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
7. Sampling Distributions & Confidence Intervals: Mean
Introduction to Confidence Intervals
Problem 6.5.12
Textbook Question
Determining Normality. In Exercises 9–12, refer to the indicated sample data and determine whether they appear to be from a population with a normal distribution. Assume that this requirement is loose in the sense that the population distribution need not be exactly normal, but it must be a distribution that is roughly bell-shaped.
Dunkin’ Donuts The drive-through service times (seconds) of Dunkin’ Donuts lunch customers, as listed in Data Set 36 “Fast Food” in Appendix B

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Step 1: Organize the data. Begin by listing the drive-through service times provided in the data set. Ensure the data is sorted in ascending order to make further analysis easier.
Step 2: Create a histogram. Divide the range of the data into intervals (bins) and plot the frequency of data points in each bin. A roughly bell-shaped histogram suggests normality.
Step 3: Construct a normal probability plot (Q-Q plot). Plot the quantiles of the sample data against the quantiles of a standard normal distribution. If the points form a straight line, the data is approximately normal.
Step 4: Calculate summary statistics. Compute the mean, median, and standard deviation of the data. For a normal distribution, the mean and median should be close, and the data should be symmetric around the mean.
Step 5: Perform a formal test for normality. Use statistical tests such as the Shapiro-Wilk test or Anderson-Darling test to assess whether the data comes from a normal distribution. Compare the p-value to the significance level (e.g., 0.05) to make a conclusion.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Normal Distribution
Normal distribution is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. It is often represented as a bell-shaped curve, where the mean, median, and mode are all equal. Understanding this concept is crucial for determining if a dataset approximates a normal distribution.
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Central Limit Theorem
The Central Limit Theorem states that the sampling distribution of the sample mean will tend to be normally distributed, regardless of the shape of the population distribution, as the sample size becomes large. This theorem is fundamental in statistics because it allows for the use of normal distribution techniques even when the original data is not normally distributed, provided the sample size is sufficiently large.
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Calculating the Mean
Normality Tests
Normality tests are statistical tests used to determine if a dataset follows a normal distribution. Common tests include the Shapiro-Wilk test and the Kolmogorov-Smirnov test. These tests provide a formal method to assess normality, which is essential for validating assumptions in various statistical analyses, particularly those that rely on normality.
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Step 2: Calculate Test Statistic
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