Atkins Weight Loss Program In a test of weight loss programs, 40 adults used the Atkins weight loss program. After 12 months, their mean weight loss was found to be 2.1 lb, with a standard deviation of 4.8 lb. Construct a 90% confidence interval estimate of the standard deviation of the weight loss for all such subjects. Does the confidence interval give us information about the effectiveness of the diet?
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 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 22m
- 11. Correlation1h 6m
- 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 7.c.10a
Textbook Question
Tour de France Listed below are the average speeds (km/h) of winners of the Tour de France men’s bicycle race. The speeds are listed in order by year, beginning with the year 2000.
a. Construct a 95% confidence interval estimate of the population mean.

1
Step 1: Identify the sample data provided in the problem. These are the average speeds (in km/h) of the Tour de France winners from the year 2000 onward. Denote the sample size as n, the sample mean as \( \bar{x} \), and the sample standard deviation as \( s \).
Step 2: Determine the confidence level, which is 95% in this case. The corresponding critical value (\( t^* \)) can be found using a t-distribution table or statistical software, based on the degrees of freedom \( df = n - 1 \).
Step 3: Use the formula for the confidence interval of the population mean: \( \bar{x} \pm t^* \cdot \frac{s}{\sqrt{n}} \), where \( \bar{x} \) is the sample mean, \( t^* \) is the critical value, \( s \) is the sample standard deviation, and \( n \) is the sample size.
Step 4: Plug the values of \( \bar{x} \), \( t^* \), \( s \), and \( n \) into the formula. Compute the margin of error \( ME = t^* \cdot \frac{s}{\sqrt{n}} \). Then calculate the lower bound as \( \bar{x} - ME \) and the upper bound as \( \bar{x} + ME \).
Step 5: Interpret the result. The 95% confidence interval provides a range of values within which the true population mean of the average speeds is likely to fall, with 95% confidence.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Confidence Interval
A confidence interval is a range of values, derived from sample statistics, that is likely to contain the population parameter with a specified level of confidence, typically 95%. It provides an estimate of uncertainty around the sample mean, indicating how much the sample mean might vary from the true population mean.
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Sample Mean
The sample mean is the average of a set of observations drawn from a larger population. It serves as a point estimate of the population mean and is calculated by summing all sample values and dividing by the number of observations. The sample mean is crucial for constructing confidence intervals.
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Standard Error
The standard error measures the variability of the sample mean from the true population mean. It is calculated by dividing the sample standard deviation by the square root of the sample size. A smaller standard error indicates that the sample mean is a more accurate estimate of the population mean, which is essential for constructing a reliable confidence interval.
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