Alarge number of insurance records are to be examined to develop a model for predicting fraudulent claims. of the claims in the historical database, 1% were judged to be fraudulent (class 1). a sample database is taken to develop a model, and oversampling is used to provide a balanced sample in light of the very low response rate. when applied to this sample database (total number of records, n = 800), the model ends up correctly classifying 310 frauds, and 270 non-frauds. it misses 90 frauds, and classified 130 records incorrectly as frauds when they were not. what is the true positive rate when the model is applied to the data set generated by oversampling?

nope.   happens to me as well.

-aparri

step-by-step explanation:

let's assume

number of candles set =x

price of of x candles =p(x)

we are given

a candle maker prices one set of scented candles at $10 and sells an average of 200 sets each week

so, we get point as (200,10)

he finds that when he reduces the price by $1, he then sells 50 more candle sets each week

so, we can find slope

now, we can use point slope form of line

now, we can plug values

we can use point

and find b

now, we can plug it back

and we get

now, we can find revenue

we know that

revenue = price*quantity

so, we get

it's a proportional graph.

step-by-step explanation:

8 x 5 = 40         (and you have -8x and -5y)

x= absiss

y= orderly

so you have a proportional graph.