STA 3033 FIU: Introduction to Probability and Statistics for CS | StudySoup

PreparED Study Materials

STA 3033: Introduction to Probability and Statistics for CS

School: Florida International University

Number of Notes and Study Guides Available: 0

Videos

U.S. Adult Satisfaction in 2010: Confidence Interval Analysis
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Discover how to construct a confidence interval for understanding U.S. adult satisfaction. Using a sample from a 2010 Gallup Poll, we unveil the proportion of adults pleased with the state of affairs, backed by statistical calculations.

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.

Understanding Mutually Exclusive Events: Real-life Scenarios
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Explore the concept of mutually exclusive events in probability, using practical examples like rolling a die, selecting college students, and understanding political party registrations.

Crunching Car Sales Data: Size, Color, and Probability Insights
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Uncover the likelihood of different car combinations by size and color from a dealer's sales data. Learn how to calculate probabilities, from the chances of a small car being black to the odds a non-small car isn't gray. An insightful look into understanding car preferences.

Calculating Confidence Interval for Educational TV Viewership
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Learn how to determine a 90% confidence interval for the proportion of people who watch educational television based on a survey. Understand how to utilize the z-score and interpret results, guiding decisions for a television company's publicity efforts.

Testing the 123-Gallon Daily Water Claim: Fact or Fiction?
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Explore the claim that average daily water consumption is 123 gallons. Using statistical testing, we evaluate a new sample against the Old Farmer’s Almanac claim. Learn how to interpret p-values and t-scores in hypothesis testing.

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