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lab9lastone!

# lab9lastone! - Problem1 a Let y denotes the response x1...

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Problem1 a) Let y denotes the response, x1 denotes the force, x2 denotes the power, x3 denotes the temperature, and x4 denotes the time. Then the model statement for the dataset is y =-37.47667+0.21167x1+0.49833x2+0.12967x3+0.25833x4 ̂ b) The REG Procedure Model: MODEL1 Dependent Variable: str Number of Observations Read 3 0 Number of Observations Used 3 0 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model 4 1660.14000 415.03500 15.60 <.0001 Error 25 665.11867 26.60475 Corrected Total 29 2325.25867 Root MSE 5.15798 R-Square 0.7140 Dependent Mean 38.40667 Adj R-Sq 0.6682 Coeff Var 13.42991

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Parameter Estimates Variable D F Parameter Estimate Standar d Error t Value Pr > |t| 99% Confidence Limits Intercept 1 -37.47667 13.09964 -2.86 0.0084 -73.99108 -0.96226 force 1 0.21167 0.21057 1.01 0.3244 -0.37529 0.79863 power 1 0.49833 0.07019 7.10 <.0001 0.30268 0.69399 temp 1 0.12967 0.04211 3.08 0.0050 0.01227 0.24706 time 1 0.25833 0.21057 1.23 0.2313 -0.32863 0.84529 c) H0: β=0 Ha: β≠0 The F value given by the output is 15.60, and the P value is less than 0.0001 which is smaller than our alpha value. Reject the null hypotheses. We have significant evidence to claim that there is a multiple linear regression between the response and the 4 independent variables: force, temperature, power and time.
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lab9lastone! - Problem1 a Let y denotes the response x1...

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