Answer:
x = -6
Step-by-step explanation:
-2 = 12/x
-2x = 12
x = 12/-2
x = -6
I did this in school!!
You can do it 4 times.
I think...
Your welcome!
Answer:
14x=4-21y
Step-by-step explanation:
Answer:
(a) 
The expected number in the sample that treats hazardous waste on-site is 0.383.
(b) 
There is a 0.000169 probability that 4 of the 10 selected facilities treat hazardous waste on-site.
Step-by-step explanation:
Professional Geographer (Feb. 2000) reported the hazardous waste generation and disposal characteristics of 209 facilities.
N = 209
Only eight of these facilities treated hazardous waste on-site.
r = 8
a. In a random sample of 10 of the 209 facilities, what is the expected number in the sample that treats hazardous waste on-site?
n = 10
The expected number in the sample that treats hazardous waste on-site is given by




Therefore, the expected number in the sample that treats hazardous waste on-site is 0.383.
b. Find the probability that 4 of the 10 selected facilities treat hazardous waste on-site
The probability is given by
For the given case, we have
N = 209
n = 10
r = 8
x = 4




Therefore, there is a 0.000169 probability that 4 of the 10 selected facilities treat hazardous waste on-site.
The plot that organizes the data into 4 groups of equal sizes is box and whisker plot.
The image below shows a box and whisker plot. Following are the elements of box and whisker plot:
Minimum = This is the smallest value of the data set
Q1 = First (Lower) Quartile of the data set. 25% of the data values lie below this point
Q2 = Second Quartile or Median. This is the central value so 50% of the data values lie below this point
Q3 = Third (Upper) Quartile of the data set. 75% of the data values lie below this point.
Maximum = This is the maximum value of the data set.
Based on box and whisker plot we can compare two or more sets of data by comparing the spread of the data. We can also directly observe from the box and whisker plot if the data is uniform, normal or skewed. Using box and whisker plot we can also visualize any outliers that may be in the data.