Case Study Library
Bring practical statistical problem solving to your course
A wide selection of real-world scenarios with practical multistep solution paths. Complete with objectives, data, illustrations, insights and exercises. Exercise solutions available to qualified instructors only.
|JMP001||Medical Malpractice||Healthcare||Insurance Claims Management||Summary Statistics & Box Plot|
|JMP002||Baggage Complaints||Operations||Customer Care||Time Series Plots & Descriptive Statistics|
|JMP003||Defect Sampling||Engineering||Manufacturing Quality||Tabulation & Summary Statistics|
|JMP004||Film on the Rocks||Marketing||Research Methods||Chi-Squared Test & Distribution|
|JMP005||Improving Patient Satisfaction||Life Sciences||Quality Improvement||Correlation & Summary Statistics|
|JMP006||Price Quotes||Marketing||Pricing||One Sample t - Test|
|JMP007||Treatment Facility||Operations||Quality Improvement||Two Sample t - Test & Welch Test|
|JMP008||Siblings||General||Transforming Data||Normality & Transformation|
|JMP009||Fish Story||Finance||Resource Management||Non parametric & Wilcoxon Signed Rank Test|
|JMP010||Subliminal Messages||Social Sciences||Experiments||t - Test & Wilcoxon Rank Sums test|
|JMP011||Priority Assessment||Operations||Project Management||ANOVA & Welch Test|
|JMP012||Backgammon||General||Games||t - Test & One way ANOVA|
|JMP013||Per Capita Income||Social Sciences||Demographics||ANOVA & Kruskal-Wallis Test|
|JMP014||Kerrich : Is a Coin Fair?||General||Games of Chance||Simulation for One Proportion|
|JMP015||Lister and Germ Theory||Life Sciences||Disease||Chi-Squared Test & Relative Risk|
|JMP016||Salk Vaccine||Life Sciences||Vaccines||Chi-Squared Test & Fisher's Exact Test|
|JMP017||Smoking and Lung Cancer||Life Sciences||Oncology||Odds Ratio & Conditional Probability|
|JMP018||Mendel's Law of Inheritance||Life Sciences||Genetics||Chisquare test for Multiple Proportions|
|JMP019||Contributions||Marketing||Fundraising||Simple Linear Regression & Prediction Intervals|
|JMP020||Direct Mail||Marketing||Advertising||Time Series & Simple Linear Regression|
|JMP021||Cost Leadership||Marketing||Strategy||Curve Fitting and Regression|
|JMP022||Archosaur||Life Sciences||Paleontology||Simple Linear Regression & Transformation|
|JMP023||Cell Phone Service||Operations||Service Reliability||Multiple Linear Regression & Correlation|
|JMP024||Housing Prices||Marketing||Pricing||Multiple Linear Regression & Model Diagnostics|
|JMP025||Bank Revenue||Finance||Revenue Management||Stepwise Regression & Model Diagnostics|
|JMP026||Lost Sales||Operations||Sales||Logistic Regression & Chi Squared test|
|JMP027||Titanic Passengers||History||Demography||Logistic Regression & Odds Ratio|
|JMP028*||Credit Card Marketing||Marketing||Customer Acquisition||Classification Tree & Model Validation|
|JMP029||Call Center Improvement||Operations||Customer Care||Process Capability & Partition Model|
|JMP030||Customer Churn||Marketing||Customer Retention||Neural Networks & Variable importance|
|JMP031*||Boston Housing||Social Sciences||Socioeconomics||Predictive Modeling & Model comparison|
|JMP032||Durability of Mobile Phone Screen - Part 1||Engineering||Product Testing||Chi Squared Test & Relative Risk|
|JMP033||Durability of Mobile Phone Screen - Part 2||Engineering||Product Testing||Chi Squared Test & Odds Ratio|
|JMP034||Durability of Mobile Phone Screen - Part 3||Engineering||Product Testing||Univariate Logistic Regression|
|JMP035||Durability of Mobile Phone Screen - Part 4||Engineering||Product Testing||Multivariate Logistic Regression|
|JMP036||Online Mortgage Application||Marketing||Customer Acquisition||Population Parameter Estimation|
|JMP037||Performance of Food Manufacturing Process - Part 1||Engineering||Quality Management||Descriptive Statistics & Visualization|
|JMP038||Performance of Food Manufacturing Process - Part 2||Engineering||Quality Management||Normality & Test of Standard deviation|
|JMP039||Detergent Cleaning Effectiveness||Operations||Product Management||t - Test & ANOVA|
|JMP040||Manufacturing Systems Variation||Engineering||Quality Improvement||Variability Gauge R&R, Variance Components|
|JMP041*||Text Exploration of Patents||General||Knowledge Management||Word Cloud & Term Selection|
|JMP042||US Stock Indices||Finance||Time Series Analysis||Stationarity & Differencing|
|JMP043||Pricing Musical Instrument||Marketing||Research Methods||Conjoint, Part Worths, OLS, Utility|
|JMP044||Pricing Spectacles||Marketing||Research Methods||Discrete choice & Willingness to Pay|
|JMP045||Modeling Gold Prices||Finance||Time Series Analysis||ARIMA Models & Model Comparison|
|JMP046||Endangered||Life Sciences||Ecology||Non Parametric Kendall's Tau & Normality|
|JMP047||Manufacturing Excellence at Pharma Company Part 1||Engineering||Pharmaceutical Manufacturin||Statistical Quality Control|
|JMP048||Manufacturing Excellence at Pharma Company Part 2||Engineering||Pharmaceutical Manufacturing||Statistical Process Control|
|JMP049||Manufacturing Excellence at Pharma Company Part 3||Engineering||Pharmaceutical Manufacturing||Design of Experiments|
|JMP050||Polymerization at Lohmann Part 1||Engineering||Chemical Manufacturing||Design of Experiments|
|JMP051||Polymerization at Lohmann Part 2||Engineering||Chemical Manufacturing||Functional Data Exploration (FDE)|
|JMP052||Optimization of Microbial Cultivation Process||Engineering||Biotech Manufacturing||Design of Experiments|
|JMP053||Cluster Analysis in the Public Sector||Marketing||Demography||PCA & Clustering|
|JMP054||Forecasting Copper Prices||Finance||Time Series Forecasting||Exponential Smoothing Methods|
*: The cases with * need JMP Pro
Explore claim payment amounts for medical malpractice lawsuits and identify factors that appear to influence the amount of the payment using descriptive statistics and data visualizations.
Key words: Summary statistics, frequency distribution, histogram, box plot, bar chart, Pareto plot, and pie chart
Analyze and compare baggage complaints for three different airlines using descriptive statistics and time series plots. Explore differences between the airlines, whether complaints are getting better or worse over time, and if there are other factors, such as destinations, seasonal effects or the volume of travelers that might affect baggage performance.
Key words: Time series plots, summary statistics
Film on the Rocks
Use survey results from a summer movie series to answer questions regarding customer satisfaction, demographic profiles of patrons, and the use of media outlets in advertising.
Key words: Bar charts, frequency distribution, summary statistics, mosaic plot, contingency table, (cross-tabulations), and chi-squared test
Improving Patient Satisfaction
Analyze patient complaint data at a medical clinic to identify the issues resulting in customer dissatisfaction and determine potential causes of decreased patient volume.
Key words: Frequency distribution, summary statistics, Pareto plot, tabulation, scatterplot, run chart, correlation
Evaluate the price quoting process of two different sales associate to determine if there is inconsistency between them to decide if a new more consistent pricing process should be developed.
Key words: Histograms, summary statistics, confidence interval for the mean, one sample t-Test
Determine what effect a reengineering effort had on the incidence of behavioral problems and turnover at a treatment facility for teenagers.
Key words: Summary statistics, time series plots, normal quantile plots, two sample t-Test, unequal variance test, Welch's test
Use data from a survey of students to perform exploratory data analysis and to evaluate the performance of different approaches to a statistical analysis.
Key words: Histograms, normal quantile plots, log transformations, confidence intervals, inverse transformation
Fish Story: Not Too Many Fishes in the Sea
Determine whether subliminal messages were effective in increasing math test scores, and if so, by how much.
Key words: Histograms, summary statistics, box plots, t-Test and pooled t-Test, normal quantile plot, Wilcoxon Rank Sums test, Cohen's d
Determine whether a software development project prioritization system was effective in speeding the time to completion for high priority jobs.
Key words: Summary statistics, histograms, normal quantile plot, ANOVA, pairwise comparison, unequal variance test, and Welch's test
Determine if a backgammon program has been upgraded by comparing the performance of a player against the computer across different time periods.
Key words: Histograms, confidence intervals, stacking data, one-way ANOVA, unequal variances test, one-sample t-Test, ANOVA table and calculations, F Distribution, F ratios
Per Capita Income
Use data from the World Factbook to explore wealth disparities between different regions of the world and identify those with the highest and lowest wealth.
Key words: Geographic mapping, histograms, log transformation, ANOVA, Welch's ANOVA, Kruskal-Wallis
Kerrich: Is a Coin Fair?
Using outcomes for 10,000 flips of a coin, use descriptive statistics, confidence intervals and hypothesis tests to determine whether the coin is fair.
Key words: Bar charts, confidence intervals for proportions, hypothesis testing for proportions, likelihood ratio, simulating random data, scatterplot, fitting a regression line
Lister and Germ Theory
Use results from a 1860’s sterilization study to determine if there is evidence that the sterilization process reduces deaths when amputations are performed.
Key words: Mosaic plots, contingency tables, Pearson and likelihood ratio tests, Fisher's exact test, two-sample proportions test, one- and two-sided tests, confidence interval for the difference, relative risk
Using data from a 1950’s study, determine whether the polio vaccine was effective in a cohort study, and, if it was, quantify the degree of effectiveness.
Key words: Bar charts, two-sample proportions test, relative risk, two-sided Pearson and likelihood ratio tests, Fisher's exact test, and the Gamma measure of association
Smoking and Lung Cancer
Use the results of a retrospective study to determine if there is a positive association between smoking and lung cancer, and estimate the risk of lung cancer for smokers relative to non-smokers.
Key words: Mosaic plots, two-by-two contingency tables, odds ratios and confidence intervals, conditional probability, hypothesis tests for proportions (likelihood ratio, Pearson's, Fisher's Exact, two sample tests for proportions)
Mendel's Laws of Inheritance
Evaluate different regression models to determine if sales at small retail shop are influence by direct mail campaign and using the resulting models to predict sales based upon the amount of marketing.
Key words: Time series plots, simple linear regression, lagged variables, predicted values, prediction intervals
Assess the effectiveness of a cost leadership strategy in increasing market share, and assess the potential for additional gains in market share under the current strategy.
Key words: Simple linear regression, spline fitting, transformations, predicted values, prediction intervals
Archosaur: The Relationship Between Body Size and Brain Size
Analyze data on the brain and body weight of different dinosaur species to determine if a proposed statistical model performs well at describing the relationship and use the model to predict brain weight based on body weight.
Key words: Histogram and summary statistics, fitting a regression line, log transformations, residual plots, interpreting regression output and parameter estimates, inverse transformations
Cell Phone Service
Determine whether wind speed and barometric pressure are related to phone call performance (percentage of dropped or failed calls) and use the resulting model to predict the percentage of bad calls based upon the weather conditions.
Key words: Histograms, summary statistics, simple linear regression, multiple regression, scatterplot, 3D-scatterplot
After determining which factors relate to the selling prices of homes located in and around a ski resort, develop a model to predict housing prices.
Key words: Scatterplot matrix, correlations, multiple regression, stepwise regression, multicollinearity, model building, model diagnostics
A bank wants to understand how customer banking habits contribute to revenues and profitability. Build a model that allows the bank to predict profitability for a given customer. The resulting model will be used to forecast bank revenues and guide the bank in future marketing campaigns.
Key words: Log transformation, stepwise regression, regression assumptions, residuals, Cook’s D, model coefficients, singularity, prediction profiler, inverse transformations
Determine whether certain conditions make it more likely that a customer order will be won or lost.
Key words: Bar charts, frequency distribution, mosaic plots, contingency table, chi-squared test, logistic regression, predicted values, confusion matrix
Use the passenger data related to the sinking of the RMS Titanic ship to explore some questions of interest about survival rates for the Titanic. For example, were there some key characteristics of the survivors? Were some passenger groups more likely to survive than others? Can we accurately predict survival?
Key words: Logistic regression, log odds and logit, odds, odds ratios, prediction profiler
Credit Card Marketing
A bank would like to understand the demographics and other characteristics associated with whether a customer accepts a credit card offer. Build a Classification model that will provide insight into why some bank customers accept credit card offers.
Key words: Classification trees, training & validation, confusion matrix, misclassification, leaf report, ROC curves, lift curves
Call Center Improvement: Visual Six Sigma
The scenario relates to the handling of customer queries via an IT call center. The call center performance is well below best in class. Identify potential process changes to allow the call center to achieve best in class performance.
Key words: Interactive data visualization, graphs, distribution, tabulate, recursive partitioning, process capability, control chart, multiple regression, prediction profiler
Analyze the factors related to customer churn of a mobile phone service provider. The company would like to build a model to predict which customers are most likely to move their service to a competitor. This knowledge will be used to identify customers for targeted interventions, with the ultimate goal of reducing churn.
Key words: Neural networks, activation functions, model validation, confusion matrix, lift, prediction profiler, variable importance
Build a variety of prediction models (multiple regression, partition tree, and a neural network) to determine the one that performs the best at predicting house prices based upon various characteristics of the house and its location.
Key words: Stepwise regression, regression trees, neural networks, model validation, model comparison
Durability of Mobile Phone Screen - Part 1
Evaluate the durability of mobile phone screens in a drop test. Determine if a desired level of durability is achieved for each of two types of screens and compare performance.
Key words: Confidence Intervals, Hypothesis Tests for One and Two Population Proportions, Chi-square, Relative Risk
Durability of Mobile Phone Screen - Part 2
Evaluate the durability of mobile phone screens in a drop test at various drop heights. Determine if a desired level of durability is achieved for each of three types of screens and compare performance.
Key words: Contingency analysis, comparing proportions via difference, relative risk and odds ratio
Durability of Mobile Phone Screen - Part 3
Evaluate the durability of mobile phone screens in a drop test across various heights by building individual simple logistic regression models. Use the models to estimate the probability of a screen being damaged across any drop height.
Key words: Single variable logistic regression, inverse prediction
Durability of Mobile Phone Screen - Part 4
Evaluate the durability of mobile phone screens in a drop test across various heights by building a single multiple logistic regression model. Use the model to estimate the probability of a screen being damaged across any drop height.
Key words: Multivariate logistic regression, inverse prediction, odds ratio
Online Mortgage Application
Evaluate the potential improvement to the UI design of an online mortgage application process by examining the usability rating from a sample of 50 customers and comparing their performance using the new design vs. a large collection of historic data on customer’s performance with the current design.
Key words: Distribution, normality, normal quantile plot, Shapiro Wilk and Anderson Darling tests, t-Test
Performance of Food Manufacturing Process - Part 1
Performance of Food Manufacturing Process - Part 2
Evaluate the performance to specifications of a food manufacturing process using confidence intervals and hypothesis testing.
Key words: Distribution, normality, normal quantile plot, Shapiro Wilk and Anderson Darling tests, test of mean and test of standard deviation
Detergent Cleaning Effectiveness
Analyze the results of an experiment to determine if there is statistical evidence demonstrating an improvement in a new laundry detergent formulation. Explore and describe the affect that multiple factors have on a response, as well as identify conditions with the most and least impact.
Key words: Analysis of variance (ANOVA), t-Test, pairwise comparison, model diagnostics, model performance
Manufacturing Systems Variation
Study the use of Nested Variability chart to understand and analyze the different components of variances. Also explore the ways to minimize the variability by applying various rules of operation related to variance.
Key words: Variability gauge, nested design, component analysis of variance
US Stock Indices
Understand the basic concepts related to time series data analysis and explore the ways to practically understand the risks and rate of return related to the financial indices data.
Key words: Differencing, log transformation, stationarity, Augmented Dickey Fuller (ADF) test
Pricing Musical Instrument
Study the application of regression and concepts related to choice modeling (also called conjoint analysis) to understand and analyze the importance of the product attributes and their levels influencing the preferences.
Key words: Part Worth, regression, prediction profiler
Design and analyze discrete choice experiments (also called conjoint analysis) to discover which product or service attributes are preferred by potential customers.
Key words: Discrete choice design, regression, utility and probability profiler, willingness to pay
Modeling Gold Prices
Learn univariate time series modeling using US Gold Prices. Build AR, MA, ARMA and ARMA models to analyze the characteristics of the time series data and forecast.
Key words: Stationarity, AR, MA, ARMA, ARIMA, model comparison and diagnostics