SURE TO SHOW WORK. Using your chosen value and the predicted value, explain what information this tells you. Based on all of the previous tasks, do you feel that your prediction is reliable – why or why not? 

35 30 25 20 15 10 5 0 At Bats 51 67 77 44 55 39 45 0 10 SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept 20 30 Regression Statistics Hits 19 25 30 20 23 16 18 Hits 40 SLOPE 0.340215 y = 0.3402x + 3.1998.. 60 70 80 YIntercept 3.199795 90 Correlation Regression 0.967625 0.954306 Field: At Bats and Field: Hits appear highly correlated. 35 30 25 20 15 10 5 0 20 30 40 50 60 70 80 Hits At Bats 50 0.976885985 0.954306228 0.942882786 1.218625281 6 df SS MS F ignificance F 1 124.0598 124.0598 83.53928 0.000795 4 5.94019 1.485048 5 130 Coefficients andard Erro t Stat P-value Lower 95% Upper 95% ower 95.0%Jpper 95.0% 3.726325329 2.06028 1.80865 0.144774 -1.99393 9.446581 -1.99393 9.446581 0.335296783 0.036685 9.139983 0.000795 0.233444 0.43715 0.233444 0.43715 51 0 10 90
Transcribed Image Text:35 30 25 20 15 10 5 0 At Bats 51 67 77 44 55 39 45 0 10 SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept 20 30 Regression Statistics Hits 19 25 30 20 23 16 18 Hits 40 SLOPE 0.340215 y = 0.3402x + 3.1998.. 60 70 80 YIntercept 3.199795 90 Correlation Regression 0.967625 0.954306 Field: At Bats and Field: Hits appear highly correlated. 35 30 25 20 15 10 5 0 20 30 40 50 60 70 80 Hits At Bats 50 0.976885985 0.954306228 0.942882786 1.218625281 6 df SS MS F ignificance F 1 124.0598 124.0598 83.53928 0.000795 4 5.94019 1.485048 5 130 Coefficients andard Erro t Stat P-value Lower 95% Upper 95% ower 95.0%Jpper 95.0% 3.726325329 2.06028 1.80865 0.144774 -1.99393 9.446581 -1.99393 9.446581 0.335296783 0.036685 9.139983 0.000795 0.233444 0.43715 0.233444 0.43715 51 0 10 90
ANOVA Regression Residual Total Intercept RESIDUAL OUTPUT Observation 51 1 2 3 4 5 6 df SS MS F ignificance F 1 124.0598 124.0598 83.53928 0.000795 4 5.94019 1.485048 5 130 Coefficients andard Erro t Stat P-value Lower 95% Upper 95% ower 95.0%Jpper 95.0% 3.726325329 2.06028 1.80865 0.144774 -1.99393 9.446581 -1.99393 9.446581 0.335296783 0.036685 9.139983 0.000795 0.233444 0.43715 0.233444 0.43715 Predicted 19 Residuals 26.19120979 -1.19121 29.54417762 0.455822 18.47938378 1.520616 22.16764839 0.832352 16.80289986 -0.8029 18.81468056 -0.81468
Transcribed Image Text:ANOVA Regression Residual Total Intercept RESIDUAL OUTPUT Observation 51 1 2 3 4 5 6 df SS MS F ignificance F 1 124.0598 124.0598 83.53928 0.000795 4 5.94019 1.485048 5 130 Coefficients andard Erro t Stat P-value Lower 95% Upper 95% ower 95.0%Jpper 95.0% 3.726325329 2.06028 1.80865 0.144774 -1.99393 9.446581 -1.99393 9.446581 0.335296783 0.036685 9.139983 0.000795 0.233444 0.43715 0.233444 0.43715 Predicted 19 Residuals 26.19120979 -1.19121 29.54417762 0.455822 18.47938378 1.520616 22.16764839 0.832352 16.80289986 -0.8029 18.81468056 -0.81468
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