Regression #1 The regression equation is: (Final Bionass · Ini. BLonass)--n, Pre

Question

Regression #1 The regression equation is: (Final Bionass · Ini. BLonass)--n, Predictor Constant Digested Org. 0.24120 0.04459 5.41 0.001 Residual Plats for (Final-Am) Biamaus Coof SE Coef 13.70 10.93 -1.25 0.246 5.84353 R-Sq- " 78.5% R-Sq (adj) 75.8% . Analysis of Variance DF SS Source Regression R.esidual Error 273.17 34.15 Total NS 1 999.23 999.23 29.26 0.001 9 1272.40 Regression #2 The regression equation is: Pinal Biomass-. 23.0 + 1.40 tni. Bionau . 0.21 pigested Or Resioual Plots for Final Blomass Coef SE Coef 22.9917.76 -1.29 0.237 Predictor Constant Ini. Biomass 2.39570.5825 2.40 0.04 Digested Org. 0.21761 0.05777 3.77 0.007 R-Sq-87.3% R.Sqtadj) "83.76 S_ 6.05079 Analysis of Variance MSFP 0.001 DF Source Regression Residual Errory 256.28 Total x 1764.22 9 2020.50

Explanation / Answer

Here we have given ANOVA table and regression analysis table from these we can get to know that

Whether the regression has worth while or not

And if the regression is worth while then which variables are having significant impact on the model.

We will use p-value in each case for testing

Where p-value <0.05 reject Null hypothesis

Otherwise we accept null hypothesis

We will start with ANOVA table that will give us overall impact

Here p-value =0.001<0.05 that means the regression is worth while.

Now will check what factors effecting the regression that will be done using regression table we have

Here except the intercept part all has p-value <0.05 hence we can reject Ho that there is no effect of this factors.

So can comment that the regression is worth while and both factors are significantly effective.

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