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
5.615 bereits angemeldet
von
Statistical Thinking for Industrial Problem Solving, presented by JMP
SASÜber diesen Kurs
33.784 kürzliche Aufrufe
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Anfänger“
No prior knowledge of statistics or experience with JMP software is required.
Ca. 44 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (europäisch), Russisch, Englisch, Spanisch
Könnte Ihr Unternehmen von Mitarbeiterweiterbildungen für gefragte Kompetenzen profitieren?
Probieren Sie Coursera for Business ausWas Sie lernen werden
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.
Kompetenzen, die Sie erwerben
- Experimental Design
- Data Analysis
- Data Visualization (DataViz)
- Statistical Hypothesis Testing
- Statistics
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Anfänger“
No prior knowledge of statistics or experience with JMP software is required.
Ca. 44 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (europäisch), Russisch, Englisch, Spanisch
Könnte Ihr Unternehmen von Mitarbeiterweiterbildungen für gefragte Kompetenzen profitieren?
Probieren Sie Coursera for Business aus
von
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
1Woche 1
Course Overview
In this module you learn about the course and about accessing JMP software in this course.
3 Videos (Gesamt 13 min), 4 Lektüren, 1 Quiz
3 Videos
Why You Need a Foundation in Statistical Thinking4m
First Time Using JMP? View the JMP Quickstart Video6m
4 Lektüren
Learner Prerequisites1m
Taking this Course2m
Using Forums and Getting Help1m
Using the JMP Virtual Lab1m
Module 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 (Gesamt 72 min), 3 Lektüren, 17 Quiz
26 Videos
What Is Statistical Thinking?4m
Overview of Problem Solving2m
Statistical Problem Solving1m
Types of Problems2m
Defining the Problem3m
Goals and Key Performance Indicators3m
The White Polymer Case Study2m
What Is a Process?3m
Developing a SIPOC Map1m
Developing an Input/Output Process Map4m
Top-Down and Deployment Flowcharts2m
Summary2m
Tools for Identifying Potential Causes2m
Brainstorming4m
Multi-voting2m
Using Affinity Diagrams2m
Cause-and-Effect Diagrams4m
The 5 Whys1m
Cause-and-Effect Matrices1m
Summary1m
Data Collection for Problem Solving2m
Types of Data2m
Operational Definitions4m
Data Collection Strategies4m
Importing Data for Analysis1m
3 Lektüren
Activity: Developing a Cause-and-Effect Diagram10m
Read About It5m
Summary: Statistical Thinking and Problem Solving 1m
16 praktische Übungen
Question 1.011m
Question 1.031m
Question 1.041m
Question 1.061m
Question 1.071m
Question 1.081m
Question 1.091m
Question 1.101m
Question 1.121m
Question 1.131m
Question 1.151m
Question 1.161m
Questions 1.18 - 1.192m
Question 1.201m
Question 1.211m
Questions 1.23-1.253m
Woche
2Woche 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 (Gesamt 184 min)
50 Videos
Introduction to Descriptive Statistics1m
Types of Data5m
Histograms4m
Demo: Creating Histograms in JMP4m
Demo: Saving Your Work Using Scripts1m
The Chemical Manufacturing Case Study38
The White Polymer Case Study1m
Measures of Central Tendency and Location6m
Demo: Summarizing Continuous Data with the Distribution Platform4m
Demo: Summarizing Continuous Data with Column Viewer and Tabulate4m
Measures of Spread: Range and Interquartile Range5m
Demo: Hiding and Excluding Data2m
Measures of Spread: Variance and Standard Deviation4m
Visualizing Continuous Data7m
Demo: Creating Tabular Summaries with Tabulate2m
Demo: Creating Scatterplots and Scatterplot Matrices3m
Demo: Creating Comparative Box Plots with Graph Builder2m
Demo: Creating Run Charts (Line Graphs) with Graph Builder2m
Describing Categorical Data5m
Creating Tabular Summaries for Categorical Data3m
Demo: Creating Bar Charts and Mosaic Plots4m
Review and Introduction to Probability Concepts2m
Samples and Populations4m
Understanding the Normal Distribution3m
Checking for Normality6m
Demo: Checking for Normality2m
Demo: Finding the Area Under a Curve2m
The Central Limit Theorem4m
Demo: Exploring the Central Limit Theorem3m
Introduction to Exploratory Data Analysis3m
Exploring Continuous Data: Enhanced Tools6m
Demo: Adding Markers, Colors, and Row Legends3m
Demo: Switching Columns in an Analysis2m
Pareto Plots6m
Demo: Creating Sorted Bar Charts and Pareto Plots3m
Packed Bar Charts and Data Filtering3m
Demo: Creating Packed Bar Charts2m
Demo: Using the Local Data Filter3m
Tree Maps and Mosaic Plots4m
Demo: Creating a Tree Map2m
Using Trellis Plots and Overlay Variables5m
Demo: Creating Trellis Plots and Using Overlay Variables3m
Bubble Plots and Heat Maps2m
Demo: Creating Bubble Plots3m
Demo: Creating Heat Maps3m
Visualizing Geographic and Spatial Data6m
Demo: Creating a Geographic Map Using Shape Files2m
Demo: Creating Maps Using Coordinates3m
Summary of Exploratory Data Analysis Tools2m
31 praktische Übungen
Question 2.012m
Question 2.022m
Practice: Understanding Yield for a Chemical Manufacturing Process10m
Practice: Exploring the Relationship Between Variables10m
Question 2.03 - 2.041m
Practice: Summarizing Continuous Data with the Distribution Platform10m
Question 2.06 - 2.072m
Practice: Understanding Box Plots10m
Question 2.082m
Question 2.092m
Practice: Visualizing Continuous Data10m
Question 2.10 - 2.112m
Practice: Visualizing Categorical Data10m
Question 2.131m
Question 2.151m
Practice: Checking for Normality10m
Practice: Recognizing Shapes in Normal Quantile Plots10m
Practice: Exploring the Central Limit Theorem10m
Question 2.161m
Practice: Exploring Many Variables Using the Column Switcher10m
Question 2.17 - 2.182m
Practice: Creating Sorted Bar Charts in JMP10m
Question 2.191m
Practice: Exploring Data with a Local Data Filter10m
Question 2.201m
Practice: Exploring Data with a Tree Map and Mosaic Plot10m
Practice: Exploring Data Using Trellis Plots10m
Question 2.211m
Practice: Exploring Data Using Bubble Plots and Heat Maps10m
Question 2.221m
Practice: Exploring Data with a Geographic Map10m
Woche
3Woche 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 (Gesamt 114 min), 2 Lektüren, 33 Quiz
36 Videos
Creating Effective Visualizations1m
Evaluating the Effectiveness of a Visualization4m
Designing an Effective Visualization: Part 13m
Designing an Effective Visualization: Part 25m
Communicating Visually with Animation2m
Designing for Your Audience3m
Understanding Your Target Audience5m
Designing Visualizations for Communication52
Designing Visualizations: The Do's5m
Designing Visualizations: The Don'ts2m
Demo: Customizing Graphics3m
Introduction to Saving and Sharing Results2m
Saving and Sharing Results in JMP3m
Saving and Sharing Results outside of JMP3m
Deciding Which Format to Use1m
Demo: Organizing Your Saved Scripts2m
Demo: Combining JMP Scripts for Analyses3m
Demo: Sharing Static Output2m
Demo: Saving Your Work in a JMP Journal4m
Data Tables Essentials2m
Common Data Quality Issues5m
Identifying Issues in the Data Table4m
Identifying Issues One Variable at a Time3m
Summarizing What You Have Learned3m
Demo: Exploring Missing Values3m
Demo: Using Recode3m
Restructuring Data for Analysis2m
Demo: Stacking and Splitting Data2m
Combining Data2m
Demo: Concatenating Data Tables1m
Demo: Joining Data Tables2m
Deriving New Variables2m
Demo: Binning Data Using Conditional IF-THEN Statements3m
Demo: Transforming Data2m
Working with Dates1m
2 Lektüren
Read About It2m
Summary - Exploratory Data Analysis1m
31 praktische Übungen
Question 2.241m
Question 2.251m
Question 2.262m
Question 2.28 - 2.292m
Practice: Customizing Graphics10m
Practice: Creating a Slope Graph10m
Question 2.31 - 2.322m
Question 2.331m
Practice: Exploring Reports Published on JMP Public10m
Practice: Grouping and Combining Analysis Scripts10m
Practice: Creating a Simple Dashboard10m
Practice: Using a JMP Journal to Document Your Work10m
Question 2.342m
Question 2.352m
Practice: Creating the Formula for Scrap Rate10m
Practice: Checking the Data Table for Issues10m
Question 2.361m
Practice: Checking Data Quality with Summary Statistics and Graphs10m
Question 2.37 - 2.382m
Question 2.391m
Practice: Exploring Missing Data15m
Practice: Recoding Missing Values10m
Practice: Using Recode to Bin Data10m
Question 2.401m
Practice: Stacking Data10m
Question 2.411m
Practice: Concatenating Data Tables10m
Practice: Joining Data Tables10m
Practice: Creating a Binning Formula10m
Practice: Extracting Information from a Column10m
Practice: Working with Dates10m
Woche
4Woche 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 (Gesamt 154 min), 3 Lektüren, 28 Quiz
41 Videos
Quality Methods Overview4m
Introduction to Control Charts6m
Individual and Moving Range Charts4m
Demo: Creating an I and MR Chart Using the Control Chart Builder2m
Common Cause versus Special Cause Variation6m
Testing for Special Causes6m
Demo: Testing for Special Causes in the Control Chart Builder2m
X-bar and R and X-bar and S Charts3m
Demo: Creating X-bar and R and X-bar and S Charts2m
Rational Subgrouping4m
3-Way Control Charts2m
Demo: Creating 3-Way Control Charts2m
Control Charts with Phases3m
Demo: Adding Phases to Control Charts1m
The Voice of the Customer2m
Process Capability Indices5m
Short- and Long-Term Estimates of Capability2m
Understanding Capability for Process Improvement4m
Estimating Process Capability: An Example4m
Demo: Calculating Capability Indices Using the Distribution Platform5m
Demo: Conducting a Capability Analysis Using the Control Chart Builder2m
Calculating Capability for Nonnormal Data3m
Demo: Estimating Capability for Nonnormal Data3m
Estimating Process Capability for Many Variables2m
Identifying Poorly Performing Processes4m
Demo: Identifying Poorly Performing Processes5m
A View from Industry6m
What is a Measurement Systems Analysis2m
Language and Terminology4m
Designing a Measurement System Study2m
Designing and Conducting an MSA4m
Demo: Creating a Gauge Study Worksheet1m
Analyzing an MSA with Visualizations6m
Demo: Visualizing Measurement System Variation4m
Analyzing the MSA4m
Demo: Analyzing an MSA2m
Demo: Conducting a Gauge R&R Analysis4m
Studying Measurement System Accuracy3m
Demo: Analyzing Measurement System Bias2m
Improving the Measurement Process2m
3 Lektüren
Activity: Area MSA5m
Read About It1m
Summary: Quality Methods1m
26 praktische Übungen
Question 3.021m
Practice: Creating an I and MR Chart10m
Question 3.032m
Question 3.041m
Practice: Creating I and MR Charts for the White Polymer Case Study10m
Practice: Constructing an X-Bar and S Chart10m
Question 3.051m
Question 3.061m
Practice: Evaluating whether Improvements Have Been Sustained10m
Practice: Using Control Charts as an Exploratory Tool10m
Question 3.071m
Question 3.082m
Activity: Calculating Capability Indices2m
Question 3.091m
Question 3.10 - 3.112m
Practice: Calculating Capability Indices10m
Practice: Conducting a Capability Analysis with a Phase Variable10m
Practice: Conducting a Capability Analysis with Nonnormal Data10m
Question 3.122m
Question 3.131m
Practice: Designing a Gauge Study10m
Practice: Visualizing the Area Measurement MSA Data10m
Practice: Visualizing the MFI MSA Data10m
Practice: Analyze the Area Measurement MSA Data10m
Practice: Analyzing the Melt Flow Index MSA10m
Question 3.151m
Woche
5Woche 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 (Gesamt 156 min), 2 Lektüren, 40 Quiz
47 Videos
Introduction to Statistical Inference3m
What Is a Confidence Interval?2m
A Practical Example1m
Estimating a Mean4m
Visualizing Sampling Variation3m
Constructing Confidence Intervals4m
Demo: Understanding the Confidence Level and Alpha Risk2m
Demo: Calculating Confidence Intervals1m
Prediction Intervals3m
Tolerance Intervals4m
Demo: Calculating Prediction and Tolerance Intervals2m
Comparing Interval Estimates1m
Introduction to Statistical Testing1m
Statistical Decision Making5m
Understanding the Null and Alternative Hypothesis3m
Sampling Distribution under the Null3m
The p-Value and Statistical Significance4m
Summary of Foundations in Statistical Testing1m
Conducting a One-Sample t Test6m
Demo: Conducting a One-Sample t Test3m
Demo: Understanding p-Values and t Ratios3m
Equivalence Testing3m
Comparing Two Means3m
Two-Sample t Tests4m
Unequal Variances Tests1m
Demo: Conducting a Two-Sample t Test3m
Paired Observations5m
Demo: Performing a Paired t Test1m
Comparing More Than Two Means2m
One-Way ANOVA (Analysis of Variance)5m
Multiple Comparisons3m
Demo: Comparing More Than Two Means4m
Revisiting Statistical Versus Practical Significance2m
Summary of Hypothesis Testing for Continuous Data1m
Introduction to Sample Size and Power2m
Sample Size for a Confidence Interval for the Mean4m
Demo: Calculating the Sample Size for a Confidence Interval3m
Outcomes of Statistical Tests5m
Statistical Power2m
Exploring Sample Size and Power4m
Demo: Exploring the Power Animation2m
Calculating the Sample Size for One-Sample t Tests2m
Demo: Calculating the Sample Size for a One-Sample t Test2m
Calculating the Sample Size for Two-Sample t Tests2m
Demo: Calculating the Sample Size for Two or More Sample Means3m
Summary of Sample Size and Power1m
2 Lektüren
Read About It1m
Summary: Decision Making with Data1m
38 praktische Übungen
Question 4.011m
Question 4.021m
Question 4.031m
Questions 4.04 - 4.062m
Practice: Constructing a Confidence Interval10m
Practice: Comparing Intervals at Different Confidence Levels10m
Practice: Constructing a Confidence Interval for the Speed of Light10m
Question 4.071m
Question 4.081m
Practice: Constructing Prediction and Tolerance Intervals10m
Question 4.092m
Practice: Comparing Interval Estimates10m
Question 4.111m
Questions 4.12 - 4.143m
Question 4.151m
Questions 4.16 - 4.183m
Question 4.201m
Practice: Conducting a One-Sample t Test10m
Practice: Conducting a One-Sample t Test with a BY Variable10m
Practice: Conducting an Equivalence Test10m
Question 4.211m
Practice: Conducting a Two-Sample t Test10m
Practice: Conducting an Equivalence Test for Two Means10m
Practice: Conducting an Unequal Variances Test10m
Question 4.221m
Practice: Conducting a Paired t Test10m
Question 4.231m
Practice: Conducting a One-Way ANOVA Analysis10m
Practice: Comparing Several Means10m
Question 4.251m
Question 4.261m
Practice: Calculating Sample Size for a CI for a Mean10m
Practice: Calculating Sample Size for a CI for a Proportion10m
Question 4.27 - 4.282m
Question 4.301m
Question 4.311m
Practice: Calculating Sample Size for a One-Sample t Test10m
Practice: Calculating Sample Size for a Two-Sample t Test10m
Woche
6Woche 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 (Gesamt 150 min), 2 Lektüren, 32 Quiz
43 Videos
What Is Correlation?2m
Interpreting Correlation2m
Demo: Exploring the Impact of Outliers on Correlation1m
Demo: Assessing Correlations3m
Introduction to Regression Analysis6m
Demo: Fitting a Regression Model2m
The Simple Linear Regression Model4m
The Method of Least Squares1m
Demo: The Method of Least Squares1m
Visualizing the Method of Least Squares1m
Regression Model Assumptions5m
Demo: Evaluating Model Assumptions1m
Interpreting Regression Results6m
Demo: Interpreting Regression Analysis Results3m
Fitting a Model with Curvature3m
Demo: Fitting Polynomial Models2m
What is Multiple Linear Regression?3m
Fitting the Multiple Linear Regression Model5m
Demo: Fitting Multiple Linear Regression Models2m
Interpreting Results in Explanatory Modeling6m
Demo: Using the Prediction Profiler2m
Residual Analysis and Outliers6m
Demo: Analyzing Residuals and Outliers2m
Multiple Linear Regression with Categorical Predictors5m
Demo: Fitting a Model with Categorical Predictors1m
Multiple Linear Regression with Interactions5m
Demo: Fitting a Model with Interactions2m
Variable Selection6m
Demo: Selecting Variables Using Effect Summary2m
Multicollinearity4m
Demo: Assessing Multicollinearity2m
Closing Thoughts on Multiple Linear Regression2m
What Is Logistic Regression?2m
The Simple Logistic Model4m
Simple Logistic Regression Example3m
Interpreting Logistic Regression Results3m
Demo: Fitting a Simple Logistic Regression Model4m
Multiple Logistic Regression4m
Demo: Fitting a Multiple Logistic Regression Model2m
Logistic Regression with Interactions3m
Demo: Fitting a Logistic Regression Model with Interactions2m
Common Issues3m
2 Lektüren
Read About It1m
Summary: Correlation and Regression1m
30 praktische Übungen
Question 5.012m
Question 5.02-5.032m
Practice: Exploring Correlations (Example)10m
Practice: Exploring Correlations (Case Study)10m
Question 5.051m
Practice: Fitting a Simple Linear Regression Model10m
Question 5.061m
Practice: Exploring Least Squares10m
Practice: Visualizing Regression with Anscombe's Quartet10m
Practice: Interpreting Regression Analysis Results10m
Practice: Fitting Polynomial Models10m
Question 5.081m
Practice: Comparing Simple Linear and Multiple Linear Regression Models10m
Question 5.0910m
Practice: Exploring Significant Predictors10m
Question 5.101m
Practice: Identifying Outliers and Influential Observations10m
Question 5.111m
Practice: Fitting a Model with Categorical Predictors10m
Question 5.121m
Practice: Fitting a Model with Interactions10m
Practice: Selecting Variables Using Effect Summary10m
Question 5.141m
Question 5.151m
Practice: Regression Modeling Mini Case Study10m
Question 5.161m
Question 5.172m
Practice: Fitting a Simple Logistic Model for Reaction Time10m
Practice: Fitting a Multiple Logistic Regression Model10m
Practice: Fitting a Logistic Regression Model with Interactions10m
Woche
7Woche 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 (Gesamt 148 min), 2 Lektüren, 27 Quiz
36 Videos
A View from Industry4m
What is DOE?5m
Conducting Ad Hoc and One-Factor-at-a-Time (OFAT) Experiments5m
Why Use DOE?4m
Terminology of DOE3m
Types of Experimental Designs7m
Designing Factorial Experiments6m
Demo: Designing Full Factorial Experiments5m
Analyzing a Replicated Full Factorial5m
Analyzing an Unreplicated Full Factorial3m
Demo: Analyzing Full Factorial Experiments5m
Summary of Factorial Experiments1m
Screening for Important Effects1m
A Look at Fractional Factorial Designs4m
Demo: Creating 2^k-r Fractional Factorial Designs4m
Custom Screening Designs4m
Demo: Creating Screening Designs in the Custom Designer3m
Introduction to Response Surface Designs2m
Response Surface Designs for Two Factors5m
Analyzing Response Surface Experiments4m
Demo: Designing a Central Composite Design4m
Creating Custom Response Surface Designs2m
Sequential Experimentation4m
Response Surface Summary1m
Introduction to DOE Guidelines4m
Defining the Problem and the Objectives3m
Identifying the Responses1m
Identifying the Factors and Factor Levels4m
Identifying Restrictions and Constraints4m
Preparing to Conduct the Experiment2m
The Anodize Case Study: Part 16m
The Anodize Case Study: Part 23m
Summary1m
Demo: Optimizing Multiple Responses4m
Demo: Simulating Data Using the Prediction Profiler5m
2 Lektüren
Read About It1m
Summary: Design of Experiments (DOE)1m
25 praktische Übungen
Question 6.01 - 6.021m
Question 6.031m
Question 6.042m
Question 6.052m
Question 6.06 - 6.072m
Question 6.082m
Question 6.09 - 6.122m
Practice: Designing a Full Factorial Experiment10m
Question 6.13 - 6.142m
Question 6.151m
Question 6.161m
Practice: Analyzing a Replicated Full Factorial Experiment10m
Question 6.171m
Question 6.18 - 6.192m
Practice: Designing a Fractional Factorial Experiment10m
Practice: Analyzing a 20-Run Custom Design10m
Question 6.21- 6.22
Question 6.23 - 6.242m
Practice: Analyzing a Custom Central Composite Design10m
Practice: Optimizing the Heck Reaction10m
Question 6.261m
Question 6.27 - 6.282m
Question 6.291m
Question 6.301m
Practice: Optimizing Multiple Responses10m
Woche
8Woche 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 (Gesamt 143 min), 2 Lektüren, 32 Quiz
39 Videos
Introduction to Predictive Modeling5m
Overfitting and Model Validation8m
Demo: Creating a Validation Column3m
Assessing Model Performance: Prediction Models5m
Demo: Fitting a Multiple Linear Regression Model with Validation2m
Assessing Model Performance: Classification Models3m
Receiver-Operating Characteristic (ROC) Curves4m
Demo: Fitting a Logistic Model with Validation1m
Demo: Changing the Cutoff for Classification3m
Introduction to Decision Trees1m
Classification Trees4m
Demo: Creating a Classification Tree4m
Regression Trees5m
Demo: Fitting a Regression Tree3m
Decision Trees with Validation4m
Demo: Fitting a Decision Tree with Validation3m
Random (Bootstrap) Forests5m
Demo: Variable Selection with a Bootstrap Forest2m
What is a Neural Network?1m
Interpreting Neural Networks3m
Demo: Fitting a Neural Network3m
Predictive Modeling with Neural Networks3m
Demo: Fitting a Neural Model with Two Layers3m
Introduction to Generalized Regression2m
Fitting Models Using Maximum Likelihood3m
Demo: Fitting a Linear Model in Generalized Regression3m
Demo: Variable Selection in Generalized Regression4m
Introduction to Penalized Regression3m
Demo: Fitting a Penalized Regression (Lasso) Model4m
Comparing Predictive Models4m
Demo: Comparing and Selecting Predictive Models4m
Introduction to Text Mining1m
Processing Text Data4m
Curating the Term List2m
Demo: Processing Unstructured Text Data4m
Visualizing and Exploring Text Data3m
Demo: Visualizing and Exploring Text Data4m
Analyzing (Mining) Text Data2m
2 Lektüren
Read About It1m
Summary: Predictive Modeling and Text Mining1m
30 praktische Übungen
Question 7.011m
Question 7.022m
Question 7.031m
Practice: Fitting a Multiple Linear Regression Model with Validation10m
Practice: Fitting a Logistic Model with Validation10m
Question 7.041m
Practice: Using a Classification Tree for Problem Solving10m
Practice: Identifying Important Variables10m
Question 7.051m
Question 7.061m
Practice: Using a Regression Tree with Validation10m
Practice: Using a Classification Tree with Validation10m
Question 7.071m
Practice: Using Trees to Identify Important Variables10m
Question 7.081m
Practice: Fitting a Simple Neural Network10m
Practice: Fitting a Neural Network for Prediction10m
Practice: Fitting a Neural Network for Classification10m
Question 7.091m
Question 7.101m
Question 7.11 - 7.122m
Practice: Reducing a Model Using Generalized Regression10m
Practice: Fitting a Regression Model using the Lasso10m
Question 7.131m
Practice: Comparing and Selecting Predictive Models10m
Question 7.141m
Question 7.152m
Question 7.161m
Practice: Developing a Term List10m
Practice: Exploring Terms and Phrases in STIPS10m
Woche
9Woche 9
Review Questions and Case Studies
In this module you have an opportunity to test your understanding of what you have learned. In addition, this module will help your prepare for certification if you choose to take the certification exam.
2 praktische Übungen
Review Questions30m
Case Studies30m
Bewertungen
- 5 stars86,36 %
- 4 stars12,12 %
- 1 star1,51 %
Top-Bewertungen von STATISTICAL THINKING FOR INDUSTRIAL PROBLEM SOLVING, PRESENTED BY JMP
von RJ23. Dez. 2021
Really good course, learnt a tremendous amount.
Thanks
von HV16. Juni 2022
great start for data analysis. General understanding, visualization.
von RS2. Sep. 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.
von AK13. Feb. 2022
very interesting course and i get a lot of things
than you very much
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Der Zugang zu Vorlesungen und Aufgaben hängt von der Art Ihrer Anmeldung ab. Wenn Sie einen Kurs im Gastmodus belegen, können Sie die meisten Kursmaterialien kostenlos einsehen. Um auf benotete Aufgaben zuzugreifen und ein Zertifikat zu erhalten, müssen Sie während oder nach Ihrer Gastphase das Zertifikat erwerben. Wenn Sie die Gastoption nicht sehen:
Der Kurs bietet möglicherweise keine Gastoption an. Sie können stattdessen eine kostenlose Testversion ausprobieren oder finanzielle Unterstützung beantragen.
Der Kurs kann stattdessen "Vollständiger Kurs ohne Zertifikat" anbieten. Mit dieser Option können Sie alle Kursmaterialien einsehen, die erforderlichen Aufgaben einreichen und eine Endnote erhalten. Dies bedeutet auch, dass Sie kein Zertifikat erwerben können.
Was bekomme ich, wenn ich das Zertifikat erwerbe?
Wenn Sie ein Zertifikat erwerben, erhalten Sie Zugriff auf alle Kursmaterialien, einschließlich bewerteter Aufgaben. Nach Abschluss des Kurses wird Ihr elektronisches Zertifikat zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
Ist finanzielle Unterstützung möglich?
Ja. Für ausgewählte Lernprogramme können Sie finanzielle Unterstützung oder ein Stipendium beantragen, wenn Sie sich die Anmeldegebühr nicht leisten können. Wenn für das von Ihnen gewählte Lernprogramm finanzielle Unterstützung oder ein Stipendium verfügbar ist, wird auf der Beschreibungsseite ein Link zu einem solchen Antrag angezeigt.
Haben Sie weitere Fragen? Besuchen Sie das Learner Help Center.
