Join Eddie Davila for an in-depth discussion in this video What you should know, part of Statistics Foundations: 2.
- So, you've decided to dive into a statistics course. Are you ready for it? I think you are. While this course will explore statistical concepts, numbers, charts, and probabilities, we won't be doing any significant mathematical gymnastics. If you know your math basics, adding and subtracting, multiplying and dividing, square roots. If you're comfortable with basic fractions, if you can understand that when we're discussing probabilities, 0.05 is the same thing as 5%.
0.40 is the same thing as 40%, and that 100% is the same thing as 1.00, I think you'll be fine. And hopefully, you're also comfortable with normal distribution curves and z-scores. Plus, you should be comfortable with means, medians, standard deviation, and basic probabilities. And look, even if some of those things make you a little bit uncomfortable, don't worry. Often we use pictures, charts, and tables to help illustrate the concept.
Sometimes, we attack problems in more than one way. And of course, through the power of the internet, you can always pause and rewind. So, whether you're math muscles are strong, or you're just beginning to rediscover math concepts, I think the probability of success and discovery is quite high. Thanks for exploring Statistics Fundamentals Part 2, and good luck.
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.