2 Course Bundle: Quality Analytics

Learn to create and interpret data visualizations in Excel, focusing on pie and donut charts, with hands-on steps using the mtcars dataset, including counting, bucketing, and applying formatting.

Explore how histograms reveal the weight distribution of cars in the mtcars data by counting points per weight bin in thousands of pounds, adjusting bins and axis labels in Excel.

Learn to use bar charts to compare groups such as four, six, and eight cylinder engines, using average horsepower as a summary statistic, plus create, label, and format the chart.

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.

Read, create, interpret, and customize a box and whisker plot in Excel to visualize distribution, minimum and maximum, quartiles, median, mean, and data spread across carburetor groups.

Explore how to visualize and compare multiple vehicle attributes with a radial chart in Excel, using miles per gallon, acceleration, and weight across models.

Learn to build a combo chart in Excel that compares each automaker’s average miles per gallon to 1979 and 2025 cafe standards, using mtcars data and target lines.

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 advanced conditional formatting in excel, including manage rules, top/bottom rules, color scales, data bars, and sparklines, to visualize cell values and compare data like earthquake magnitudes.

Apply conditional formatting in Excel to visualize data with icon sets, data bars, and color scales, and customize indicators using manage rules and percent thresholds.

discover sparklines in Excel to visualize data within cells, build compact dashboards, and customize line or column charts with highlighted high points for quick insights.

Explore control charts that track data over time to assess a stable process, using the mean and upper and lower spec limits to signal deviations, with Excel steps to implement.

Show box plots of tooth length by supplement and dose (vit c vs oj) with mean markers, then a combo chart shows 29 of 60 trials meeting 20 cm goal.

Apply conditional formatting and icon sets to visualize dosage effects, highlight smallest values with data bars, and compare Vitamin C and orange juice using descriptive statistics.

Learn to create and customize pivot tables in Excel, cross-tabulate downtime data by production line and reason code, and refresh data sources to reveal root causes.

Explore root cause analysis using pivot tables to identify downtime patterns, filter by production line and machines, and quantify events by cause and minutes of downtime.

Perform comparative analysis with pivot tables to compare downtime events across production lines and machines, using count and sum, filters, and charts to reveal top causes and best performers.

Explore pivot charts and slicers to visualize pivot table data in real time, using date grouping, filtering, and timelines to create clear, professional downtime analytics.

Learn to use pivot tables to analyze sales, costs, and profit across regions, store types, and holidays in a retail data set, revealing profitability patterns.

Leverage pivot tables to analyze store types by region, identify unprofitable strip malls, and close them and select regions to turn a $10 million profit on $52 million in sales.

Use pivot tables to analyze how promotions and markdowns affect sales by region and store type, and tailor regional strategies. Examine unemployment versus sales to derive actionable, localized marketing decisions.

Explore nominal, ordinal, and scaled data types, and learn how data type guides choosing hypothesis tests, from basic data matrices to chi-squared tests and regression analysis.

Explore sampling concepts, from population and random samples to margin of error, bias, and distributions, and learn how statistics extrapolate findings from smaller groups.

Explore normal, uniform, and exponential distributions, and learn how mean, median, and mode relate to symmetry and variance in data responses.

Explore sampling and distributions, focusing on the normal and exponential distributions and the central limit theorem. Grasp mean, median, mode, symmetry, and variance for interpreting data and informing hypothesis testing.

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.

Explore the two-sample t test, state null and alternative hypotheses, assess assumptions including independence and normality, and interpret p-values against alpha using the Excel data analysis toolpak.

Perform a two-tailed t test with unequal variances to compare a sample mean to a zero or one hypothesized mean, using Data Analysis Toolpak and alpha 0.05 to reject null.

Explore one-tailed t tests comparing IBM and Apple stock price changes, with null vs alternative hypotheses, directionality, small samples, and alpha 0.05 for deeper statistical power.

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.

Explore the chi-squared test for independence, compute degrees of freedom, and interpret the p-value from observed versus expected counts in a right-tailed contingency table.

Apply the chi-squared goodness-of-fit test to assess normality by comparing observed values to a standard normal distribution, using mean, standard deviation, and ranks.

Explore how to apply the chi squared test to assess normality, compare observed data to the standard normal distribution, and interpret p values and q-q plots.

Explore one-way ANOVA in Excel to compare multiple groups, interpret the between-groups p-value, and learn how a main effects plot helps interpretation while noting ANOVA cannot identify which groups differ.

Apply two-way ANOVA with replication to assess how swimmer performance and diet interact, revealing main effects and the interaction term when the interaction plot crosses and p < 0.05.

Explore simple linear regression and test for a relationship between two continuous variables, interpreting the regression equation and R-squared on scatter plots.

Examine simple linear regression outputs, including the regression equation, slope, and R squared, to assess relationships like height versus year and weight versus height using p values and alpha 0.05.

Learn how multiple linear regression uses several X variables to predict one Y, and build a correlation matrix in Excel’s data analysis toolpak to explore Boston housing relationships.

Explore how multiple linear regression handles both continuous and binary predictors, interpret R-squared and p-values, and refine models by removing non-significant variables to balance explanation and simplicity.

Learn to interpret multiple regression results, standardize data for apples-to-apples coefficient comparison, and assess how factors like crime per capita, zoning, and highway accessibility impact taxes using p values.

Practice hypothesis testing on Chicago crime data with t-tests and ANOVA. Compute contingency table expected values and chi-square independence for domestic vs non domestic crime, districts, and clearance rate.

demonstrates regression analysis linking crime to weather and to clearance rate, showing temperature and precipitation significantly affect crime, with r-squared values of 0.57 and 0.13 respectively.

Explore forecasting in quality analytics by distinguishing relational forecasting from time series, and evaluate predictions with mean absolute error, mean squared error, and percentage error using historic data.

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.

Apply multivariate regression to forecast the output using multiple variables, assess statistical significance to select meaningful predictors, and compare mean squared error and mean absolute error against simple regression.

Learn Monte Carlo simulation to forecast outcomes in complex, uncertain systems by running thousands of trials with interrelated inputs, guided by probability, distributions, and the central limit theorem.

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.

Apply Monte Carlo simulation to forecast downtime and staffing, using 10,000 observations to assess demand, probability of being overworked, and staffing percentiles (80, 90, 95).

Explore time series forecasting with simple moving average, weighted moving average, and exponential smoothing, using alpha parameters and historic stock data to validate predictions.

Explore how simple moving averages and exponential smoothing shape time series forecasts, revealing lag versus reactivity in stock price, crossovers, and parameter tuning to minimize mean squared error.

Learn to tune time series forecast parameters with Excel solver, minimizing mean squared error by optimizing alpha and moving average values, and exploring solver's objective function, variable cells, and constraints.

Explore parameter tuning for time series forecasts using Nile river data, comparing simple, weighted, and exponential smoothing methods, and optimizing mean squared error with Excel solver.

Learn time series forecasting parameter tuning, using weighted moving average and exponential smoothing, to minimize mean squared error with a solver that optimizes alpha and range under constraints.

Apply autoregressive forecasting by treating lagged values t minus one, two, and three as regression factors and evaluate predictions with mean absolute percent error.

Explore how the ARMA model blends autoregressive and moving average components for time series forecasting. Learn to combine a 3-day moving average with autoregressive predictions using alpha tuning.

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.

Apply autoregressive, weighted moving average, and exponential smoothing to time-series forecasting of births to gauge pediatric unit demand. Optimize parameters with mean squared error using regression inputs and solver-based tuning.

Explore forecasting with autoregressive, weighted moving average, and exponential smoothing models; treat projections as actuals when data runs out and observe convergence and mean squared error effects on forecasts.

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 how goal seek in Excel performs what-if analysis to reach a target grade, using the data tab and comparing it to solver while noting its unconstrained limitations.

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 data tables in Excel to analyze how profit, via the objective function, responds to two variables—markup and customers—through what-if scenarios, guiding pricing, volume, and sensitivity testing.

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 classic gasoline mixing problem with Excel solver to maximize profit by blending additives A, B, and C under inventory constraints.

Explore solving the traveling salesman problem with Excel Solver by minimizing total distance, using an eight-city distance matrix, integer variable cells, and constraints that each city is visited once.

Explore how to maximize profit from coffee blends by using a solver model with house, breakfast, and bold blends under inventory and safety stock constraints, plus what-if analysis.

Explore a non-linear variant of the traveling salesperson problem using an Excel solver approach, optimizing domestic and international routes to minimize total distance.

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

Learn what a control chart is as a visualization for statistical process control. Discover its history and how it identifies changes, external influences, stability, and spec limits across industries.

Learn the parts of a control chart, set control limits in Excel, and build a first chart using a data series, the average, and upper and lower spec limits.

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.

Explore nominal, ordinal, and scaled data and how they guide control chart choices. Learn variable data charts (individual and moving range) and attribute charts (pie and U chart).

Learn how to use the offset formula in Excel to switch between column references and pull the average, USL, LSL, and series data for dynamic charts.

Explore signaling with control charts for continuous data, establishing regular intervals, a random normal distribution, and a stable process to interpret points outside upper or lower limits.

Identify signals in control charts to detect external factors causing special-cause variation, differentiate from common variation, and use root-cause analysis and standards to prevent recurrence.

Explore how moderate, sustained signals in control charts reveal assignable causes like construction, guiding problem solving and countermeasures to improve process performance.

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 signals and control charts to improve processes by reducing common cause variation and shifting the mean, using Lean and Six Sigma to enhance time-to-fill metrics and customer value.

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.

Learn how u-charts extend control charts to handle multiple categorical data by tracking defects and proportions within subgroups, using a Poisson process to set meaningful control limits.

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

Discover how to signal quality issues without charts by using worksheet formulas to detect nine consecutive points above or below the mean and weak sustained signals near spec limits.

Consolidate multiple signals using nested conditional statements to produce a single, interpretable signal. Distinguish between upper spec limit and lower spec limit, labeling triggers as strong USL or strong LSL.

Consolidate six signals into three using nested if statements with and conditions, evaluating above and below mean against USL and LSL, and verify with evaluate formula for debugging.

Consolidate three signals into one column, test mutual exclusivity of upper and lower spec limits, and apply conditional formatting to create a one-column stoplight view for control charts.