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Statistical Thinking for Industrial Problem Solving, presented by JMP

Курс от SAS

Начальный≈ 53.5 чАнглийский

О курсе

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

Программа курса

10 модулей · 615 учебных материалов

01Course Overview8 материалов

Welcome

Course OverviewВидео Why You Need a Foundation in Statistical ThinkingВидео First Time Using JMP? View the JMP Quickstart VideoВидео

Course Logistics & Resources

Getting Started with This CourseЧтение

Учитесь у экспертов

Mia Stephens

JMP Principal Product Manager

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Обучение на Coursera

≈ 53.5 ч

10 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Statistical Thinking for Industrial Problem Solving, presented by JMP

О курсе

Навыки, которые вы освоите

Программа курса

Welcome

Course Logistics & Resources

Учитесь у экспертов

Mia Stephens

Overview

1.1 Statistical Thinking

1.2 Problem Solving

1.3 Defining the Problem

1.4 Defining the Process

1.5 Identifying Potential Root Causes

1.6 Compiling and Collecting Data

Review

Overview

2.1 Describing Data

2.2 Probability Concepts

2.3 Exploratory Data Analysis for Problem Solving

Overview

2.4 Communicating with Data

2.5 Saving and Sharing Results

2.6 Data Preparation for Analysis

Review

Overview

3.1 Statistical Process Control

3.2 Process Capability

3.3 Measurement System Studies

Review

Overview

4.1 Estimation

4.2 Foundations in Statistical Testing

4.3 Hypothesis Testing for Continuous Data

4.4 Sample Size and Power

Review

Overview

5.1 Correlation

5.2 Simple Linear Regression

5.3 Multiple Linear Regression

5.4 Introduction to Logistic Regression

Review

Overview

6.1 Introduction to DOE

6.2 Factorial Experiments

6.3 Screening Experiments

6.4 Response Surface Experiments

6.5 DOE Guidelines

Review

Overview

7.1 Essentials of Predictive Modeling

7.2 Decision Trees

7.3 Neural Networks

7.4 Generalized Regression

7.5 Model Comparison and Selection

7.6 Introduction to Text Mining

Review

Quizzes