All categories

2 years ago

Solve by any method (graphing, substitution or linear combination)

Mathematics

1 answer:

Lostsunrise [7]2 years ago

8 0

Answer:

b) (5, -3)

Step-by-step explanation:

The given equations are;

$y=x-8...(1)$

$2x+3y=1...(2)$

I prefer using substitution because of the first equation.

Put equation (1) into equation (2) to obtain;

$2x+3(x-8)=1$

We expand the parenthesis to obtain;

$2x+3x-24=1$

Group similar terms to get;

$2x+3x=1+24$

Simplify

$5x=25$

Divide through by 5;

$x=\frac{25}{5}$

$\Rightarrow x=5$

Put $x=5$ into equation (1) to get;

$y=5-8$

$\Rightarrow y=-3$

The solution is (5,-3).

The correct answer is B

You might be interested in

Through (-5.-2) with a slope = 4/5

eduard

It should be -3 I hope this helps

7 0

2 years ago

I Need help ASAP it is due today 5/26/21

Westkost [7]

Answer:

Step-by-step explanation:

$9*8=72\\0.5*6*8=24\\10*9=90\\72+24+90=186 m^{2}$

4 0

2 years ago

"is it appropriate to use the normal approximation for the sampling distribution of"

Katyanochek1 [597]

Answer: Normal approximation can be used for discrete sampling distributions, such as Binomial distribution and Poisson distribution if certain conditions are met.

Step-by-step explanation: We will give conditions under which the Binomial and Poisson distribitions, which are discrete, can be approximated by the Normal distribution. This procedure is called normal approximation.

1. Binomial distribution: Let the sampling distribution be the binomial distribution $B(n,p)$ , where $n$ is the number of trials and $p$ is the probability of success. It can be approximated by the Normal distribution with the mean of $np$ and the variance of $np(1-p)$ , denoted by $N(np,np(1-p))$ if the following condition is met:

$n>9\left(\frac{1-p}{p}\right)\text{ and } n>9\left(\frac{p}{1-p}\right)$

2. Poisson distribution: Let the sampling distribution be the Poisson distribution $P(\lambda)$ where $\lambda$ is its mean. It can be approximated by the Normal distribution with the mean $\lambda$ and the variance $\lambda$ , denoted by $N(\lambda,\lambda)$ when $\lambda$ is large enough, say $\lambda>1000$ (however, different sources may give different lower value for $\lambda$ but the greater it is, the better the approximation).