Answer:
F is reduced by 31%
Step-by-step explanation:
F = 1/(d²)
now, we increase the distance by 20% (multiply by 1.2).
F new = 1/(1.2×d)² = 1/(1.44×d²) = (1/1.44) × (1/(d²)) =
= 1/1.44 × old F = 0.69 × old F
100 - 69 = 31%
Answer:
It is actually 2 and -6.
Step-by-step explanation:
i hope i helped <33
Let the two numbers be x and y.
According to your question;
x + y = 7
10y + x = 10x + y + 9
By equation 1 ; x = 7-y
Substituting the value of x ;
10y + ( 7 -y) = 10(7-y) + y + 9
9y + 7 = 70 -10y + y + 9
9y + 7 = 70 - 9y + 9
=> 18y = 70 -7 + 9
=> 18y = 72
=> y = 4
Substituting for x ;
x = 7 - y
=> x = 7 -4
=> x = 3
Thus, x = 3 and y = 4;
=> The number is 34.
Answer:
4. C) 
3. B) 9,6 = the number of points you would increase each hour of studying; 65,8 = your score if you studied 0 hours
2. B) The events have a strong positive linear correlation.
1. C) Find the slope using the slope formula:

Step-by-step explanation:
4. (−7, 10) → 10 = 7 + 3 ☑
(−1, 4) → 4 = 1 + 3 ☑
(0, 3) → 3 = 0 + 3 ☑
(3, −2) → −2 ≠ −3 + 3; 0 ☒
3. You obviously have to plug "0" in for x to get your initial value of 65,8, which represents the minimum value of points you would receive if you never were to study, and of course, the 9,6 is the average score increased for every hour studied.
2. The correlation coefficient is 0,02, which is positive, so this would be the obvious choice.
1. You CANNOT write a linear equation without FIRST finding the rate of change [slope]. You will ALWAYS need the rate of change in order to write any linear equation.
I am joyous to assist you anytime.
Answer: A.

Step-by-step explanation:
Null hypothesis
: A statement describing population parameters as per the objective of the study. It usually takes "≤,≥,=" signs.
Alternative hypothesis
: A statement describing population parameters as per the objective of the study. It usually takes ">, <, ≠" signs.
Let p be the proportion of screens that will be rejected.
12 percent of the screens manufactured using a previous process were rejected at the final inspection.
(i.e. p= 0.12)
Objective of the study = whether the new process<em> reduces </em>the population proportion of screens that will be rejected
i.e. p< 0.12
So, the appropriate hypotheses to investigate whether the new process reduces the population proportion of screens that will be rejected:
