Plot a point at the y-intercept pf the following function on the graph provided. 3y= -5^x+7

Mathematics

1 answer:

Brums [2.3K]3 years ago

4 0

Answer:

y-intercept: (0, 2)

Step-by-step explanation:

By graphing the equation into a graphing calc, you can trace the graph to where the y-int is located and you have your answer of (0, 2)

Alternatively, you could set x equal to zero and solve algebraically to find the y-int. However, if you are under a time crunch, graphing will be the faster way to go.

You might be interested in

I need help on this problem in math

S_A_V [24]

Answer:

correct answer is C
Haley incorrectly applied the distributive property

Step-by-step explanation:

If you simplify the given equation, you find it matches choice C.

$\dfrac{1}{2}(6-x)+3x=\dfrac{1}{2}x-8\\\\3-\dfrac{1}{2}x+3x=\dfrac{1}{2}x-8\\\\3+\dfrac{5}{2}x=\dfrac{1}{2}x-8\qquad\text{matches choice C}$

Haley's error seems to be failing to distribute the 1/2 properly when she eliminated parentheses. Apparent, she incorrectly decided that ...

1/2(6 -x) ⇒ 3 -x . . . . instead of 3 -1/2x

Then when -x was added to +3x, she got 2x. Had she done it properly, she would have added -1/2x to +3x to get 5/2x.

_____

Additional comment

It is a common error to "distribute" the factor outside parentheses to the first term only, as Haley apparently did. Another common error is to fail to distribute minus signs properly. The distributive property requires you apply the outside factor to all of the terms inside parentheses.

5 0

2 years ago

A shirt boutique purchased 100 new shirts for $1072. The short-sleeved shirts cost $10 each and the long-sleeved shirts cost $12

Scilla [17]

Answer:

one equation would be S+ L=36for the number of shirts sold.

4 0

2 years ago

Read 2 more answers

If we reject the null hypothesis, h0: ρ = 0 , what can we conclude about the population correlation coefficient?

ehidna [41]

If the null hypothesis, $H_0:\:\rho =0$ is rejected, then we can conclude that the correlation coefficient is significant. This means that there is enough evidence to conclude that a relationship exists between the two (or more) variables involved in the regression.