2 Course Bundle: Quality Analytics

Learn to build and interpret time series line charts in Excel, using year as the x-axis and miles per gallon under CAFE standards, with data labels, projections, and legends.

Explore scatter plots in Excel to reveal relationships between weight, miles per gallon, horsepower, and engine displacement, and learn to add trend lines and axis titles for clear interpretation.

Explore visualizing data in the spreadsheet with conditional formatting and spark lines, using highlight rules and top/bottom checks to create a clean, dashboard-style earthquake data view.

Explore how to formulate null and alternative hypotheses and compare groups with a hypothesis test. Learn about type I and II errors, alpha, beta, power, and interpreting rejecting the null.

Learn how the chi-squared test assesses independence in nominal data using contingency tables, expected values, and the chi-squared statistic, illustrated with hair color and sex.

Apply regression analysis to forecast non-retail acres from nitric oxide, using an intercept and coefficients, then evaluate with mean absolute error, mean squared error, and mean absolute percentage error.

Use Monte Carlo simulation to determine maintenance staffing in a manufacturing setting by modeling stochastic demand, downtime, and call-offs with normal and Poisson processes.

Explore when to use ARMA versus ARIMA for time series forecasting by testing stationarity, differencing to create stationary data, and integrating predictions back to original values.

Explore regression analysis to predict worldwide gross from production budget and multiple factors, assess significance with p-values, and normalize data to rank factors like Rotten Tomatoes score, season, and budget.

Forecast gross with regression using four predictors and an intercept, comparing normalized and unnormalized models by mape. Run a Monte Carlo demand model for popcorn, cups, kernels, inventory, and costs.

Build arima forecasts for hospital admissions using moving average and autoregressive components. Optimize alpha with excel solver, evaluate with mean squared error and mape, and forecast quarterly admissions.

Explore scenario analysis in Excel using the scenario manager to compare budget scenarios and project plans, evaluating upfront costs, yearly savings, and break-even timelines.

Explore how the Excel Solver uses the simplex method to optimize outcomes within a feasible region. Apply break even analysis and profit problems with multiple constraints, variables, and corner points.

Explore the excel solver backpack problem archetype, minimizing grams per serving while meeting calories between 1800 and 2200 and enforcing at least one serving per food group.

Solve a nonlinear integer optimization to maximize potential sales price for light, medium, and dark roasts under vacuum capacity and total bean constraints.

Explore the benefits of control charting over bar and pie charts, showing how control charts reveal trends, external factors, and process consistency through control limits and mean analysis.

Identify three assignable signals in control charts—points beyond the control limit, eight points above the average, and three near the upper limit—and use them to anticipate process variation.

Explore root cause analysis techniques like five whys and fishbone diagrams (Ishikawa), plus six m's and six p's, to identify control chart signals and fix root causes.

Learn root cause analysis using fault tree analysis and logic gates (or/and), compare reactive and proactive use, and translate insights into an actionable improvement plan.

Explore how to build p-charts for binary pass/fail data by transforming counts into proportions, using subgroups, and applying a Poisson-based control limit framework to accommodate varying sample sizes.

Explore control charts without charts fundamentals by signaling upper and lower control limits in Excel with IF statements and conditional formatting for quick alerts.