When can the Empirical Rule be used to identify unusual results in a binomial experiment? Why can the Empirical Rule be used to identify results in a binomial experiment?
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- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 25m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA1h 57m
5. Binomial Distribution & Discrete Random Variables
Binomial Distribution
Problem 6.RE.7d
Textbook Question
Driving AgeAccording to a Gallup poll, 60% of U.S. women 18 years old or older stated that the minimum driving age should be 18. In a random sample of 15 U.S. women 18 years old or older, find the probability that:
d. Between 7 and 12, inclusive, believe that the minimum driving age should be 18.
Verified step by step guidance1
Identify the type of distribution involved. Since we are dealing with a fixed number of trials (15 women), each with two possible outcomes (believe or not believe the minimum driving age should be 18), this is a binomial distribution problem.
Define the parameters of the binomial distribution: the number of trials \(n = 15\), and the probability of success (a woman believes the minimum driving age should be 18) \(p = 0.60\).
Express the probability we want to find: the probability that the number of successes \(X\) is between 7 and 12 inclusive, i.e., \(P(7 \leq X \leq 12)\).
Use the binomial probability formula for each value of \(X\) from 7 to 12:
\(P(X = k) = \binom{n}{k} p^{k} (1-p)^{n-k}\)
where \(\binom{n}{k}\) is the binomial coefficient representing the number of ways to choose \(k\) successes out of \(n\) trials.
Calculate the total probability by summing the individual probabilities for \(k = 7, 8, 9, 10, 11, 12\):
\(P(7 \leq X \leq 12) = \sum_{k=7}^{12} \binom{15}{k} (0.60)^{k} (0.40)^{15-k}\).
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Binomial Distribution
The binomial distribution models the number of successes in a fixed number of independent trials, each with the same probability of success. Here, each woman either supports the minimum driving age of 18 (success) or not (failure), with a probability of 0.60. The distribution helps calculate probabilities for a specific number of successes in the sample.
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Mean & Standard Deviation of Binomial Distribution
Probability of a Range of Outcomes
To find the probability that the number of successes falls within a range (e.g., between 7 and 12 inclusive), sum the probabilities of each individual outcome in that range. This involves calculating binomial probabilities for each count from 7 to 12 and adding them together to get the total probability.
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Introduction to Probability
Random Sampling and Independence
Random sampling ensures each individual in the population has an equal chance of selection, making the trials independent. Independence means the outcome for one woman does not affect another's response, a key assumption for applying the binomial distribution accurately.
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Simple Random Sampling
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