Statistical Thinking for Industrial Problem Solving is an applied statistics course for scientists and engineers offered by JMP, a division of SAS. By completing this course, students will understand the importance of statistical thinking, and will be able to use data and basic statistical methods to solve many real-world problems. Students completing this course will be able to:
Statistical Thinking for Industrial Problem Solving, presented by JMP
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Statistical Thinking for Industrial Problem Solving, presented by JMP
SASAbout this Course
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Beginner Level
No prior knowledge of statistics or experience with JMP software is required.
Approx. 44 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
How to describe data with statistical summaries, and how to explore your data using advanced visualizations.
Understand statistical intervals, hypothesis tests and how to calculate sample size.
How to fit, evaluate and interpret linear and logistic regression models.
How to build predictive models and conduct a statistically designed experiment.
Skills you will gain
- Experimental Design
- Data Analysis
- Data Visualization (DataViz)
- Statistical Hypothesis Testing
- Statistics
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Beginner Level
No prior knowledge of statistics or experience with JMP software is required.
Approx. 44 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
SAS
Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.
Syllabus - What you will learn from this course
Week1
Week 1
Course Overview
In this module you learn about the course and about accessing JMP software in this course.
3 videos (Total 13 min), 4 readings, 1 quiz
3 videos
Course Overview2mWhy You Need a Foundation in Statistical Thinking4mFirst Time Using JMP? View the JMP Quickstart Video6m4 readings
Learner Prerequisites1mTaking this Course2mUsing Forums and Getting Help1mUsing the JMP Virtual Lab1mModule 1: Statistical Thinking and Problem Solving
Statistical thinking is about understanding, controlling and reducing process variation. Learn about process maps, problem-solving tools for defining and scoping your project, and understanding the data you need to solve your problem.
26 videos (Total 72 min), 3 readings, 17 quizzes
26 videos
Introduction36sWhat Is Statistical Thinking?4mOverview of Problem Solving2mStatistical Problem Solving1mTypes of Problems2mDefining the Problem3mGoals and Key Performance Indicators3mThe White Polymer Case Study2mWhat Is a Process?3mDeveloping a SIPOC Map1mDeveloping an Input/Output Process Map4mTop-Down and Deployment Flowcharts2mSummary2mTools for Identifying Potential Causes2mBrainstorming4mMulti-voting2mUsing Affinity Diagrams2mCause-and-Effect Diagrams4mThe 5 Whys1mCause-and-Effect Matrices1mSummary1mData Collection for Problem Solving2mTypes of Data2mOperational Definitions4mData Collection Strategies4mImporting Data for Analysis1m3 readings
Activity: Developing a Cause-and-Effect Diagram10mRead About It5mSummary: Statistical Thinking and Problem Solving 1m16 practice exercises
Question 1.011mQuestion 1.031mQuestion 1.041mQuestion 1.061mQuestion 1.071mQuestion 1.081mQuestion 1.091mQuestion 1.101mQuestion 1.121mQuestion 1.131mQuestion 1.151mQuestion 1.161mQuestions 1.18 - 1.192mQuestion 1.201mQuestion 1.211mQuestions 1.23-1.253mWeek2
Week 2
Module 2A: Exploratory Data Analysis, Part 1
Learn the basics of how to describe data with basic graphics and statistical summaries, and how to explore your data using more advanced visualizations. You’ll also learn some core concepts in probability, which form the foundation of many methods you learn throughout this course.
50 videos (Total 184 min)
50 videos
Introduction47sIntroduction to Descriptive Statistics1mTypes of Data5mHistograms4mDemo: Creating Histograms in JMP4mDemo: Saving Your Work Using Scripts1mThe Chemical Manufacturing Case Study38sThe White Polymer Case Study1mMeasures of Central Tendency and Location6mDemo: Summarizing Continuous Data with the Distribution Platform4mDemo: Summarizing Continuous Data with Column Viewer and Tabulate4mMeasures of Spread: Range and Interquartile Range5mDemo: Hiding and Excluding Data2mMeasures of Spread: Variance and Standard Deviation4mVisualizing Continuous Data7mDemo: Creating Tabular Summaries with Tabulate2mDemo: Creating Scatterplots and Scatterplot Matrices3mDemo: Creating Comparative Box Plots with Graph Builder2mDemo: Creating Run Charts (Line Graphs) with Graph Builder2mDescribing Categorical Data5mCreating Tabular Summaries for Categorical Data3mDemo: Creating Bar Charts and Mosaic Plots4mReview and Introduction to Probability Concepts2mSamples and Populations4mUnderstanding the Normal Distribution3mChecking for Normality6mDemo: Checking for Normality2mDemo: Finding the Area Under a Curve2mThe Central Limit Theorem4mDemo: Exploring the Central Limit Theorem3mIntroduction to Exploratory Data Analysis3mExploring Continuous Data: Enhanced Tools6mDemo: Adding Markers, Colors, and Row Legends3mDemo: Switching Columns in an Analysis2mPareto Plots6mDemo: Creating Sorted Bar Charts and Pareto Plots3mPacked Bar Charts and Data Filtering3mDemo: Creating Packed Bar Charts2mDemo: Using the Local Data Filter3mTree Maps and Mosaic Plots4mDemo: Creating a Tree Map2mUsing Trellis Plots and Overlay Variables5mDemo: Creating Trellis Plots and Using Overlay Variables3mBubble Plots and Heat Maps2mDemo: Creating Bubble Plots3mDemo: Creating Heat Maps3mVisualizing Geographic and Spatial Data6mDemo: Creating a Geographic Map Using Shape Files2mDemo: Creating Maps Using Coordinates3mSummary of Exploratory Data Analysis Tools2m31 practice exercises
Question 2.012mQuestion 2.022mPractice: Understanding Yield for a Chemical Manufacturing Process10mPractice: Exploring the Relationship Between Variables10mQuestion 2.03 - 2.041mPractice: Summarizing Continuous Data with the Distribution Platform10mQuestion 2.06 - 2.072mPractice: Understanding Box Plots10mQuestion 2.082mQuestion 2.092mPractice: Visualizing Continuous Data10mQuestion 2.10 - 2.112mPractice: Visualizing Categorical Data10mQuestion 2.131mQuestion 2.151mPractice: Checking for Normality10mPractice: Recognizing Shapes in Normal Quantile Plots10mPractice: Exploring the Central Limit Theorem10mQuestion 2.161mPractice: Exploring Many Variables Using the Column Switcher10mQuestion 2.17 - 2.182mPractice: Creating Sorted Bar Charts in JMP10mQuestion 2.191mPractice: Exploring Data with a Local Data Filter10mQuestion 2.201mPractice: Exploring Data with a Tree Map and Mosaic Plot10mPractice: Exploring Data Using Trellis Plots10mQuestion 2.211mPractice: Exploring Data Using Bubble Plots and Heat Maps10mQuestion 2.221mPractice: Exploring Data with a Geographic Map10mWeek3
Week 3
Module 2B: Exploratory Data Analysis, Part 2
Learn how to use interactive visualizations to effectively communicate the story in your data. You'll also learn how to save and share your results, and how to prepare your data for analysis.
36 videos (Total 114 min), 2 readings, 33 quizzes
36 videos
Introduction to Communicating with Data3mCreating Effective Visualizations1mEvaluating the Effectiveness of a Visualization4mDesigning an Effective Visualization: Part 13mDesigning an Effective Visualization: Part 25mCommunicating Visually with Animation2mDesigning for Your Audience3mUnderstanding Your Target Audience5mDesigning Visualizations for Communication52sDesigning Visualizations: The Do's5mDesigning Visualizations: The Don'ts2mDemo: Customizing Graphics3mIntroduction to Saving and Sharing Results2mSaving and Sharing Results in JMP3mSaving and Sharing Results outside of JMP3mDeciding Which Format to Use1mDemo: Organizing Your Saved Scripts2mDemo: Combining JMP Scripts for Analyses3mDemo: Sharing Static Output2mDemo: Saving Your Work in a JMP Journal4mData Tables Essentials2mCommon Data Quality Issues5mIdentifying Issues in the Data Table4mIdentifying Issues One Variable at a Time3mSummarizing What You Have Learned3mDemo: Exploring Missing Values3mDemo: Using Recode3mRestructuring Data for Analysis2mDemo: Stacking and Splitting Data2mCombining Data2mDemo: Concatenating Data Tables1mDemo: Joining Data Tables2mDeriving New Variables2mDemo: Binning Data Using Conditional IF-THEN Statements3mDemo: Transforming Data2mWorking with Dates1m2 readings
Read About It2mSummary - Exploratory Data Analysis1m31 practice exercises
Question 2.241mQuestion 2.251mQuestion 2.262mQuestion 2.28 - 2.292mPractice: Customizing Graphics10mPractice: Creating a Slope Graph10mQuestion 2.31 - 2.322mQuestion 2.331mPractice: Exploring Reports Published on JMP Public10mPractice: Grouping and Combining Analysis Scripts10mPractice: Creating a Simple Dashboard10mPractice: Using a JMP Journal to Document Your Work10mQuestion 2.342mQuestion 2.352mPractice: Creating the Formula for Scrap Rate10mPractice: Checking the Data Table for Issues10mQuestion 2.361mPractice: Checking Data Quality with Summary Statistics and Graphs10mQuestion 2.37 - 2.382mQuestion 2.391mPractice: Exploring Missing Data15mPractice: Recoding Missing Values10mPractice: Using Recode to Bin Data10mQuestion 2.401mPractice: Stacking Data10mQuestion 2.411mPractice: Concatenating Data Tables10mPractice: Joining Data Tables10mPractice: Creating a Binning Formula10mPractice: Extracting Information from a Column10mPractice: Working with Dates10mWeek4
Week 4
Module 3: Quality Methods
Learn about tools for quantifying, controlling and reducing variation in your product, service or process. Topics include control charts, process capability and measurement systems analysis.
41 videos (Total 154 min), 3 readings, 28 quizzes
41 videos
Introduction41sQuality Methods Overview4mIntroduction to Control Charts6mIndividual and Moving Range Charts4mDemo: Creating an I and MR Chart Using the Control Chart Builder2mCommon Cause versus Special Cause Variation6mTesting for Special Causes6mDemo: Testing for Special Causes in the Control Chart Builder2mX-bar and R and X-bar and S Charts3mDemo: Creating X-bar and R and X-bar and S Charts2mRational Subgrouping4m3-Way Control Charts2mDemo: Creating 3-Way Control Charts2mControl Charts with Phases3mDemo: Adding Phases to Control Charts1mThe Voice of the Customer2mProcess Capability Indices5mShort- and Long-Term Estimates of Capability2mUnderstanding Capability for Process Improvement4mEstimating Process Capability: An Example4mDemo: Calculating Capability Indices Using the Distribution Platform5mDemo: Conducting a Capability Analysis Using the Control Chart Builder2mCalculating Capability for Nonnormal Data3mDemo: Estimating Capability for Nonnormal Data3mEstimating Process Capability for Many Variables2mIdentifying Poorly Performing Processes4mDemo: Identifying Poorly Performing Processes5mA View from Industry6mWhat is a Measurement Systems Analysis2mLanguage and Terminology4mDesigning a Measurement System Study2mDesigning and Conducting an MSA4mDemo: Creating a Gauge Study Worksheet1mAnalyzing an MSA with Visualizations6mDemo: Visualizing Measurement System Variation4mAnalyzing the MSA4mDemo: Analyzing an MSA2mDemo: Conducting a Gauge R&R Analysis4mStudying Measurement System Accuracy3mDemo: Analyzing Measurement System Bias2mImproving the Measurement Process2m3 readings
Activity: Area MSA5mRead About It1mSummary: Quality Methods1m26 practice exercises
Question 3.021mPractice: Creating an I and MR Chart10mQuestion 3.032mQuestion 3.041mPractice: Creating I and MR Charts for the White Polymer Case Study10mPractice: Constructing an X-Bar and S Chart10mQuestion 3.051mQuestion 3.061mPractice: Evaluating whether Improvements Have Been Sustained10mPractice: Using Control Charts as an Exploratory Tool10mQuestion 3.071mQuestion 3.082mActivity: Calculating Capability Indices2mQuestion 3.091mQuestion 3.10 - 3.112mPractice: Calculating Capability Indices10mPractice: Conducting a Capability Analysis with a Phase Variable10mPractice: Conducting a Capability Analysis with Nonnormal Data10mQuestion 3.122mQuestion 3.131mPractice: Designing a Gauge Study10mPractice: Visualizing the Area Measurement MSA Data10mPractice: Visualizing the MFI MSA Data10mPractice: Analyze the Area Measurement MSA Data10mPractice: Analyzing the Melt Flow Index MSA10mQuestion 3.151mWeek5
Week 5
Module 4: Decision Making with Data
Learn about tools used for drawing inferences from data. In this module you learn about statistical intervals and hypothesis tests. You also learn how to calculate sample size and see the relationship between sample size and power.
47 videos (Total 156 min), 2 readings, 40 quizzes
47 videos
Introduction to Decision Making with Data1mIntroduction to Statistical Inference3mWhat Is a Confidence Interval?2mA Practical Example1mEstimating a Mean4mVisualizing Sampling Variation3mConstructing Confidence Intervals4mDemo: Understanding the Confidence Level and Alpha Risk2mDemo: Calculating Confidence Intervals1mPrediction Intervals3mTolerance Intervals4mDemo: Calculating Prediction and Tolerance Intervals2mComparing Interval Estimates1mIntroduction to Statistical Testing1mStatistical Decision Making5mUnderstanding the Null and Alternative Hypothesis3mSampling Distribution under the Null3mThe p-Value and Statistical Significance4mSummary of Foundations in Statistical Testing1mConducting a One-Sample t Test6mDemo: Conducting a One-Sample t Test3mDemo: Understanding p-Values and t Ratios3mEquivalence Testing3mComparing Two Means3mTwo-Sample t Tests4mUnequal Variances Tests1mDemo: Conducting a Two-Sample t Test3mPaired Observations5mDemo: Performing a Paired t Test1mComparing More Than Two Means2mOne-Way ANOVA (Analysis of Variance)5mMultiple Comparisons3mDemo: Comparing More Than Two Means4mRevisiting Statistical Versus Practical Significance2mSummary of Hypothesis Testing for Continuous Data1mIntroduction to Sample Size and Power2mSample Size for a Confidence Interval for the Mean4mDemo: Calculating the Sample Size for a Confidence Interval3mOutcomes of Statistical Tests5mStatistical Power2mExploring Sample Size and Power4mDemo: Exploring the Power Animation2mCalculating the Sample Size for One-Sample t Tests2mDemo: Calculating the Sample Size for a One-Sample t Test2mCalculating the Sample Size for Two-Sample t Tests2mDemo: Calculating the Sample Size for Two or More Sample Means3mSummary of Sample Size and Power1m2 readings
Read About It1mSummary: Decision Making with Data1m38 practice exercises
Question 4.011mQuestion 4.021mQuestion 4.031mQuestions 4.04 - 4.062mPractice: Constructing a Confidence Interval10mPractice: Comparing Intervals at Different Confidence Levels10mPractice: Constructing a Confidence Interval for the Speed of Light10mQuestion 4.071mQuestion 4.081mPractice: Constructing Prediction and Tolerance Intervals10mQuestion 4.092mPractice: Comparing Interval Estimates10mQuestion 4.111mQuestions 4.12 - 4.143mQuestion 4.151mQuestions 4.16 - 4.183mQuestion 4.201mPractice: Conducting a One-Sample t Test10mPractice: Conducting a One-Sample t Test with a BY Variable10mPractice: Conducting an Equivalence Test10mQuestion 4.211mPractice: Conducting a Two-Sample t Test10mPractice: Conducting an Equivalence Test for Two Means10mPractice: Conducting an Unequal Variances Test10mQuestion 4.221mPractice: Conducting a Paired t Test10mQuestion 4.231mPractice: Conducting a One-Way ANOVA Analysis10mPractice: Comparing Several Means10mQuestion 4.251mQuestion 4.261mPractice: Calculating Sample Size for a CI for a Mean10mPractice: Calculating Sample Size for a CI for a Proportion10mQuestion 4.27 - 4.282mQuestion 4.301mQuestion 4.311mPractice: Calculating Sample Size for a One-Sample t Test10mPractice: Calculating Sample Size for a Two-Sample t Test10mWeek6
Week 6
Module 5: Correlation and Regression
Learn how to use scatterplots and correlation to study the linear association between pairs of variables. Then, learn how to fit, evaluate and interpret linear and logistic regression models.
43 videos (Total 150 min), 2 readings, 32 quizzes
43 videos
Introduction34sWhat Is Correlation?2mInterpreting Correlation2mDemo: Exploring the Impact of Outliers on Correlation1mDemo: Assessing Correlations3mIntroduction to Regression Analysis6mDemo: Fitting a Regression Model2mThe Simple Linear Regression Model4mThe Method of Least Squares1mDemo: The Method of Least Squares1mVisualizing the Method of Least Squares1mRegression Model Assumptions5mDemo: Evaluating Model Assumptions1mInterpreting Regression Results6mDemo: Interpreting Regression Analysis Results3mFitting a Model with Curvature3mDemo: Fitting Polynomial Models2mWhat is Multiple Linear Regression?3mFitting the Multiple Linear Regression Model5mDemo: Fitting Multiple Linear Regression Models2mInterpreting Results in Explanatory Modeling6mDemo: Using the Prediction Profiler2mResidual Analysis and Outliers6mDemo: Analyzing Residuals and Outliers2mMultiple Linear Regression with Categorical Predictors5mDemo: Fitting a Model with Categorical Predictors1mMultiple Linear Regression with Interactions5mDemo: Fitting a Model with Interactions2mVariable Selection6mDemo: Selecting Variables Using Effect Summary2mMulticollinearity4mDemo: Assessing Multicollinearity2mClosing Thoughts on Multiple Linear Regression2mWhat Is Logistic Regression?2mThe Simple Logistic Model4mSimple Logistic Regression Example3mInterpreting Logistic Regression Results3mDemo: Fitting a Simple Logistic Regression Model4mMultiple Logistic Regression4mDemo: Fitting a Multiple Logistic Regression Model2mLogistic Regression with Interactions3mDemo: Fitting a Logistic Regression Model with Interactions2mCommon Issues3m2 readings
Read About It1mSummary: Correlation and Regression1m30 practice exercises
Question 5.012mQuestion 5.02-5.032mPractice: Exploring Correlations (Example)10mPractice: Exploring Correlations (Case Study)10mQuestion 5.051mPractice: Fitting a Simple Linear Regression Model10mQuestion 5.061mPractice: Exploring Least Squares10mPractice: Visualizing Regression with Anscombe's Quartet10mPractice: Interpreting Regression Analysis Results10mPractice: Fitting Polynomial Models10mQuestion 5.081mPractice: Comparing Simple Linear and Multiple Linear Regression Models10mQuestion 5.0910mPractice: Exploring Significant Predictors10mQuestion 5.101mPractice: Identifying Outliers and Influential Observations10mQuestion 5.111mPractice: Fitting a Model with Categorical Predictors10mQuestion 5.121mPractice: Fitting a Model with Interactions10mPractice: Selecting Variables Using Effect Summary10mQuestion 5.141mQuestion 5.151mPractice: Regression Modeling Mini Case Study10mQuestion 5.161mQuestion 5.172mPractice: Fitting a Simple Logistic Model for Reaction Time10mPractice: Fitting a Multiple Logistic Regression Model10mPractice: Fitting a Logistic Regression Model with Interactions10mWeek7
Week 7
Module 6: Design of Experiments (DOE)
In this introduction to statistically designed experiments (DOE), you learn the language of DOE, and see how to design, conduct and analyze an experiment in JMP.
36 videos (Total 148 min), 2 readings, 27 quizzes
36 videos
Introduction1mA View from Industry4mWhat is DOE?5mConducting Ad Hoc and One-Factor-at-a-Time (OFAT) Experiments5mWhy Use DOE?4mTerminology of DOE3mTypes of Experimental Designs7mDesigning Factorial Experiments6mDemo: Designing Full Factorial Experiments5mAnalyzing a Replicated Full Factorial5mAnalyzing an Unreplicated Full Factorial3mDemo: Analyzing Full Factorial Experiments5mSummary of Factorial Experiments1mScreening for Important Effects1mA Look at Fractional Factorial Designs4mDemo: Creating 2^k-r Fractional Factorial Designs4mCustom Screening Designs4mDemo: Creating Screening Designs in the Custom Designer3mIntroduction to Response Surface Designs2mResponse Surface Designs for Two Factors5mAnalyzing Response Surface Experiments4mDemo: Designing a Central Composite Design4mCreating Custom Response Surface Designs2mSequential Experimentation4mResponse Surface Summary1mIntroduction to DOE Guidelines4mDefining the Problem and the Objectives3mIdentifying the Responses1mIdentifying the Factors and Factor Levels4mIdentifying Restrictions and Constraints4mPreparing to Conduct the Experiment2mThe Anodize Case Study: Part 16mThe Anodize Case Study: Part 23mSummary1mDemo: Optimizing Multiple Responses4mDemo: Simulating Data Using the Prediction Profiler5m2 readings
Read About It1mSummary: Design of Experiments (DOE)1m25 practice exercises
Question 6.01 - 6.021mQuestion 6.031mQuestion 6.042mQuestion 6.052mQuestion 6.06 - 6.072mQuestion 6.082mQuestion 6.09 - 6.122mPractice: Designing a Full Factorial Experiment10mQuestion 6.13 - 6.142mQuestion 6.151mQuestion 6.161mPractice: Analyzing a Replicated Full Factorial Experiment10mQuestion 6.171mQuestion 6.18 - 6.192mPractice: Designing a Fractional Factorial Experiment10mPractice: Analyzing a 20-Run Custom Design10mQuestion 6.21- 6.22Question 6.23 - 6.242mPractice: Analyzing a Custom Central Composite Design10mPractice: Optimizing the Heck Reaction10mQuestion 6.261mQuestion 6.27 - 6.282mQuestion 6.291mQuestion 6.301mPractice: Optimizing Multiple Responses10mWeek8
Week 8
Module 7: Predictive Modeling and Text Mining
Learn how to identify possible relationships, build predictive models and derive value from free-form text.
39 videos (Total 143 min), 2 readings, 32 quizzes
39 videos
Introduction54sIntroduction to Predictive Modeling5mOverfitting and Model Validation8mDemo: Creating a Validation Column3mAssessing Model Performance: Prediction Models5mDemo: Fitting a Multiple Linear Regression Model with Validation2mAssessing Model Performance: Classification Models3mReceiver-Operating Characteristic (ROC) Curves4mDemo: Fitting a Logistic Model with Validation1mDemo: Changing the Cutoff for Classification3mIntroduction to Decision Trees1mClassification Trees4mDemo: Creating a Classification Tree4mRegression Trees5mDemo: Fitting a Regression Tree3mDecision Trees with Validation4mDemo: Fitting a Decision Tree with Validation3mRandom (Bootstrap) Forests5mDemo: Variable Selection with a Bootstrap Forest2mWhat is a Neural Network?1mInterpreting Neural Networks3mDemo: Fitting a Neural Network3mPredictive Modeling with Neural Networks3mDemo: Fitting a Neural Model with Two Layers3mIntroduction to Generalized Regression2mFitting Models Using Maximum Likelihood3mDemo: Fitting a Linear Model in Generalized Regression3mDemo: Variable Selection in Generalized Regression4mIntroduction to Penalized Regression3mDemo: Fitting a Penalized Regression (Lasso) Model4mComparing Predictive Models4mDemo: Comparing and Selecting Predictive Models4mIntroduction to Text Mining1mProcessing Text Data4mCurating the Term List2mDemo: Processing Unstructured Text Data4mVisualizing and Exploring Text Data3mDemo: Visualizing and Exploring Text Data4mAnalyzing (Mining) Text Data2m2 readings
Read About It1mSummary: Predictive Modeling and Text Mining1m30 practice exercises
Question 7.011mQuestion 7.022mQuestion 7.031mPractice: Fitting a Multiple Linear Regression Model with Validation10mPractice: Fitting a Logistic Model with Validation10mQuestion 7.041mPractice: Using a Classification Tree for Problem Solving10mPractice: Identifying Important Variables10mQuestion 7.051mQuestion 7.061mPractice: Using a Regression Tree with Validation10mPractice: Using a Classification Tree with Validation10mQuestion 7.071mPractice: Using Trees to Identify Important Variables10mQuestion 7.081mPractice: Fitting a Simple Neural Network10mPractice: Fitting a Neural Network for Prediction10mPractice: Fitting a Neural Network for Classification10mQuestion 7.091mQuestion 7.101mQuestion 7.11 - 7.122mPractice: Reducing a Model Using Generalized Regression10mPractice: Fitting a Regression Model using the Lasso10mQuestion 7.131mPractice: Comparing and Selecting Predictive Models10mQuestion 7.141mQuestion 7.152mQuestion 7.161mPractice: Developing a Term List10mPractice: Exploring Terms and Phrases in STIPS10mWeek9
Week 9
Review Questions and Case Studies
In this module you have an opportunity to test your understanding of what you have learned.
2 practice exercises
Review Questions30mCase Studies30mReviews
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TOP REVIEWS FROM STATISTICAL THINKING FOR INDUSTRIAL PROBLEM SOLVING, PRESENTED BY JMP
by FFSep 10, 2020
This course it's incredibly well structured, I relly enjoyed learning with it!
by RSSep 2, 2020
It is a really exhilarating course testing your practical and theoretical understanding about the subject covering most fields in the topic of data analytics along with jmp, a user-friendly platform.
by AKFeb 13, 2022
very interesting course and i get a lot of things
than you very much
by RJDec 23, 2021
Really good course, learnt a tremendous amount.
Thanks
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