Answer:
180 texts
Explanation:
mutiply 92 by 0.10 and you'll get 9.2
So each text costs 10 cents.
Answer:
50/p increases from a small positive number to a big positive number.
Step-by-step explanation:
p is in the denominator. This means that p and the value of the expression 50/p are inverse proportional. So for a big value of p, 50/p has a small positive value. For a small value of p, 50/p has a high positive value.
what happens to the value of the expression 50/p as p decreases from a large positive number to a small positive number?
50/p increases from a small positive number to a big positive number.
For example
50/1000 = 0.05
50/1 = 50
Answer: 31.25%
Step-by-step explanation: To answer the question <em>what percent of 80 is 25</em>, we translate the question into an equation.
So we have "what percent" that's x, "of 80" means times 80, "is 25" = 25.
Now to solve for <em>x</em>, since <em>x</em> is being multiplied by 80, we need to divide by 80 on both sides of the equation. On the left side of the equation the 80's cancel and we have <em>x</em> and on the right side of the equation, we have 25 ÷ 80 which gives us 0<em>.</em>3125. Now, remember that we want to right our answer as a percent so to write 0.3125 as a percent, we move the decimal point 2 places to the right and we get 31.25%.
Therefore, 31.25% of 80 is 25.
Answer:
the slope of the regression equation for predicting our Exam 2 scores from Exam 1 scores is 0.492
And the y-intercept of the regression equation for predicting our Exam 2 scores from Exam 1 is 33.688
Step-by-step explanation:
Given the data in the question;
mean X" = 86
SD σx = 10
Y" = 76
SD σy = 8.2
r = 0.6
Here, Exam 2 is dependent and Exam 1 is independent.
The Regression equation is
y - Y" = r × σy/σx ( x - x" )
we substitute
y - 76 = 0.6 × 8.2/10 ( x - 86 )
y - 76 = 0.492( x - 86 )
y - 76 = 0.492x - 42.312
y = 0.492x - 42.312 + 76
y = 0.492x + 33.688
Hence, the slope of the regression equation for predicting our Exam 2 scores from Exam 1 scores is 0.492
And the y-intercept of the regression equation for predicting our Exam 2 scores from Exam 1 is 33.688
