PreparED Study Materials
ST 810: ST 810
School: North Carolina State University
Number of Notes and Study Guides Available: 2
Notes
Videos
Which Confidence Level Produces the Widest Interval? Explained!
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Uncover the relationship between confidence levels and interval widths. Grasp how the range of confidence intervals broadens with increasing confidence. Understand the trade-offs in statistical certainty and estimation.
Analyzing Teen Texting: Confidence Interval & Plausibility
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Discover how to calculate the 95% confidence interval for a population proportion using sample data. Through a practical example about texting habits among American teens, learn the implications of this statistical tool and how to assess plausibility.
Probabilities with Poisson Variables X1 & X2
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Explore the complexities of two independent Poisson random variables, X1 and X2, with means ?1 = 2 and ?2 = 3. Understand the process of calculating specific event probabilities and the application of the Poisson formula. Key takeaways include the manipulation and interpretation of these statistical values.
Two-Sample T-Test: Analyzing Control vs. Supplemented Groups
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Explore the steps to conduct a two-sample t-test using a control and supplemented group. Through hypothesis testing, standard deviation, and P-value interpretation, we analyze the means of both groups to determine statistical differences.
Estimating Study Hours: A 99% Confidence Method"
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Explore how a university dean estimates freshmen study hours using statistical methods. Understand the importance of z-scores, confidence levels, and sample sizes in making accurate predictions. Learn how to ensure results are within a desired error margin.
Chi-Square Observations: Probability of Exceeding 7.779
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Discover how to compute the likelihood of observations exceeding a certain value in a chi-square distribution with 4 degrees of freedom. Using the binomial distribution formula, evaluate the chances of at most 3 out of 15 observations surpassing the 7.779 mark. Results highlight the intricacies of data distributions.