This video provides an example of how companies use large samples of product to either accept or reject an entire population.
- K-Nosh is a national gourmet dog food company.…They sell thousands of bags of dog food each day.…They sell dog food in eight, 20, and 40-pound bags.…And the 20-pound bag is by far the most popular size.…K-Nosh's high-end customers demand outstanding products…and excellent service.…Customers don't want a bag with less than 20 pounds.…So while the bag is labeled as 20 pounds,…K-Nosh sets the desired weight of each bag…at 20.15 pounds to ensure customers get…at least 20 pounds in each bag.…
Each day, K-Nosh employees pull a random sample…of 100 bags out of the thousands they ship.…Based on the 100-bag sample,…they will either send out the shipment…or they will reject the shipment for that day.…Today's sample had an average weight of 20.10 pounds,…and the population standard deviation is 0.26 pounds.…So let's start our four-step process.…
Step one, develop the hypotheses…and state the significance level.…So, let's develop our hypotheses.…Our null hypothesis, H sub zero or H-naught,…mu is greater than or equal to 20.15 pounds.…
Eddie Davila first provides a bridge from Part 1, reviewing introductory concepts such as data and probability, and then moves into the topics of sampling, random samples, sample sizes, sampling error and trustworthiness, the central unit theorem, t-distribution, confidence intervals (including explaining unexpected outcomes), and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone else who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world.
- List the three primary issues addressed in Statistics Foundations: 2.
- Recognize two key characteristics associated with simple random samples.
- Apply the Central Limit Theorem to find the average of sample means.
- Analyze random samples during hypothesis testing.
- Assess individual situations to determine whether a one-tailed or two-tailed test is necessary.
- Define acceptance sampling.