Statistics for Business Analytics: Data Analysis with Excel

Explore central tendency (mean, median, mode), dispersion (standard deviation, variance), and the normal distribution, then covariance and correlation, using Excel for practice through exercises, quizzes, and practice tests.

Explore the two main variable types in business analytics: quantitative and qualitative, and learn to distinguish subtypes—continuous versus discrete and nominal versus ordinal—with practical examples.

Identify the difference between population and sample, and emphasize using a representative sample to ensure accurate conclusions when analyzing data with Excel.

Explore a burger restaurant case study using customer-level data to analyze eight burger types, demographic information, and revenue by age group, with mean, median, standard deviation, and correlation.

Explore the mean, median, and mode, their sensitivity to outliers and skewed data, and when to apply each central tendency measure in business analytics.

Explore the arithmetic mean as the standard measure of central tendency, why it can be misleading with outliers, and how it compares to other means in business analytics.

Learn to analyze revenue data in Excel by calculating the mean, creating a histogram, and building a pivot table by age group and gender for actionable business insights.

learn how to compute the median in Excel, compare it to the mean using revenue and a vegan burger dataset, and interpret skew with histograms and outliers.

Explore the mode, the most common element in a set, and its use for discrete and categorical variables. See elections and fashion as examples of when the mode is observed.

Learn to compute the mode in Excel for continuous data and apply a workaround for categorical data using countif, max, and index/match, including handling crashes and selecting data ranges.

Explore central tendency in Excel by comparing mean and median with skewed data, including double cheeseburger and crispy chicken, and summarize variability and gender counts using average and countif.

Understand standard deviation as a key performance indicator that measures dispersion around the mean, and compare population and sample formulas to explain variability's impact on business quality and consistency.

Explore how to compute standard deviation in Excel using stdev.s and stdev.p alongside the mean, apply it to revenue data, and compare practical versus theoretical accuracy.

Master the coefficient of variation, a relative variability measure defined as standard deviation over the mean times 100, with stock returns and burger juiciness examples in Excel.

Compute the coefficient of variation as the standard deviation divided by the mean, noting that a value above 100% implies high dispersion and volatility over time.

Explore the normal distribution, also known as the Gaussian or bell curve, symmetric around the mean with the 68-95-99.7 rule, and see how standard deviation links to Six Sigma goals.

Learn when the normal distribution is appropriate in business analytics with Excel, noting skewed revenue data may favor the median over the mean, and using countifs and norm.dist for percentiles.

Covariance measures the direction of the linear relationship and joint variability between two variables, with positive, negative, or near-zero associations illustrated by height and weight, or sleep and exercise.

Explore covariance as a measure of how two variables move together and compare it to correlation, noting direction and strength from a sample perspective.

Explore correlation, with Pearson, to assess strength and direction between two variables, positive or negative, with applications in machine learning and Excel, and remember it does not imply causation.

Explore how to compute and interpret correlations in Excel using bacon and cheeseburger data, identify weak, medium, and strong relationships, and visualize with a scatter plot.

Houston Rockets leverage data analytics to favor three pointers and layups, guided by metrics like player efficiency rating, true shooting percentage, and win shares, reshaping team strategy and league play.

Master confidence intervals by calculating the standard error of the sample mean and exploring mean Z scores with Excel, quizzes, and a Guinness Beer case study on the T distribution.

Explore confidence intervals through a case study of the Diogenes pizza joint, analyzing one month of sales, price, delivery time, free delivery, and KPIs.

Understand the standard error of the sample mean and how it differs from standard deviation, and see how it informs confidence intervals by comparing sample and population means.

Learn to compute the standard error of the sample mean for price in Excel, using the data set’s standard deviation, count, and confidence intervals.

Learn how the z score standardizes values by subtracting the mean and dividing by the standard deviation, enabling comparisons across different scales with real-life GPA and GMAT examples.

learn to standardize a delivery time variable using mean and standard deviation in excel, then compute the share below 30 minutes via norm.dist, revealing about 98% below the limit.

Define and interpret confidence levels as percentage certainty in statistical conclusions. Explore how 90%, 95%, and 99% levels map to percentile bounds and underpin hypothesis testing and confidence intervals.

Learn to compute confidence levels in Excel using the normal distribution, converting confidence levels to z-scores and vice versa with norm.s.dist and norm.s.inf.

Apply confidence intervals for large samples using the standard error of the mean and the z value at a chosen level. Apple's 95% interval guides potential demand and decisions.

Compute a 99% confidence interval for the mean price using Excel with large samples, by calculating the sample mean, sample standard deviation, and margin of error from the data.

Explore how Guinness' small-sample data spurred the development of the Student's t distribution, enabling confidence intervals and hypothesis testing in statistics and business analytics.

Develop confidence intervals for small samples using the t distribution and margin of error. See an Excel example estimating population values with bounds and the impact of sample size.

Explore degrees of freedom as the number of values that can vary under constraints, and why n minus one governs confidence intervals with the t distribution, sample mean and variance.

Compute confidence intervals for small and large samples in Excel, compare margins of error, and explore t versus z scores, using mean, standard deviation, and sample size.

Encourage learners to complete the feedback form, share what's firing them up and what's missing, and fuel the halfway-through course toward the next half with greater energy.

Master hypothesis testing with an Amazon fashion returns study, learning p-values, null hypotheses, type I and II errors, publication bias, two-sample t tests, and one-tailed vs two-tailed decisions in Excel.

Apply hypothesis testing to Amazon fashion returns to confirm size chart inaccuracies, then update size charts and measuring guidance to reduce returns and improve customer satisfaction.

Formulate the null hypothesis as the status quo and the alternate as the relationship to prove; use data to decide whether to reject H0 in favor of H1, never prove either.

Understand the p value as the likelihood results are due to chance and how it guides rejecting or not rejecting the null hypothesis in hypothesis testing.

Explore type I and type II errors with examples of false positives and false negatives, relating to the null hypothesis, alpha, beta, and the confusion matrix.

Analyze publication bias in statistics by comparing published and unpublished antidepressant trial data, revealing that positive results distort evidence and may mask weaker effectiveness and safety risks.

Learn how to test hypotheses with known variance using the z distribution, computing z-scores, and deciding to reject or fail to reject the null hypothesis at chosen confidence levels.

Explore hypothesis testing in the Tesla Model X production case study, linking zero defects and high quality to factors like production line, weather, and workers.

Learn to perform hypothesis testing in Excel with known variance and both two-tailed and one-tailed tests, including null and alternative hypotheses, z-scores, and p-values for 95% confidence.

Test a hypothesis with unknown variance using the t distribution in a two-tailed t test. Compare a sample mean of 6.5 to a population mean of 6 using Excel.

Learn to perform a two-tailed t test in excel with unknown variance, using the sample mean and standard deviation to compute the t statistic and p-value, decide if 2.2 differs.

Apply the paired t test to compare means of two related groups, determine a statistically significant difference, and compute the delta and standard deviation of differences in Excel.

Perform a two-tailed paired t-test in Excel to compare month one versus month two production, using data analysis toolpak or t.test, and interpret the p-value against 0.05.

Use a two-sample t test to compare means of groups and determine significance, illustrated with pizza consumption by horror versus romantic movie watchers, including variance checks and Welch's t test.

Learn to compare two shifts' productivity with a two-sample, two-tailed t-test in Excel for data analysis, using an F test to choose equal-variance assumptions and interpreting p-values.

Explore one-tailed hypothesis testing, distinguishing left and right tails, and compare a sample mean to a population mean with directed alternative hypotheses, confidence levels, and one-tailed t tests.

Explore one-tailed tests with known and unknown variance, learn how to frame hypotheses and read p-values, and compare with paired t tests and two-sample t tests.

Explore one-tailed hypothesis tests in Excel using paired and two-sample t tests, with an F-test for variances, and interpret p-values to decide between null and alternate hypotheses.

Master regression analysis with Excel. Start from linear regression and advance to multi linear regression and model evaluation, using scatter plots and real world case studies to inform decisions.

Explore how regression analysis uses diamond characteristics: carat, weight, certification, color, and clarity, to predict diamond price from data in a spreadsheet.

Linear regression uses a diamond case study to show how carats affect price, clarifying that regression is not correlation, with the equation Y = A + B X + E.

Explore regression analysis in Excel using the pricing diamonds data to study how carat influences price, via correlation and scatter plots, and begin linear regression.

apply a linear regression in Excel using the Data Analysis Toolpak, inputting price as y and carat as x, then interpret coefficients with a coefficient interpreter and round results.

Explore how regression output uses multiple R, R squared, adjusted R square, degrees of freedom, and statistical significance to explain the relationship between study effort and exam scores.

Analyze linear regression in Excel by interpreting correlation, R-squared, coefficients, and confidence intervals for price and carrot. Focus on the coefficient and its business relevance to interpret the price-carrot relationship.

Explore how to use dummy variables to represent categorical data in regression analysis, and learn why the dummy variable trap—perfect multicollinearity—requires removing one variable while preserving information.

This lecture demonstrates performing linear regression with dummy variables in Excel, converting categorical fields into binary columns, avoiding the dummy variable trap, and interpreting coefficients for price.

This case study explains linearity bias in statistics and business analytics, showing how extrapolating a straight line with non-linear data yields flawed conclusions and limited validity to similar data.

Explore multilinear regression by extending linear regression with multiple predictors to explain diamond price, covering predictors such as color, clarity, and owner, and addressing issues like multicollinearity and overfitting.

Explore how to perform multilinear regression in Excel 16 using the data analysis toolpak, including creating dummy variables for color and clarity, interpreting coefficients, and assessing adjusted R square.

Explore the balance between overfitting and underfitting in regression and other models, focusing on accuracy, complexity, training versus test data, and statistical significance of value-added predictors.

Learn to randomly split data into training and test sets, with 80/20, train on representative data, and evaluate on unseen test data to ensure robust predictions.

Learn to create a training and test split in Excel using the rand function, paste as values, and 80/20 allocation training and test tabs for predictions and multi linear regression.

Master multilinear regression using the data analysis package to build predictions for price, interpreting coefficients and intercept while examining predictor significance and visualizing results with a scatter plot.

Assess regression accuracy using MAE, RMSE, and MAP to compare actual values with predictions; understand their interpretability, handling of outliers, and how to compute them in Excel.

Learn to assess a regression model in Excel by calculating mean absolute error, mean absolute percentage error, and root mean squared error from train and test data.

Examine how regression analysis suggested estrogen lowered heart attack risk. Subsequent trials revealed higher risks, underscoring that correlation is not causation and that regression is a starting point.

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