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2 years ago

If the point (3,3) lies on the graph of y=f(x), what point lies on y=f(x-2)?

Mathematics

2 answers:

Bad White [126]2 years ago

6 0

<h2>Answer:</h2>

Option: A is the correct answer.

A. (5,3)

<h2>Step-by-step explanation:</h2>

It is given that:

The point (3,3) lie on the graph of y=f(x)

Now, we are asked to find:

which point lie on the graph of:

y=f(x-2)

The graph is nothing but the translation or the shifting of the function y=f(x) 2 units to the right.

i.e. the coordinates are transformed using the rule:

(x,y) → (x+2,y)

Hence,

(3,3) → (3+2,3)

i.e.

(3,3) → (5,3)

The answer is: Option: A

My name is Ann [436]2 years ago

3 0

Answer:

A. (5, 3)

Step-by-step explanation:

Making use of the hint:

x -2 = 3

x = 5 . . . . . . add 2

The point (x, 3) is on the graph for x=3.

The point (x-2, 3) is on the graph for x=5, so ...

the graph of (x, f(x-2)) will include the point (5, 3).

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Simplify the expression <br> 12(8b-5)

Crazy boy [7]

Answer:

96b-60

Step-by-step explanation:

12(8b-5)=

12*8b - 5*12 =

96b-60

4 0

3 years ago

medical tests. Task Compute the requested probabilities using the contingency table. A group of 7500 individuals take part in a

uysha [10]

Probabilities are used to determine the chances of an event

The probability that a person is sick is: 0.008
The probability that a test is positive, given that the person is sick is 0.9833
The probability that a test is negative, given that the person is not sick is: 0.9899
The probability that a person is sick, given that the test is positive is: 0.4403
The probability that a person is not sick, given that the test is negative is: 0.9998
A 99% accurate test is a correct test

<u />

<u>(a) Probability that a person is sick</u>

From the table, we have:

$\mathbf{Sick = 59+1 = 60}$

So, the probability that a person is sick is:

$\mathbf{Pr = \frac{Sick}{Total}}$

This gives

$\mathbf{Pr = \frac{60}{7500}}$

$\mathbf{Pr = 0.008}$

The probability that a person is sick is: 0.008

<u>(b) Probability that a test is positive, given that the person is sick</u>

From the table, we have:

$\mathbf{Positive\ and\ Sick=59}$

So, the probability that a test is positive, given that the person is sick is:

$\mathbf{Pr = \frac{Positive\ and\ Sick}{Sick}}$

This gives

$\mathbf{Pr = \frac{59}{60}}$

$\mathbf{Pr = 0.9833}$

The probability that a test is positive, given that the person is sick is 0.9833

<u>(c) Probability that a test is negative, given that the person is not sick</u>

From the table, we have:

$\mathbf{Negative\ and\ Not\ Sick=7365}$

$\mathbf{Not\ Sick = 75 + 7365 = 7440}$

So, the probability that a test is negative, given that the person is not sick is:

$\mathbf{Pr = \frac{Negative\ and\ Not\ Sick}{Not\ Sick}}$

This gives

$\mathbf{Pr = \frac{7365}{7440}}$

$\mathbf{Pr = 0.9899}$

The probability that a test is negative, given that the person is not sick is: 0.9899

<u>(d) Probability that a person is sick, given that the test is positive</u>

From the table, we have:

$\mathbf{Positive\ and\ Sick=59}$

$\mathbf{Positive=59 + 75 = 134}$

So, the probability that a person is sick, given that the test is positive is:

$\mathbf{Pr = \frac{Positive\ and\ Sick}{Positive}}$

This gives

$\mathbf{Pr = \frac{59}{134}}$

$\mathbf{Pr = 0.4403}$

The probability that a person is sick, given that the test is positive is: 0.4403

<u>(e) Probability that a person is not sick, given that the test is negative</u>

From the table, we have:

$\mathbf{Negative\ and\ Not\ Sick=7365}$

$\mathbf{Negative = 1+ 7365 = 7366}$

So, the probability that a person is not sick, given that the test is negative is:

$\mathbf{Pr = \frac{Negative\ and\ Not\ Sick}{Negative}}$

This gives

$\mathbf{Pr = \frac{7365}{7366}}$

$\mathbf{Pr = 0.9998}$

The probability that a person is not sick, given that the test is negative is: 0.9998

<u>(f) When a test is 99% accurate</u>

The accuracy of test is the measure of its sensitivity, prevalence and specificity.

So, when a test is said to be 99% accurate, it means that the test is correct, and the result is usable; irrespective of whether the result is positive or negative.