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.2c
Textbook Question
The random variable x is normally distributed with the given parameters. Find each probability.
c. μ = 5.5, σ ≈ 0.08, P(5.36 < x < 5.64)

1
Step 1: Understand the problem. The random variable x follows a normal distribution with mean (μ) = 5.5 and standard deviation (σ) ≈ 0.08. We are tasked with finding the probability that x lies between 5.36 and 5.64, i.e., P(5.36 < x < 5.64).
Step 2: Standardize the values of x to convert them into z-scores using the formula: z = (x - μ) / σ. For the lower bound (x = 5.36), calculate z₠= (5.36 - 5.5) / 0.08. For the upper bound (x = 5.64), calculate z₂ = (5.64 - 5.5) / 0.08.
Step 3: Use the standard normal distribution table (or a calculator) to find the cumulative probabilities corresponding to zâ‚ and zâ‚‚. Let Φ(z) represent the cumulative probability for a given z-score. Find Φ(zâ‚) and Φ(zâ‚‚).
Step 4: Compute the probability P(5.36 < x < 5.64) by subtracting the cumulative probability at zâ‚ from the cumulative probability at zâ‚‚. This can be expressed as: P(5.36 < x < 5.64) = Φ(zâ‚‚) - Φ(zâ‚).
Step 5: Interpret the result. The value obtained represents the probability that the random variable x falls within the range 5.36 to 5.64 under the given normal distribution.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Normal Distribution
The normal distribution is a continuous probability distribution characterized by its bell-shaped curve, defined by its mean (μ) and standard deviation (σ). It is symmetric around the mean, meaning that approximately 68% of the data falls within one standard deviation from the mean, and about 95% falls within two standard deviations. This distribution is fundamental in statistics as many real-world phenomena tend to follow this pattern.
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Standard Normal Distribution
The standard normal distribution is a special case of the normal distribution where the mean is 0 and the standard deviation is 1. To find probabilities for any normal distribution, we often convert the values to the standard normal distribution using the z-score formula: z = (x - μ) / σ. This transformation allows us to use standard normal distribution tables or software to find probabilities associated with specific ranges of values.
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Probability Calculation
Calculating probabilities for a normal distribution involves finding the area under the curve between two points. For the given parameters, P(5.36 < x < 5.64) can be determined by calculating the z-scores for both values and then using the standard normal distribution to find the corresponding probabilities. The difference between these probabilities gives the desired probability for the range specified.
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