Answer:
It means that for every 4cm on an axis, it counts as 1 unit.
So for instance, you have a 24cm axis to work with. You would have a total of 6 units on the axis, because each unit is 4cm.
Answer:
B
Step-by-step explanation:
<span>a) define the variables
The variables are x and y ; x can represent the number of times Aleah goes swimming ; while y can represents the number of times Aleah goes skating.
b) are there any restrictions on the variables?
The total sum of both activities must be equal or less than $80 a month
c) write a linear inequality to represent this situation
</span><span>Swimming costs $5 each time and skating costs $4 each time. No more than $80 a month.
5x + 4y <u><</u> 80
You can graph linear inequality using these coordinates.
x y
16 0
12 5
8 10
4 15
0 20</span>
Step-by-step explanation:
<em>x</em><em> </em><em>=</em><em> </em><em>5</em><em>1</em><em>.</em><em>5</em>
<em>I'm </em><em>am </em><em>not </em><em>sure </em><em>about</em><em> </em><em>it </em><em>since </em><em>in </em><em>the</em><em> </em><em>case </em><em>it </em><em>is </em><em>not </em><em>g</em><em>iven </em><em>that </em><em>how </em><em>much </em><em>miles </em><em>he </em><em>walk </em><em>in </em><em>total </em><em>so.</em>
<em>I </em><em>just </em><em>added </em><em>the </em><em>no.</em><em> </em><em>of </em><em>miles </em><em>he </em><em>walk </em><em>in </em><em>part </em><em>A </em><em>and </em><em>B </em><em>so </em><em>I </em><em>got </em><em>x </em><em>=</em><em> </em><em>5</em><em>1</em><em>.</em><em>5</em>
<em><u>hope </u></em><em><u>this </u></em><em><u>answer </u></em><em><u>helps </u></em><em><u>you </u></em><em><u>dear.</u></em><em><u>.</u></em><em><u>.</u></em><em><u>take </u></em><em><u>care </u></em><em><u>and </u></em><em><u>stay </u></em><em><u>safe!</u></em>
Answer:
The answer is c) 761.0
Step-by-step explanation:
Mathematical hope (also known as hope, expected value, population means or simply means) expresses the average value of a random phenomenon and is denoted as E (x). Hope is the sum of the product of the probability of each event by the value of that event. It is then defined as shown in the image, Where x is the value of the event, P the probability of its occurrence, "i" the period in which said event occurs and N the total number of periods or observations.
The variance of a random variable provides an idea of the dispersion of the random variable with respect to its hope. It is then defined as shown in the image.
Then you first calculate E [x] and E [
], and then be able to calculate the variance.
![E[x]=0*\frac{1}{40} +10*\frac{1}{20} +50*\frac{1}{10} +100*\frac{33}{40}](https://tex.z-dn.net/?f=E%5Bx%5D%3D0%2A%5Cfrac%7B1%7D%7B40%7D%20%2B10%2A%5Cfrac%7B1%7D%7B20%7D%20%2B50%2A%5Cfrac%7B1%7D%7B10%7D%20%2B100%2A%5Cfrac%7B33%7D%7B40%7D)
![E[x]=0+\frac{1}{2} +5+\frac{165}{2}](https://tex.z-dn.net/?f=E%5Bx%5D%3D0%2B%5Cfrac%7B1%7D%7B2%7D%20%2B5%2B%5Cfrac%7B165%7D%7B2%7D)
E[X]=88
So <em>E[X]²=88²=7744</em>
On the other hand
![E[x^{2} ]=0^{2} *\frac{1}{40} +10^{2} *\frac{1}{20} +50^{2} *\frac{1}{10} +100^{2} *\frac{33}{40}](https://tex.z-dn.net/?f=E%5Bx%5E%7B2%7D%20%5D%3D0%5E%7B2%7D%20%2A%5Cfrac%7B1%7D%7B40%7D%20%2B10%5E%7B2%7D%20%2A%5Cfrac%7B1%7D%7B20%7D%20%2B50%5E%7B2%7D%20%2A%5Cfrac%7B1%7D%7B10%7D%20%2B100%5E%7B2%7D%20%2A%5Cfrac%7B33%7D%7B40%7D)
E[x²]=0+5+250+8250
<em>E[x²]=8505
</em>
Then the variance will be:
Var[x]=8505-7744
<u><em>Var[x]=761
</em></u>