How do I solve this

When we use arcsine, we are finding the angle while giving the trigonometric ratio.

Arcsin(u) = theta can be rewritten as:

sin(theta) = u

Sine is opposite over hypotenuse, so u/1 means that the side opposite to theta (the y value) is u, and the hypotenuse is 1.

We can use Pythagorean Theorem to find the adjacent (x value).

1^2 - u^2 = x^2

x = sqrt(1-u^2)

Back to the original question, we are trying to find cos(arcsin(u)). We just solved all the sides for our triangle using arcsin(u). Now we need to do cos(u).

Cosine is adjacent over hypotenuse.

So our answer is sqrt(1-u^2)/1

Or just sqrt(1-u^2)

5 0

3 years ago

Jason owns a small business selling bagels. He knows that in the last week 45 customers paid cash, 8 customers used a debit card

tatuchka [14]

The probability that the next customer will pay with cash is 0.76

<h3>What is the probability?</h3>

Formula to find the probability of an event is:

P(A) = n(A)/n(S)

where;

n(A) is the number of favorable outcomes of an event A

n(S) is the total number of outcomes for an experiment/survey

We are given that;

45 customers paid cash

8 customers used a debit card

6 customers used a credit card.

Thus, the total number of customers:

n(S) = 45 + 8 + 6

n(S) = 59

Let event A be the event that the next customer will pay with cash. Thus, n(A) = 45

Therefore, the probability that the next customer will pay with cash would be;

P(A) = n(A)/n(S)

P(A) = 45/59

P(A) = 0.76

Read more about Probability at; brainly.com/question/23047865

#SPJ1

7 0

1 year ago

In a study relating the degree of warping, in mm, of a copper plate (y) to temperature in °C (x), the following summary statisti

kipiarov [429]

Answer:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

Step-by-step explanation:

Linear regression is a way "to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables)".

Regression estimates are "used to describe data and to explain the relationship between one dependent variable and one or more independent variables"

The linear model is given by the following equation $Y=mx +b$ , where y is the dependent variable, x the independent variable, m the slope and b the intercept.