Discrete Random Variables - Mean

The final exam grade distribution for all students in the introductory statistics class at a local community college is displayed in the table with [tex]$A=4$[/tex], [tex]$B=3$[/tex], [tex]$C=2$[/tex], [tex]$D=1$[/tex], and [tex]$F=0$[/tex]. Let [tex]$X$[/tex] represent the grade for a randomly selected student from the class.

\begin{tabular}{|c|c|c|c|c|c|}
\hline
Grade & 4 & 3 & 2 & 1 & 0 \\
\hline
Probability & 0.4 & 0.32 & 0.17 & 0.08 & 0.03 \\
\hline
\end{tabular}

Which statement correctly interprets the standard deviation?

A. The grade for a randomly selected student would typically vary from the expected grade by 1.16.

B. The grade for a randomly selected student would typically vary from the expected grade by 1.08.

C. The mean grade for a randomly selected student would typically vary by [tex]$1.16$[/tex] from the expected grade.

D. The mean grade for a randomly selected student would typically vary by 1.08 from the expected grade.



Answer :

To solve this problem, we'll first need to understand the basic concepts of mean, variance, and standard deviation for a discrete random variable. Let's break it down step-by-step for clarity.

1. Mean (Expected Value):
The mean of a discrete random variable [tex]\(X\)[/tex] is found by multiplying each possible value of the variable by its probability and then summing all these products. Given the grades and their corresponding probabilities:
[tex]\[ \text{Mean} (\mu) = \sum (x_i \times P(x_i)) \][/tex]
Where [tex]\(x_i\)[/tex] are the grades and [tex]\(P(x_i)\)[/tex] are their respective probabilities.

2. Variance:
Variance measures the spread of the random variable's possible values around the mean. It is calculated using the formula:
[tex]\[ \sigma^2 = \sum ((x_i - \mu)^2 \times P(x_i)) \][/tex]
Where [tex]\(\mu\)[/tex] is the mean computed earlier.

3. Standard Deviation:
The standard deviation is simply the square root of the variance:
[tex]\[ \sigma = \sqrt{\sigma^2} \][/tex]

Given the grades and probabilities as:
[tex]\[ \begin{array}{|c|c|c|c|c|c|} \hline \text{Grade} & 4 & 3 & 2 & 1 & 0 \\ \hline \text{Probability} & 0.4 & 0.32 & 0.17 & 0.08 & 0.03 \\ \hline \end{array} \][/tex]

1. Calculate the Mean:
The mean grade ([tex]\(\mu\)[/tex]) is:
[tex]\[ (4 \times 0.4) + (3 \times 0.32) + (2 \times 0.17) + (1 \times 0.08) + (0 \times 0.03) \][/tex]
This simplifies to:
[tex]\[ 1.6 + 0.96 + 0.34 + 0.08 + 0 = 2.98 \][/tex]

2. Calculate the Variance:
The variance ([tex]\(\sigma^2\)[/tex]) is:
[tex]\[ ((4 - 2.98)^2 \times 0.4) + ((3 - 2.98)^2 \times 0.32) + ((2 - 2.98)^2 \times 0.17) + ((1 - 2.98)^2 \times 0.08) + ((0 - 2.98)^2 \times 0.03) \][/tex]
Simplifying the calculations results in:
[tex]\[ 1.1596 \][/tex]

3. Calculate the Standard Deviation:
Taking the square root of the variance:
[tex]\[ \sigma = \sqrt{1.1596} = 1.076847 \][/tex]

Now, interpreting the result:
The correct interpretation of the standard deviation in the context of this problem is:
"The grade for a randomly selected student would typically vary from the expected grade by approximately 1.08."

Hence, the correct statement is:
"The grade for a randomly selected student would typically vary from the expected grade by 1.08."