Question

Which prediction equation will describe the below experimental data the best? Explain why.

Y=0.35+1.15x₁+0.75x₂

Y=0.35+1.15x₁-0.75x₂

Y=0.35+1.15x₂+0.75x₁

Y=0.35+1.15x₂-0.75x₁

Here is the data for question3

Which prediction equation will describe the below experimental data the best? Explain why.

Y=0.35+1.15x₁+0.75x₂

Y=0.35+1.15x₁-0.75x₂

Y=0.35+1.15x₂+0.75x₁

Y=0.35+1.15x₂-0.75x₁

y	x1	x2
1	1	1
1	2	2
2	3	2
2	4	4
4	5	3

Answer 1

Sol:

In R studio fit a linear regression of y on x1 and x2 using lm function

linmod is a linear model fit of y on x1 and x2

coefficients to get the coefficients

Rcode:

df1 =read.table(header = TRUE, text ="
y x1 x2
1 1 1
1 2 2
2 3 2
2 4 4
4 5 3
"
)
df1
linmod <- lm(y~x1+x2,data=df1)
coefficients(linmod)

Output:

(Intercept) x1 x2
0.35 1.15 -0.75

Rscreenshot:

y^=0.35+1.15x1-0.75x2

The prediction equation that best fit the data is

Y=0.35+1.15x1-0.75x2

OPTION B