The graph is an open circle at -1 with the line going towards the positive numbers.
B. 2 time 2 is 4 , 4 times two is 8, 8 times five is 40
Answer:
Either 130 or 131 would be acceptable.
Step-by-step explanation:
When we estimate, we do not use exact values.
66.17 is close to 66
44.98 is close to 45
20.15 is close to 20
Add the estimates together: 66+ 45+ 20 = 131
We could also estimate 66.17 to 65 which would give us an estimate of 130
Answer:
a) 
And we can use the probability mass function and we got:
And adding we got:

b)
c) ![P(X>3) = 1-P(X \leq 3) = 1- [P(X=0)+P(X=1)+P(X=2)+P(X=3)]](https://tex.z-dn.net/?f=P%28X%3E3%29%20%3D%201-P%28X%20%5Cleq%203%29%20%3D%201-%20%5BP%28X%3D0%29%2BP%28X%3D1%29%2BP%28X%3D2%29%2BP%28X%3D3%29%5D%20)


And replacing we got:
![P(X>3) = 1-[0.0115+0.0576+0.1369+0.2054]= 1-0.4114= 0.5886](https://tex.z-dn.net/?f=%20P%28X%3E3%29%20%3D%201-%5B0.0115%2B0.0576%2B0.1369%2B0.2054%5D%3D%201-0.4114%3D%200.5886)
d) 
Step-by-step explanation:
Previous concepts
The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".
Solution to the problem
Let X the random variable of interest, on this case we now that:
The probability mass function for the Binomial distribution is given as:
Where (nCx) means combinatory and it's given by this formula:
Part a
We want this probability:

And we can use the probability mass function and we got:
And adding we got:

Part b
We want this probability:

And using the probability mass function we got:
Part c
We want this probability:

We can use the complement rule and we got:
![P(X>3) = 1-P(X \leq 3) = 1- [P(X=0)+P(X=1)+P(X=2)+P(X=3)]](https://tex.z-dn.net/?f=P%28X%3E3%29%20%3D%201-P%28X%20%5Cleq%203%29%20%3D%201-%20%5BP%28X%3D0%29%2BP%28X%3D1%29%2BP%28X%3D2%29%2BP%28X%3D3%29%5D%20)


And replacing we got:
![P(X>3) = 1-[0.0115+0.0576+0.1369+0.2054]= 1-0.4114= 0.5886](https://tex.z-dn.net/?f=%20P%28X%3E3%29%20%3D%201-%5B0.0115%2B0.0576%2B0.1369%2B0.2054%5D%3D%201-0.4114%3D%200.5886)
Part d
The expected value is given by:

And replacing we got:

Answer and explanation:
Discrete random variables can be counted as integers and you can't divide them. Continuous random variables are counted as real numbers and are magnitudes that can´t be counted as an exact number (there will always be an uncertainty in the measurement).
a. The random variable is continuous. Distance is a measurement.
b. The random variable is discrete. You can count how many people are sitting at a computer.
c. The random variable is continuous. Weight is a measurement.
d. The random variable is discrete. You can count how many fishes were caught.
e. The random variable is discrete. You can count how many hints a web site had.