The answer is 1206 cm. The formula for the volume of this cone is 1/3 • 3.14 • 12(2) • 8.
Which gives you 1205.76 which is rounded up to 1206.
The (2) means the second power so it’s 12 to the second power.
<span>4.8 (x+4)=2.6
Use distributive property
4.8x + 19.2= 2.6
Subtract 19.2 from both sides
4.8x = -16.6
Divide 4.8 on both sides so that the only thing remaining on the left side is the variable x.
Final Answer: x = -3.458</span>
Your question appears to be phrased incorrectly. If the ratio of soccer players to football players is 4:1 then for every 1 football player there are 4 soccer players. For example, if there are 3 football players, there would be 12 soccer players.
The equivalent expression would be S = 4F
The statement: "there are 4 more soccer players than football players" is not the same thing. It simply means that we add 4 to the total of football players to find out how many soccer players there are.
The equivalent expression would be S = F + 4
That being said: An equivalent ratio to 4:1 would be 8:2 , 12:3, 16:4, ...
Think of the ratio as a fraction. 4:1 = 4/1
4/1 = 8/2 = 12/3 = 16/4 ..., etc.
Answer:
In decimal 0.428571428
Step-by-step explanation:
The slope = (y2 - y1)/(x2 - x1) y2 = 16, y1 = 19x2=2 and x1=9
The slope = (16 - 19)/(2 -9)
The slope = (-3)/(-7)
The slope = 0.428571428
So in decimal form the coordinates are going to be written as 0.428571428
Slope is the steepness of a line, it is the difference between the y coordinates divided by the difference in x coordinates.
<u>Answer:</u>
<u>Null hypothesis: Policy B remains more effective than policy A.</u>
<u>Alternate hypothesis: Policy A is more effective than policy B.</u>
<u>Step-by-step explanation:</u>
Remember, a hypothesis is a usually tentative (temporary until tested) assumption about two variables– independent and the dependent variable.
We have two types of hypothesis errors:
1. A type I error occurs when the null hypothesis (H0) is wrongly rejected.
That is, rejecting the assumption that policy B remains more effective than policy A when it is <em>actually true.</em>
2. A type II error occurs when the null hypothesis H0, is not rejected when it is actually false. That is, accepting the assumption that policy B remains more effective than policy A when it is <em>actually false.</em>
