Given that Michael Jordan had a career free throw average of 0.8 and if Jordan was going to the free throw line 6 times in a game: What is the probability he gets at most 4 baskets?
Contingency Table
A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.
Binomial Distribution
Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.
Given that Michael Jordan had a career free throw average of 0.8 and if Jordan was going to the free throw line 6 times in a game:
What is the
Did I calculate correctly? Thank you.
![### Binomial Probability Calculation Example
#### Objective:
Determining probability using the binomial formula.
#### Formula:
The binomial probability formula is:
\[ P(x) = \binom{n}{x} p^x q^{n-x} \]
where
- \(\binom{n}{x}\) is the binomial coefficient
- \( p \) is the probability of success on a single trial
- \( q \) is the probability of failure on a single trial
- \( n \) is the number of trials
- \( x \) is the number of successes
The binomial coefficient is calculated as:
\[ \binom{n}{x} = \frac{n!}{x!(n-x)!} \]
#### Example Calculation:
Given:
- \[ p = 0.8 \]
- \[ q = 0.2 \]
- \[ n = 6 \]
- \[ x = 4 \]
First, calculate the binomial coefficient:
\[ \binom{6}{4} = \frac{6!}{4!(6-4)!} \]
Expanding and simplifying:
\[ \binom{6}{4} = \frac{6 \times 5 \times 4 \times 3 \times 2 \times 1}{(4 \times 3 \times 2 \times 1)(2 \times 1)} = \frac{720}{24 \times 2} = \frac{720}{48} = 15 \]
Now we substitute back into the binomial formula:
\[ P(4) = \binom{6}{4} (0.8)^4 (0.2)^2 \]
\[ P(4) = 15 (0.4096) (0.04) \]
Multiplying these values:
\[ P(4) = 15 \times 0.4096 \times 0.04 = 0.24576 \]
So, the probability \( P(4) = 0.24576 \).
#### Conclusion:
By following these steps, calculations for specific outcomes using the binomial probability formula can be efficiently determined.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F1bc805eb-60ed-457b-ab8e-02c516698c74%2F1ea469bb-a501-466a-8bb0-1e75da963a99%2F56zsnjg_processed.jpeg&w=3840&q=75)

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