Mia Stephens +1 weiterer Dozent

von

Statistical Thinking for Industrial Problem Solving, presented by JMP

SAS

Über diesen Kurs

36,362 kürzliche Aufrufe

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:
•	Explain the importance of statistical thinking in solving problems
•	Describe the importance of data, and the steps needed to compile and prepare data for analysis
•	Compare core methods for summarizing, exploring and analyzing data, and describe when to apply these methods
•	Recognize the importance of statistically designed experiments in understanding cause and effect

Zertifikat zur Vorlage

Erhalten Sie nach Abschluss ein Zertifikat

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Anfänger“

No prior knowledge of statistics or experience with JMP software is required.

Ca. 44 Stunden zum Abschließen

Englisch

Untertitel: Englisch

Was 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 DesignData AnalysisData Visualization (DataViz)Statistical Hypothesis TestingStatistics

Zertifikat zur Vorlage

Erhalten Sie nach Abschluss ein Zertifikat

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Anfänger“

No prior knowledge of statistics or experience with JMP software is required.

Ca. 44 Stunden zum Abschließen

Englisch

Untertitel: Englisch

Dozenten

Dozentenbewertung

4.81/5 (11 Bewertungen)

Mia Stephens

JMP Principal Product Manager

1,886 Kursteilnehmer

1 Kurse

Ledi Trutna

Analytical Training Consultant

SAS Education

1,886 Kursteilnehmer

1 Kurse

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

Woche 1

19 Minuten zum Abschließen

Course Overview

In this module you learn about the course and about accessing JMP software in this course.

19 Minuten zum Abschließen

3 Videos (Gesamt 13 min), 4 Lektüren, 1 Quiz

3 Videos

Course Overview2m

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

2 Stunden zum Abschließen

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.

2 Stunden zum Abschließen

26 Videos (Gesamt 72 min), 3 Lektüren, 17 Quiz

26 Videos

Introduction36

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

Woche 2

6 Stunden zum Abschließen

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.

6 Stunden zum Abschließen

50 Videos (Gesamt 184 min)

50 Videos

Introduction47

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

Woche 3

6 Stunden zum Abschließen

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.

6 Stunden zum Abschließen

36 Videos (Gesamt 114 min), 2 Lektüren, 33 Quiz

36 Videos

Introduction to Communicating with Data3m

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

Woche 4

6 Stunden zum Abschließen

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.

6 Stunden zum Abschließen

41 Videos (Gesamt 154 min), 3 Lektüren, 28 Quiz

41 Videos

Introduction41

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

Woche 5

7 Stunden zum Abschließen

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.

7 Stunden zum Abschließen

47 Videos (Gesamt 156 min), 2 Lektüren, 40 Quiz

47 Videos

Introduction to Decision Making with Data1m

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

Woche 6

6 Stunden zum Abschließen

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.

6 Stunden zum Abschließen

43 Videos (Gesamt 150 min), 2 Lektüren, 32 Quiz

43 Videos

Introduction34

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

Woche 7

5 Stunden zum Abschließen

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.

5 Stunden zum Abschließen

36 Videos (Gesamt 148 min), 2 Lektüren, 27 Quiz

36 Videos

Introduction1m

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

Woche 8

6 Stunden zum Abschließen

Module 7: Predictive Modeling and Text Mining

Learn how to identify possible relationships, build predictive models and derive value from free-form text.

6 Stunden zum Abschließen

39 Videos (Gesamt 143 min), 2 Lektüren, 32 Quiz

39 Videos

Introduction54

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

Woche 9

1 Stunde zum Abschließen

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.

1 Stunde zum Abschließen

2 praktische Übungen

Review Questions30m

Case Studies30m

Häufig gestellte Fragen

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Statistical Thinking for Industrial Problem Solving, presented by JMP

Statistical Thinking for Industrial Problem Solving, presented by JMP

Über diesen Kurs

Was Sie lernen werden

Kompetenzen, die Sie erwerben

Dozenten

Mia Stephens

Ledi Trutna

von

SAS

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche 1

Course Overview

Module 1: Statistical Thinking and Problem Solving

Woche 2

Module 2A: Exploratory Data Analysis, Part 1

Woche 3

Module 2B: Exploratory Data Analysis, Part 2

Woche 4

Module 3: Quality Methods

Woche 5

Module 4: Decision Making with Data

Woche 6

Module 5: Correlation and Regression

Woche 7

Module 6: Design of Experiments (DOE)

Woche 8

Module 7: Predictive Modeling and Text Mining

Woche 9

Review Questions and Case Studies

Bewertungen

Top-Bewertungen von STATISTICAL THINKING FOR INDUSTRIAL PROBLEM SOLVING, PRESENTED BY JMP

Häufig gestellte Fragen

Beliebte Online-Kurse

Beliebte Online-Spezialisierungen

Online-Zertifikate

Online-Abschlussprogramme

Coursera

Community

Mehr

Statistical Thinking for Industrial Problem Solving, presented by JMP

Statistical Thinking for Industrial Problem Solving, presented by JMP

Über diesen Kurs

Was Sie lernen werden

Kompetenzen, die Sie erwerben

Dozenten

Mia Stephens

Ledi Trutna

von

SAS

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche 1

Course Overview

Module 1: Statistical Thinking and Problem Solving

Woche 2

Module 2A: Exploratory Data Analysis, Part 1

Woche 3

Module 2B: Exploratory Data Analysis, Part 2

Woche 4

Module 3: Quality Methods

Woche 5

Module 4: Decision Making with Data

Woche 6

Module 5: Correlation and Regression

Woche 7

Module 6: Design of Experiments (DOE)

Woche 8

Module 7: Predictive Modeling and Text Mining

Woche 9

Review Questions and Case Studies

Bewertungen

Top-Bewertungen von STATISTICAL THINKING FOR INDUSTRIAL PROBLEM SOLVING, PRESENTED BY JMP

Häufig gestellte Fragen

Coursera Footer

Beliebte Online-Kurse

Beliebte Online-Spezialisierungen

Online-Zertifikate

Online-Abschlussprogramme

Coursera

Community

Mehr