Statistics for Business Analytics and Data Science A-Z™

Kick off with a beginner-friendly statistics primer covering continuous versus discrete variables, distributions, standard deviation, normal distribution, skewness, and mean, median, and mode, plus Cantor's diagonal argument and homework assignment.

Examine the normal distribution's bell-curve form, its probability density function, and how mu and sigma define the center and spread; learn key probabilities for standard deviation ranges.

Define populations and samples, and explain how parameters describe entire populations while statistics describe samples. Explore why random sampling enables estimating population metrics with confidence and accuracy.

Explore the central limit theorem by visualizing the sampling distribution of the sample mean and observe how larger n yields a normal distribution with lower standard deviation.

Explore statistical significance and hypothesis testing with a live example and clear steps. Examine testing assumptions, intuition, pitfalls, region rejection approaches, and proportion testing, plus a homework exercise.

Learn how to determine the rejection region in hypothesis testing using z scores and z critical values. Compare direct p-values with reverse-engineering 5% significance using a millennial TV viewing example.

Evaluate a hypothesis test to prove defect reduction from 23% to under 18% with 95% confidence using a sample of 150 spoons.

Execute a one-tailed t-test to infer if the population mean of daily steps is below 10,000, using x-bar, s, and nine degrees of freedom, with 95% confidence.

Celebrate completing this course with a personal thank you, featuring Tasmania scenery from Cradle Mountain to Crater Lake, and invite learners to rate and share feedback.