Point slope form follows the equation y-y₁=m(x-x₁), so we want it to look like that. Starting off with m, or the slope, we can find this using your two points with the formula

. Note that y₁ and x₁ are from the same point, but it does not matter which point you designate to be point 1 and point 2. Thus, we can plug our numbers in - the x value comes first in the equation, and the y value comes second, so we have

as our slope. Keeping in mind that it does not matter which point is point 1 and which point is point 2, we go back to y-y₁=m(x-x₁) and plug a point in (I'll be using (10,5)). Note that x₁, m, and y₁ need to be plugged in, but x and y stay that way so that you can plug x or y values into the formula to find where exactly it is on the line. Thus, we have our point slope equation to be

Feel free to ask further questions!
<em>Independent variables are variables of a quantity that are not affected by any conditions. </em>
<em>Dependent variables are variables of a quantity that change if conditions relative to that variable changes.</em>
For example, we generally we take x as independent variable by x variable and dependent variable by y variable.
To find the rate of change we get two values of independent variable (x's) and two values of dependent variables (y's) to get two coordinates in form of
(x,1,y1) and (x2,y2).
<h3>And we can find the rate of change by applying slope formula</h3>
.
Answer:
2.50$
Step-by-step explanation:
Answer:
You get 17.80 cents back
Step-by-step explanation:
4.5 times 0.49 is 2.20 cents
20 minus 2.20 is 17.80 dollars
Answer:
The sample size is 
Step-by-step explanation:
From the question we are told that
The sample standard deviation is 
The mean difference of the two groups is 
The standard error is 
=> 
Let assume that the confidence level is 95% hence the level of significance is

=> 
So the critical value of
obtained from the normal distribution table is

Generally the margin of error is mathematically evaluated as

Generally the sample size is mathematically represented as
![n =[ \frac{Z_{\frac{\alpha }{2} * s^2}}{E}]^2](https://tex.z-dn.net/?f=n%20%3D%5B%20%5Cfrac%7BZ_%7B%5Cfrac%7B%5Calpha%20%7D%7B2%7D%20%20%2A%20s%5E2%7D%7D%7BE%7D%5D%5E2)
=> ![n = [\frac{1.96 * 1.5}{0.49} ]^2](https://tex.z-dn.net/?f=n%20%20%3D%20%5B%5Cfrac%7B1.96%20%2A%201.5%7D%7B0.49%7D%20%5D%5E2)
=> 