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.

Explore skewness in data distributions, identify left and right skew, and connect tails and outliers to the underlying data behind the chart.

Create two 1000-observation normal distributions for US heights—men and women—and identify the 2.2% tallest cutoffs to test a high-end boutique sales forecast.

Learn to build and update distributions for men and women using bins, the frequency function, and array formulas in Excel, illustrating how sampling affects mean, standard deviation, and charts.

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 sampling distribution by repeatedly sampling a population, recording the sample means (X-bar), and plotting their distribution to prepare for the central limit theorem.

Apply the central limit theorem to see that the sampling distribution of the sample mean becomes normal, regardless of the population distribution, enabling practical insights in business analytics.

Understand central limit theorem intuition: the sampling distribution of the sample mean becomes normal, with a mean equal to the population mean and standard deviation that shrinks as n increases.

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.

Learn how the z score standardizes a normal distribution by centering at mu and scaling by sigma to estimate probabilities using the z table.

Apply the central limit theorem to a Fedex cargo scenario, using the sampling distribution of the mean for 36 boxes to estimate a 98.3 percent probability of safe takeoff.

Explore a Wall Street quant scenario modeling weekly profits with a Laplace distribution, mean $95.70 and standard deviation $1,247, using central limit theorem to estimate loss risk and $20,000 profit.

Explore how the central limit theorem shapes sampling distributions in a Wall Street homework problem, comparing mean profits, large standard deviation, and the probability of hitting targets or incurring losses.

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.

Explore statistical significance through intuition and hypothesis testing, using p-values and the null versus alternative hypothesis to decide when to reject the null.

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.

Test a proportion with a 97% significance level to see if tablet ownership exceeds 58% based on 100 households. Learn to compute p-hat, z-score, and p-value.

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

Explore hypothesis testing with a spoon defect case, forming null and alternative hypotheses, using random samples, p-hat and z-score, and interpreting 95% confidence results.

Understand why you fail to reject the null hypothesis in hypothesis testing, using p-hat, z-scores, and 95% confidence to assess evidence against the alternative and guide experiment redesign.

Explore the t distribution, its heavier tails, and degrees of freedom, which adjust for small samples and unknown population standard deviation, converging toward normal distribution as degrees of freedom grow.

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.

Explore the intuition behind one-tailed and two-tailed tests, including null and alternative hypotheses, rejection regions, and why two-tailed tests are stricter due to split significance.

Explore a two-tailed hypothesis test on a bookstore spending scenario, comparing a 57 baseline to a 62 sample mean, and apply z-scores, p-values, and central limit theorem logic.

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.