Question

13. Examples of discrete random variables include which of the following? (I) The number of clicks an online advertisement receives (II) The amount of oxygen in a certain room. (III) The number of people on a flight from LA to Australia (IV) The number of traffic accidents on a particular stretch of I-80 in 2012

14. The expected value is essentially a _____________of the possible outcomes for the random variable. The weights are the corresponding probabilities for those possible outcomes. Just like the arithmetic mean, it is common for the expected value to be a decimal or fraction even when the original set of outcomes must be whole numbers.

15. A consequence of a continuous random variable having its probability distributed across an infinite, uncountable set of possible values is that the probability that any continuous random variable will equal any specific value is always ______________.

Answer #1

**Answer:**

**13.**

Here all of the given examples are taken into count & we can say that the counts are whole numbers.

So it is discrete.

I, II,III, IV all of them are counts.

**14.**

The expected value is essentially a **Weighted
average **of the possible outcomes for the random
variable.The weights are the corresponding probabilities for those
possible outcomes. Just like the arithmetic mean, it is common for
the expected value to be a decimal or fraction even when the
original set of outcomes must be whole numbers.

**15.**

A consequence of a continuous random variable having its
probability distributed across an infinite, uncountable set of
possible values is that the probability that any continuous random
variable will equal any specific value is always
**zero.**

A consequence of continuous random variables having an infinite,
uncountable set of possible values is that the probability of any
continuous random variable that equals to a specific value is
always ______________.
Multiple Choice
zero
very hard to tell
an extremely small number
less than one

Discrete Random Variables have either a finite or countable
number of values.
True
False
An example of continuous variables is bushels of wheat per
acre.
True
False
The Mean Value of a discrete probability distribution (denoted
by mu is a weighted average of the x-values AND represents the
average values of all possible outcomes.
True
False
Explain why in a binomial probability distribution, p + q =1.
Make one sentence work.

Discrete random variables: You are studying the number of times
that something happened (counted, not measured). For example, the
number of times that you flip a coin and it comes up "heads".
Continuous random variables: The outcomes are measured, not
counted. For example, how tall a plant grows (in centimeters) when
it has fertilizer added.
please give an example of at least one discrete random variable
and one continuous random variable that a researcher might study in
your intended career...

Which of the following statements about continuous random
variables and continuous probability distributions is/are TRUE?
I. The probability that a continuous random variable takes a
specific value is 0.
II. The probability that a continuous random variable takes a
negative value is 0.
III. The probability that any uniformly distributed random
variable takes a value less than its mean is 0.5.
IV. The probability that a normally distributed random variable
takes a value less than its mean is 0.5.

Question # 1: Which of the following are continuous random
variables?
I. The sum of numbers on a pair of two dice
II. The possible sets of outcomes from flipping ten coins
III. The possible sets of outcomes from flipping (countably)
infinite coins
IV. The possible values of outside temperature in Texas
V. The possible times that a person arrives at a restaurant
a. I & II & III
b. II & III Only
c. III & IV
d. IV...

For a discrete random variable, the probability of the random
variable takes a value within a very small interval must be
A.
zero.
B.
very small.
C.
close to 1.
D.
none of the above.
QUESTION 10
The area under the density function in a certain interval of a
continuous random variable represents
A.
randomness.
B.
the area of one rectangle.
C.
the probability of the interval.
D.
none of the above.
QUESTION 11
For any random variable, X, E(X)...

1) Which of the following statements is correct (circle one
answer)? A. A random variable is a subset of the sample space. B.
The expected value of a random variable measures the variability of
the random variable. C. If a discrete random variable has three
possible values 1, 2, and 3, then its expected value has to be one
of these three values. D. If the set of possible values of a random
variable is {-17, 0, 1.4, 5000} then...

________I. Which of the following is an example of a discrete
random variable?
The weight of a box of cookies
The length of a window frame
The number of houses in a development
The distance from home to school
________II. The following table shows the population probability
distribution of x, the random variable representing the price per
ticket, in dollars, for 20 upcoming concerts at a certain
community’s civic center.
Price
per ticket
($)
P(x)
45
0.20...

Select True or False from each pull-down menu, depending on
whether the corresponding statement is true or false.
True False
1. In the normal distribution, the
total area beneath the curve represents the probability for all
possible outcomes for a given event.
True False
2. In the normal distribution, the
mean, median, mode, and variance are all at the same position on
the horizontal axis since the distribution is symmetric.
True False
3. In the exponential distribution,
the value of ?x...

X is a discrete random variable representing number of
conforming parts in a sample and has following probability mass
function
?(?) = { ?(5 − ?) if ? = 1, 2, 3, 4
0 otherwise
i) Find the value of constant ? and justify your answer
. ii) ( Determine the cumulative distribution function of X, (in
the form of piecewise function).
iii) Use the cumulative distribution function found in question
2 to determine the following:
a) ?(2 < ?...

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