Let X e Rnd be a data matrix, consisting of n samples, cach of which has d features, and let y R" be a vector of outcomes. For example, each row of X could have information about a house on the market, like its area, number of floors, number of bathrooms/bedrooms, etc. and each entry of y could be the price of that house. We are interested in building a model that predicts house prices from the set of its features, as listed above. Suppose that domain knowledge tells us that the relationship between the features and outcomes is linear; ideally, there exists a set of parameters e Rd such that Xo = y. However, n is large and there is noise in the acquisition of X and y, so this system is overdetermined. Still, we wish to find the best linear approximation, ie. we want to find the θ that minimizes the loss L(θ)-lly-X613. Assuming X has full column rank, compute θ': arg mine L(θ) in terms of X and y 2*

the answer is c 150

step-by-step explanation:

third choice down

step-by-step explanation:

the first inequality is based on the number of adults and campers, and the second inequality is based on the cost of feeding both the adult and the camper.

we know that there is room for 525 people max.   it's ok to have less than 525, but it's not ok to have more than 525, so the inequality is less than or equal to.   the number combination of adults (x) and campers (y) cannot exceed this 525 max.   therefore:

x + y ≤ 525

now we talk about cost.   it costs $9 to feed one adult, so depending upon the number of adults you have (x) depends upon how much it will cost to feed that number of adults.   same with the campers, who cost $5 to feed.   the adults can be represented by 9x and the campers can be represented by 5y.   since the budget cannot exceed 1,200, the second inequality is:

9x + 5y ≤ 1200

8p+(-9)

(-9)3p+5p and you can swith em around