PreparED Study Materials
STAT 2820: Reasoning With Statistics
School: Appalachian State University
Number of Notes and Study Guides Available: 2
Notes

Stt 2820 ch 1 and ch 2 notes (Statistics)
STAT 2820
Appalachian State University
3 pages | Fall 2016
Videos
Predicting Preference: Using Binomial Distribution for a New Dairy For
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Discover how the binomial distribution formula can predict the preference of judges for a new dairy drink formula. Understand the odds of different outcomes from unanimous approval to complete rejection. Dive into probability, expectations, and variance to decode consumer choices.
Understanding Null vs Alternative Hypotheses in Real-world Scenarios
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Explore various scenarios and understand the fundamental principles of the null and alternative hypotheses in statistical testing. This video breaks down how these hypotheses represent current beliefs and potential deviations, aiding in the interpretation of statistical findings.
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.
Margin of Error: Decoding the Uncertainty in Estimates
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Unravel the concept of the "margin of error" within the context of estimating children's exposure to lead-based paint. Through clear explanations, comprehend the importance of acknowledging uncertainty in statistical findings and how it represents a range for potential true values.
Job Trends Analysis: Normal Approximation & Z-Values
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Explore the nuances of determining the likelihood of specific employment patterns using statistical methods. Understand the application of the normal approximation to the binomial distribution and the role of z-values in probability determinations. Gain clarity on interpreting employment trends with the help of a standard normal table.
Battery Lifetimes: Mean, Variance & Standard Deviation
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Discover the nuances of analyzing battery lifetime data through mean, variance, and standard deviation computations. Using class boundaries, midpoints, and frequencies, gain insights into the dispersion of battery lifespans and the average deviation from the mean.














