Develop confidence intervals to answer questions like these: What is the average weight of a football player? What is the average salary of a doctor?
- Political campaigns rely heavily on developing…confidence intervals for voter preference.…In many cases, these types of confidence intervals…are for proportions, but how about if we wanted to…develop confidence intervals for these types of situations?…What's the average salary of a cardiologist?…What's the weight of the average grapefruit…grown in the state of Texas?…How long does it take the average female,…20 to 29 years old, to run a mile?…In these cases, we're not looking at proportions.…
Instead, we're looking for means.…So, how do we create a confidence interval…for a population mean?…Well, it's not really that much different…than the reasoning we use to develop…confidence intervals for proportions.…Here are the formulas used to develop…a 95% confidence interval for proportions.…The sample proportion plus or minus 1.96,…times the sample's sampling error.…
Remember, 1.96 is the appropriate Z-Score…for a 95% interval.…So, if we wanted a different confidence interval,…we would just find the appropriate Z-Score.…
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.