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Ch. 7 - Estimating Parameters and Determining Sample Sizes
Triola - Elementary Statistics 14th Edition
Triola14th EditionElementary StatisticsISBN: 9780137366446Non è quello che usi tu?Cambia libro di testo
Capitolo 7, Problema 7.3.3

Red Blood Cell Count Here is a 95% confidence interval estimate of obtained by using the red blood cell counts of adult females listed in Data Set 1 “Body Data” in Appendix B:
[Image].
Identify the corresponding confidence interval estimate of and include the appropriate units.

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Step 1: Understand the problem. A confidence interval provides a range of values within which the true population parameter (in this case, the mean red blood cell count) is likely to fall. The problem asks us to identify the confidence interval and include the appropriate units.
Step 2: Identify the given confidence interval. Look at the provided data or image (if available) to determine the lower and upper bounds of the 95% confidence interval. These bounds represent the range of plausible values for the population mean.
Step 3: Verify the units of measurement. Red blood cell counts are typically measured in millions of cells per microliter (million/μL). Ensure that the units are consistent with the data provided.
Step 4: Write the confidence interval in proper notation. A confidence interval is typically expressed as (lower bound, upper bound) with the units included. For example, if the bounds are 4.2 and 5.4, the interval would be written as (4.2 million/μL, 5.4 million/μL).
Step 5: Interpret the confidence interval. Explain that we are 95% confident that the true mean red blood cell count for adult females falls within the specified range, and include the units in your interpretation.

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Confidence Interval

A confidence interval is a range of values, derived from sample statistics, that is likely to contain the true population parameter with a specified level of confidence, typically expressed as a percentage. For example, a 95% confidence interval suggests that if we were to take many samples and compute intervals, approximately 95% of those intervals would contain the true mean.
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06:33
Introduction to Confidence Intervals

Red Blood Cell Count

Red blood cell (RBC) count is a measure of the number of red blood cells in a given volume of blood, typically expressed in millions of cells per microliter (µL). This count is crucial for assessing an individual's overall health, as it can indicate conditions such as anemia or polycythemia, depending on whether the count is too low or too high.
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Fundamental Counting Principle

Units of Measurement

Units of measurement provide a standard for quantifying physical quantities. In the context of red blood cell counts, the common unit is millions of cells per microliter (µL), which allows for consistent reporting and comparison of results across different studies and populations.
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Parameters vs. Statistics
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Sample Size for Mean Find the sample size required to estimate the mean IQ of airline pilots. Assume that we want 99% confidence that the mean from the sample is within two IQ points of the true population mean. Also assume that sigma=15

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FINDING SAMPLE SIZE Instead of using Table 7-2 for determining the sample size required to estimate a population standard deviation σ, the following formula can also be used


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Constructing and Interpreting Confidence Intervals. In Exercises 13–16, use the given sample data and confidence level. In each case, (a) find the best point estimate of the population proportion p; (b) identify the value of the margin of error E; (c) construct the confidence interval; (d) write a statement that correctly interprets the confidence interval.


Eliquis The drug Eliquis (apixaban) is used to help prevent blood clots in certain patients. In clinical trials, among 5924 patients treated with Eliquis, 153 developed the adverse reaction of nausea (based on data from Bristol-Myers Squibb Co.). Construct a 99% confidence interval for the proportion of adverse reactions.

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Job Interviews In a Harris poll of 514 human resource professionals, 463 said that the appearance of a job applicant is most important for a good first impression. Use 1000 bootstrap samples to construct a 99% confidence interval estimate of the proportion of all human resource professionals believing that the appearance of a job applicant is most important for a good first impression. How does the result compare to the confidence interval found in Exercise 24 part (b) in Section 7-1?

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Determining Sample Size. Assume that each sample is a simple random sample obtained from a normally distributed population.


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