please show work, prior to comparing my answer. Thanks The data used in creating

Question

please show work, prior to comparing my answer. Thanks

The data used in creating the attached JMP output (see separate sheet) are collected in order to study the effects of two independent variables x1 = specific gravity and x2-moisture content on the response variable y = strength of wood beams. Answer the following questions from the output. (a) What were the two least square equations fit to the data? Equation 1: y= Equation 2: y (b) What fraction of the raw variability in the strength of wood beams is accounted for by the two equations? For equation 1 For equation 2: (c) What is the sample correlation between strength of wood beams (y) and moisture content (z2)? (Give a number, be careful about the sign.) T'= (d) Using the first equation, what strength of wood beams would you predict when moisture content is 10? Would you be willing to predict strength of wood beams when moisture content is 15? Why or why not? predicted y = Yes/No? Why:

Explanation / Answer

A ]

Y = Strength_wood_beam

X1 = Moisture_content

X2 = Specific Gravity

> Mod1 = lm(Strength_wood_beam~Moisture_content)
> Mod1

Call:
lm(formula = Strength_wood_beam ~ Moisture_content)

Coefficients:
(Intercept) Moisture_content  
18.0432 -0.6242  

So fited Model is

Strength of wood beam = 18.0432 - 0.6242 * Moisture content

> Mod2 = lm(Strength_wood_beam~specific_gravity)
> Mod2

Call:
lm(formula = Strength_wood_beam ~ specific_gravity)

Coefficients:
(Intercept) specific_gravity  
6.397 11.047  

Fited Model is,

Strength_wood_beam = 6.397 + 11.047 * specific_gravity

B] For Model One : R-squared: 0.5764

For Model two :  R-squared: 0.8328

C] cor(Strength_wood_beam,Moisture_content)

-0.7592328

D] predict(Mod1)
[,1]
[1,] 11.11446
[2,] 12.48773
[3,] 12.55015
[4,] 12.48773
[5,] 12.55015
[6,] 11.86352
[7,] 11.36415
[8,] 11.48899
[9,] 11.48899
[10,] 11.36415

No, Because this is large than the predicted value.

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