Answer:
A) Yes, because there is a steady correlation between the 2 variables
B) After 2 hours, they have traveled 100 miles
C) After 5 hours, they traveled 250 miles
D) After 1 Hour, they traveled 50 miles
In conclusion, for every hour of time, they travel a comstant speed of 50 miles.
Answer:
0.34
Step-by-step explanation:
a.
34 / 100 = 0.34
0.34 is the decimal form of the fraction 34 / 100.
Answer: 7/12
Step-by-step explanation:
number of times it landed on A = 6
number of times it landed on B = 21
number of times it landed on C = 9
Total number = 36
The empirical probability that the spinner will land on B is given by
P(B) = number of times it landed on B / Total number , that is
p(B) = 21/36
P(B) =7/12
Note: Empirical means verifiable by observation or experience rather than theory and it was verified that it landed on B 21 times.
Answer:
a) P(x=3)=0.089
b) P(x≥3)=0.938
c) 1.5 arrivals
Step-by-step explanation:
Let t be the time (in hours), then random variable X is the number of people arriving for treatment at an emergency room.
The variable X is modeled by a Poisson process with a rate parameter of λ=6.
The probability of exactly k arrivals in a particular hour can be written as:
a) The probability that exactly 3 arrivals occur during a particular hour is:
b) The probability that <em>at least</em> 3 people arrive during a particular hour is:
![P(x\geq3)=1-[P(x=0)+P(x=1)+P(x=2)]\\\\\\P(0)=6^{0} \cdot e^{-6}/0!=1*0.0025/1=0.002\\\\P(1)=6^{1} \cdot e^{-6}/1!=6*0.0025/1=0.015\\\\P(2)=6^{2} \cdot e^{-6}/2!=36*0.0025/2=0.045\\\\\\P(x\geq3)=1-[0.002+0.015+0.045]=1-0.062=0.938](https://tex.z-dn.net/?f=P%28x%5Cgeq3%29%3D1-%5BP%28x%3D0%29%2BP%28x%3D1%29%2BP%28x%3D2%29%5D%5C%5C%5C%5C%5C%5CP%280%29%3D6%5E%7B0%7D%20%5Ccdot%20e%5E%7B-6%7D%2F0%21%3D1%2A0.0025%2F1%3D0.002%5C%5C%5C%5CP%281%29%3D6%5E%7B1%7D%20%5Ccdot%20e%5E%7B-6%7D%2F1%21%3D6%2A0.0025%2F1%3D0.015%5C%5C%5C%5CP%282%29%3D6%5E%7B2%7D%20%5Ccdot%20e%5E%7B-6%7D%2F2%21%3D36%2A0.0025%2F2%3D0.045%5C%5C%5C%5C%5C%5CP%28x%5Cgeq3%29%3D1-%5B0.002%2B0.015%2B0.045%5D%3D1-0.062%3D0.938)
c) In this case, t=0.25, so we recalculate the parameter as:
The expected value for a Poisson distribution is equal to its parameter λ, so in this case we expect 1.5 arrivals in a period of 15 minutes.
Answer:
Step-by-step explanation:
<h3>Table 1</h3>
- x- values change 1 to 4
- y- values change inconsistently and repeat at -2
This is <u>not</u> a linear function
<h3>Table 2</h3>
- x- values change 1 to 4
- y- values change consistently, with common difference of -2
<u>This is a linear function</u>
<h3>Table 3</h3>
- x- values change 1 to 4
- y- values change inconsistently, the difference is not common
This is <u>not</u> a linear function
<h3>Table 4</h3>
- x- values change 1 to 4
- y- values change inconsistently, the difference is not common
This is <u>not</u> a linear function
