Use one of the real world example data sets from R (not previously used in the R practice assignment) or a dataset you have found, to build regularization models by using Lasso (least absolute shrinkage and selection operator) and extend Lasso model fitting to big data that cannot be loaded into memory. You will fit solution paths for linear or logistic regression models penalized by Lasso over a grid of values for the regularization parameter lambda. Use the resources in this module to guide your R code development.

the polynomial is a perfect square trinomial of the form a^2-2ab+b^2:

option a. true

step-by-step explanation:

9x^2-12x+4

comparing with the form a^2-2ab+b^2

1.) a^2=9x^2

solving for a: square root both sides of the equation:

sqrt(a^2)=sqrt(9x^2)

a=sqrt(9) sqrt(x^2)

a=3x

2.) b^2=4

solving for b: square root both sides of the equation:

sqrt(b^2)=sqrt(4)

b=2

to be a perfet square trinomial 2ab must be equal to 12x. let's check:

2ab = 2 (3x) (2)

2ab = 12x

correct

it's undefined because anything multiplied by 0 is 0. that said, 7 days is 168 hours

2y+y=5

add 2y and y