Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
6. Normal Distribution and Continuous Random Variables
Standard Normal Distribution
Problem 5.Q.11
Textbook Question
In a survey of U.S. adults, 81% feel they have little or no control over data collected about them by companies. You randomly select 250 U.S. adults and ask them whether they feel they have control over data collected about them by companies. Use this information in Exercises 11 and 12. (Source: Pew Research Center)
Determine whether you can use a normal distribution to approximate the binomial distribution. If you can, find the mean and standard deviation. If you cannot, explain why.

1
Step 1: Identify the problem type. This is a binomial distribution problem because there are two possible outcomes: either a person feels they have little or no control over their data, or they do not.
Step 2: Check the conditions for approximating a binomial distribution with a normal distribution. The conditions are: (1) The sample size (n) must be large enough, and (2) both np and n(1-p) must be greater than or equal to 5. Here, n = 250 and p = 0.81.
Step 3: Calculate np and n(1-p). Use the formulas: np = n × p and n(1-p) = n × (1-p). Substitute the values of n and p into these formulas.
Step 4: If both np and n(1-p) are greater than or equal to 5, then the normal approximation can be used. If not, explain why the approximation is not valid.
Step 5: If the normal approximation is valid, calculate the mean (μ) and standard deviation (σ) of the binomial distribution. Use the formulas: μ = n × p and σ = √(n × p × (1-p)). Substitute the values of n and p into these formulas to find the mean and standard deviation.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Binomial Distribution
A binomial distribution models the number of successes in a fixed number of independent Bernoulli trials, each with the same probability of success. In this context, the success could be defined as an adult feeling they have control over their data. The distribution is characterized by two parameters: the number of trials (n) and the probability of success (p).
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Normal Approximation
The normal approximation to the binomial distribution is applicable when certain conditions are met, specifically when both np and n(1-p) are greater than or equal to 5. This allows the binomial distribution to be approximated by a normal distribution, making calculations easier, especially for larger sample sizes. In this case, you would check if these conditions hold for the given sample size and probability.
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Using the Normal Distribution to Approximate Binomial Probabilities
Mean and Standard Deviation of a Binomial Distribution
The mean (μ) of a binomial distribution is calculated as μ = np, where n is the number of trials and p is the probability of success. The standard deviation (σ) is given by σ = √(np(1-p)). These measures provide insights into the expected number of successes and the variability around that expectation, which are essential for understanding the distribution's behavior.
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