Statistics and Probability using Excel - Statistics A to Z

Explore probability foundations and probability distributions, learn to calculate probability in Excel, and study key laws and distributions such as Poisson, binomial, exponential, and normal.

Celebrate this milestone in statistics and probability using Excel, part of the Statistics A to Z course, and access updates, resources, and an AI assistant for support.

Explore how probability quantifies uncertainty and informs business decisions. Learn to implement probability concepts in software using the sample space and event definitions.

Explore how to use Excel to calculate probability values for coin toss and dice roll, handling exclusive events, equal likelihood, and using count A to count text sample spaces.

Explore calculating nonuniform probabilities in Excel using past project data. Use countif and pivot tables to compute month-based completion probabilities (3–7 months) from 20 projects.

Explore the complementary law of probability and the addition law, learning how P(A^c)=1−P(A) and P(A∪B)=P(A)+P(B)−P(A∩B) with examples like rain, dice, and two projects.

Apply complementary and addition laws to calculate probabilities in Excel using dice and project timelines, and visualize results with column charts of sample space versus probability.

Learn how probability distributions describe outcomes and their probabilities, visualize them with charts, and summarize them with mean (mu), variance, and standard deviation.

Calculate mean (expected value mu), variance, and standard deviation in Excel from a probability distribution or raw data, using an x-p column and the corresponding formulas.

Explore discrete and continuous random variables, distinguishing outcomes like coin tosses and weights, and learn how discrete probability mass distributions differ from continuous probability density distributions.

Explore predefined probability distributions for business problems, focusing on practical applications of both discrete and continuous distributions to identify patterns and predict outcomes without heavy calculations.

Explore the discrete uniform probability distribution, where each outcome has equal probability 1/n, with examples like dice, coins and cards, and graphs showing equal-height bars.

Explore the discrete binomial probability distribution, including when to use it in sequences of two-outcome trials, the parameters p, n, and the number of successes X, with practical examples.

Explore solving a binomial distribution in excel using N=100, p=0.17, and X=20 to compute exact and cumulative probabilities.

Use Poisson distribution in Excel to compute car demand probabilities with a mean of 13, calculating exact and cumulative values, more than 30 via complement, and visualize the inventory implications.

Explore continuous probability distributions and the probability density function, showing how probabilities arise from areas under the curve over intervals.

Explains the uniform continuous probability distribution, showing a constant probability density function over 120 to 140 g, how to compute interval probabilities, the mean 130 g, and variance 33.33.

Explore the normal distribution, also called the bell curve, defined by mu and sigma, its symmetry, and how to compute probabilities with area under the curve and z-scores in Excel.

Learn to compute replacement probability under a normal distribution with mean 4500 and standard deviation 600, using Excel norm.dist and norm.inv for the 3650-day ten-year window and the 3878-day 15% cutoff.

Explore exponential distribution and its link to Poisson distribution, using lambda and mu to model time between events, with a practical airline boarding-pass example.

Conduct a practical session on the exponential distribution using Excel to compute the cumulative probability of interarrival times, convert units, and plot the exponential curve for boarding pass wait times.

Explore probability foundations and probability distributions, learn to calculate probability in Excel, study the key laws of probability, and review Poisson, binomial, exponential, and normal distributions.

Learn how sampling gathers business insights, distinguishes population from sample, and compares probability versus non-probability methods, including random, stratified, cluster, multi-stage, and systematic sampling.

Select a sample to estimate population parameters like mean, standard deviation, and proportion using sample statistics x̄, s, and p̄. Learn point estimation and interval estimates with confidence levels.

Learn to perform random sampling in Excel by assigning rand numbers to a population, sorting to shuffle, and selecting first 50 households for income and voting data for candidate a.

Compute the point estimate of the population mean from a 50-household sample in Excel, using the sample mean, standard deviation, and the sample proportion.

Explore the sampling distribution of the sample mean and proportion, population mean and proportion values, and standard deviations, and how the central limit theorem enables normal approximations for large samples.

Demonstrates sampling results on a six-element population, showing mean of all samples equals population mean and standard deviation of sample means equals population standard deviation over square root of n.

Learn to construct interval estimates around a point estimate using margin of error and confidence levels, with z or t distributions for mean and proportion.

Construct interval estimates for population mean and proportion by using the sample mean and s/√n with n=50, and a 95% confidence level via the t value.

Calculate the standard deviation of proportion from p*(1-p)/n using n=50 and build a 95% interval estimate for proportions, yielding 46% to 74% for a 60% sample proportion.

Fix the half-interval width first to determine sample size. Use n = (t s / h)^2 with pilot data to estimate s and t from degrees of freedom.

Compute 99% confidence intervals for the mean laptop price and the proportion of MBA students willing to pay for premium branded speakers, using data from 40 students.

Calculate the estimated mean test score and the proportion of employees who attended the csr workshop using a 90 percent confidence level from a sample of 50 employees.

Learn how to perform hypothesis testing by formulating null and alternate hypotheses, choosing one- or two-tailed tests, and interpreting evidence in business contexts.

Examine type I and type II errors in hypothesis testing. Learn how rejecting or not rejecting the null hypothesis or alternative impacts decisions and costs.

Learn the hypothesis testing process with null and alternative hypotheses and a one-sided test, using sales data for new versus old packaging to illustrate sampling distribution and decision making.

Clarify hypothesis testing by linking p values to alpha, showing how a p value leads to rejecting the null hypothesis in favor of the alternative and type I error risk.

Learn to calculate p-values for mean tests using t or z statistics, compare against alpha, and decide whether to reject the null hypothesis in one- or two-tailed scenarios.

Explore Excel's t and normal distribution formulas, including t.dist, t.dist.2t, t.inverse, and norm.dist, to compute left, right, and two-tailed areas and the corresponding z or t values.

Explore normal distribution using Excel formulas like norm.dist and norm.inv to compute left-tailed, right-tailed, and two-tailed probabilities. See how mean and standard deviation affect probabilities and z values.

Use a one-tailed t-test on 30 days of sales data to test if the new design increases daily sales from 300, with t=4.79, p=2.23e-5, alpha=0.01, and conclude adoption.

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