Quality Engineering Statistics

Focused View

Ray Harkins, The Manufacturing Academy,Michael J. Vella

12:49:55

114 View

1 - Introduction to the Course

1 - Introduction to the Course.mp4

14:29

2 - Course Contents.mp4

05:31

2 - QES-Table-of-Contents-021823.xlsx

3 - Comments about the Use of Software.mp4

04:09

2 - Section A Collecting and Summarizing Data

4 - Data Types.mp4

03:02

5 - Data Scales.mp4

08:30

6 - Data Coding.mp4

05:59

7 - Data Integrity.mp4

04:36

8 - Introduction to Data Visualizations.mp4

02:47

9 - Stem and Leaf Diagram.mp4

01:58

9 - Stem-and-Leaf-Plot.xlsx

10 - Histogram.xlsx

10 - The Histogram in Excel.mp4

05:46

11 - Dashboards.mp4

06:34

12 - Introduction to Descriptive Statistics.mp4

10:55

13 - Graphical Depiction of Standard Deviation.mp4

02:30

14 - Measures of Dispersion in Excel.mp4

04:27

15 - The Shape of Data Pt 1.mp4

08:32

16 - The Shape of Data Pt 2.mp4

07:18

17 - Median Quartiles and IQR in Excel.mp4

09:16

18 - Box-and-Whisker.xlsx

18 - The Box Plot in Excel.mp4

03:50

3 - Section B Quantitative Concepts

19 - Statistics and Probability Concepts Pt 1.mp4

08:01

20 - NORM.DIST.xlsx

20 - NORMDIST in Excel.mp4

04:18

21 - Statistics and Probability Concepts Pt 2.mp4

06:13

4 - Section C Probability Distributions

22 - Overview of Probability Distributions Pt 1.mp4

08:31

23 - Overview of Probability Distributions Pt 2.mp4

07:41

24 - Overview of Probability Distributions Pt 3.mp4

08:32

25 - Poisson.xlsx

25 - Poisson in Excel.mp4

05:45

26 - Using Test Statistics.mp4

08:32

5 - Section D Statistical Decision Making

27 - Point Estimates.mp4

03:29

28 - Point Estimates in Excel.mp4

05:14

28 - Point-Estimates.xlsx

29 - Confidence Intervals Pt 1.mp4

07:04

30 - Confidence Intervals Pt 2.mp4

06:21

31 - Confidence Intervals in Excel.mp4

04:19

31 - Confidence-Intervals.xlsx

32 - Standard Error of the Mean.mp4

04:50

33 - Z Score and Z Table.mp4

08:16

34 - Confidence Intervals for Variance.mp4

03:21

35 - Chi Squared Coordinates.mp4

02:54

36 - Tolerance Intervals.mp4

08:19

37 - Why Hypothesis Testing.mp4

01:48

38 - The Statistical View of Data.mp4

06:19

39 - Sampling and the Hypothesis Test.mp4

05:17

40 - Errors in Hypothesis Testing.mp4

04:25

41 - Statistical Power.mp4

10:26

42 - Statistical vs Practical Significance.mp4

04:21

43 - Tools and Requirements of Statistical Design Pt 1.mp4

01:37

44 - Tools and Requirements of Statistical Design Pt 2.mp4

03:09

45 - Ttest Examples in Hypothesis Testing.mp4

06:58

46 - T Tests in Excel.mp4

14:18

47 - Ztests in Hypothesis Testing Pt 1.mp4

06:44

48 - Ztests in Hypothesis Testing Pt 2.mp4

06:41

49 - More ZTest Examples.mp4

10:26

50 - Z Test in Excel.mp4

04:03

51 - Z Tests of Proportions.mp4

07:29

52 - Goodness of Fit.mp4

08:10

53 - Goodness of Fit in Excel.mp4

03:36

54 - The Normal Probability Plot in Excel.mp4

08:31

55 - ANOVA and the F Distribution.mp4

06:27

56 - ANOVA in Excel.mp4

03:49

57 - Twoway ANOVA Overview.mp4

01:59

58 - Twoway ANOVA in Excel Rev 1.mp4

03:41

59 - Contingency Tables.mp4

04:27

6 - Section E Relationships Between Variables

60 - Understanding Linear Regression Pt 1.mp4

09:28

61 - Understanding Linear Regression Pt 2.mp4

07:35

62 - Correlation and the Coefficient of Determination.mp4

08:26

63 - Linear Regression in Excel.mp4

06:49

63 - Linear-Regression.xlsx

64 - Overview of Time Series Analysis.mp4

08:50

7 - Section F Statistical Process Control SPC

65 - Introduction to SPC Section.mp4

05:47

66 - Overview of SPC.mp4

10:19

67 - Control Chart Terminology.mp4

08:26

68 - What to Chart.mp4

04:09

69 - Variation and Subgrouping.mp4

04:00

70 - Xbar and R Chart Pt 1.mp4

02:39

71 - Xbar and R Chart Pt 2.mp4

15:31

72 - Xbar and R chart Pt 3.mp4

04:47

72 - x-bar-R.xlsx

73 - Xbar and s Chart Pt 1.mp4

03:07

74 - Xbar and s Chart Pt 2.mp4

08:30

74 - x-bar-s.xlsx

75 - IXMR Chart Pt 1.mp4

02:17

76 - IXMR Chart Pt 2.mp4

10:06

76 - I-MR.xlsx

77 - p Chart Part 1.mp4

01:54

78 - p Chart Part 2.mp4

18:26

78 - p-Chart.xlsx

79 - np Chart Pt 1.mp4

02:05

80 - np Chart Pt 2.mp4

07:39

80 - np-Chart.xlsx

81 - c Chart Pt 1.mp4

01:59

82 - c Chart Pt 2.mp4

06:10

82 - c-Chart.xlsx

83 - u Chart Pt 1.mp4

02:25

84 - c-Chart.xlsx

84 - u Chart Pt 2.mp4

09:00

85 - Interpreting Control Charts Pt 1.mp4

02:06

86 - Interpreting Control Charts Pt 2.mp4

12:18

87 - Interpreting Control Charts Pt 3.mp4

08:25

88 - Control Chart Selection Process.mp4

02:45

88 - Control-Chart-Selection.pdf

89 - Short Run SPC.mp4

09:17

8 - Section G Process and Performance Capability

90 - Introduction to Process Capability Analysis.mp4

06:30

91 - Specification Limits.mp4

04:18

92 - Sampling Frequency.mp4

04:38

93 - Understanding Capability Indices.mp4

12:52

94 - Sample Size and the Normal Distribution.mp4

08:15

95 - Arithmetic Mean.mp4

03:21

96 - The Histogram.mp4

10:55

97 - Excels Data Analysis Add In.mp4

01:58

98 - Overview of the Normal Distribution.mp4

10:14

99 - Standard Deviation.mp4

07:54

100 - Properties of the Normal Distribution Curve.mp4

05:20

101 - Plotting the Normal Curve.mp4

05:47

102 - Pp and Ppk Pt 1.mp4

07:44

103 - Pp and Ppk Pt 2.mp4

07:40

104 - Ppk-Population.xlsx

104 - Pp and Ppk Pt 3.mp4

08:48

105 - Ppk-Sample.xlsx

105 - Pp and Ppk of a Sample.mp4

06:36

106 - Cp and Cpk Pt 1.mp4

03:33

106 - Table-of-Control-Chart-Constants.xlsx

107 - Cp and Cpk Pt 2.mp4

06:04

108 - Cpk.xlsx

108 - Cp and Cpk Pt 3.mp4

03:38

109 - Interpreting the Capability Indices.mp4

15:58

110 - Difference-Between-Ppk-and-Cpk.xlsx

110 - The Difference Between Cpk and Ppk.mp4

06:18

111 - Process-Capability-Analysis-Summary-021823.xlsx

111 - Summary of Process Capability.mp4

03:03

9 - Section H Design and Analysis of Experiments

112 - Introduction to a DOE Pt 1.mp4

03:14

113 - Introduction to a DOE Pt 2.mp4

03:44

114 - DOE Terminology.mp4

07:58

115 - Tips for a Successful DOE.mp4

02:39

116 - Types pf Experimental Designs.mp4

02:06

117 - Additional DOE concepts.mp4

08:48

118 - Full Factorial Experiments Pt 1.mp4

02:57

119 - Full Factorial Experiments Pt 2.mp4

09:21

120 - Fractional Factorial Designs and Taguchi Methods.mp4

08:01

10 - Conclusion to the Course

121 - Conclusion to the Course.mp4

02:29

122 - Bonus Lecture.mp4

05:29

Description

The tools, theories, and case studies you need to understand the analytical methods of quality engineering

What You'll Learn?

Collecting and summarizing data including type of data, measurement scales, collection methods, visualization techniques, and descriptive statistics
Statistics and probability terminology and concepts
Statistical decision making including point estimates, confidence intervals, hypothesis testing, paired comparison tests, goodness of fit tests, ANOVA and more
Tools for examining the relationships between variables such as linear regression, correlation, and time series analysis.
Control charting: Objective and benefits, common and special causes, variable charts, attribute charts, interpreting the results, and short run SPC
Process capability analysis: Pp, Ppk, Cp, Cpk, control limits, specification limits, and interpreting actual histograms and capability indices
Design and analysis of experiments: Terminology, planning and organizing experiments, replication, balance, order and more; full and fractional factorial
All topics in the "Quantitative Methods and Tools" section of the ASQ Certified Quality Engineer Body of Knowledge

Who is this for?

Quality engineers, quality technicians, quality managers

Industrial engineers, manufacturing engineers

Manufacturing managers, operations managers

Students studying for the ASQ Certified Quality Engineer exam

More details

Description
This course, Quality Engineering Statistics, is the most comprehensive course of its kind on Udemy. Featuring over 100 videos, this course covers all the analytical methods you need to succeed as a quality engineer, technician or manager. Plus its analytical methods -- many of which are detailed in Microsoft Excel -- will also serve industrial, manufacturing and process engineers and managers very well.
For those interested in preparing for the ASQÂ Certified Quality Engineer's exam, this course covers all topics in the "Quantitative Methods and Tools" section of their July 2022 Body of Knowledge.
But for those not interested in taking a certification exam, this course covers a very wide range of topics that will certainly help advance your career as a quality professional.
Topic covered include:
A. Collecting and Summarizing Data
1. Types of data
2. Measurement scales
3. Data collection methods
4. Data accuracy and integrity
5. Data visualization techniques
6. Descriptive statistics
7. Graphical methods for depicting distributions
B. Quantitative Concepts
1. Terminology
2. Drawing statistical conclusions
3. Probability terms and concepts
C. Probability Distributions
1. Continuous distributions
2. Discrete distributions
D. Statistical Decision-Making
1. Point estimates and confidence intervals
2. Hypothesis testing
3. Paired-comparison tests
4. Goodness-of-fit tests
5. Analysis of variance (ANOVA)
6. Contingency tables
E. Relationships Between Variables
1. Linear regression
2. Simple linear correlation
3. Time-series analysis
F. Statistical Process Control (SPC)
1. Objectives and benefits
2. Common and special causes
3. Selection of variable
4. Rational subgrouping
5. Control charts
6. Control chart analysis
7. Short-run SPC
G. Process and Performance Capability
1. Process capability studies
2. Process performance vs. specifications
3. Process capability indices
4. Process performance indices
H. Design and Analysis of Experiments
1. Terminology
2. Planning and organizing experiments
3. Design principles
4. Full-factorial experiments
5. Two-level fractional factorial experiments
Far more than a simple exam prep class, Quality Engineering Statistics is taught by two "hands on", senior manufacturing professionals that share DOZENSÂ of real-life examples and case studies drawn from their decades of experience.
If want to advance your analytical skill set and prepare yourself to solve increasingly complex problems in the workplace, then this is the class for you! Quality Engineering Statistics will give you teach you the skills you need to tackle the toughest problems in industry, and as a result, advance your career as a manufacturing quality professional.
SIGNÂ UPÂ TODAY!!
Who this course is for:
Quality engineers, quality technicians, quality managers
Industrial engineers, manufacturing engineers
Manufacturing managers, operations managers
Students studying for the ASQ Certified Quality Engineer exam

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Quality Management

Ray Harkins, The Manufacturing Academy

Instructor's Courses

Ray Harkins is a senior manufacturing professional with 30 years experience in manufacturing engineering, quality management, and business analysis. During his career, he has toured hundreds of manufacturing facilities and worked with leading industry professionals throughout North America and Japan. He earned his Bachelor of Science from the University of Akron where he majored in Engineering Technology, his Master of Science from Rochester Institute of Technology where he majored in Manufacturing Leadership and Project Management, and his Master of Business Administration from Youngstown State University. He is a senior member of the American Society of Quality, and holds their Quality Engineering (CQE), Quality Technician (CQT), Quality Auditing (CQA) and Calibration Technician (CCT) certifications. Ray has written extensively for national trade publications on the topics of quality engineering and career management, and has taught over 32,000 students through the Udemy platform on a range of manufacturing-related topics.

Michael J. Vella

Instructor's Courses

Mike Vella has joined the Udemy Instructors team on the Manufacturing Academy site following a 42-year career in Quality and Operations in the Automotive and Food Manufacturing industries. Most recently, Mike retired in 2021 as Senior VP Operations at the Suter Company an employee-owned food producer located in Sycamore, Illinois. Prior to joining Suter, Mike was the Vice President and General Manager of TI Automotive’s Brake and Fuel Group in North America before retiring in 2006. Mike was with TI Automotive for 30 years. Mike received his MBA from Wayne State University and his BS from Western Michigan University.Mike believes that a leader’s legacy is partially formed by sharing what you know they know and what they have experienced to develop others along their own path in life. Mike was a frequent speaker and trainer at TI Automotive and with The Suter Company along with his support of education within the American Society for Quality, Greater Detroit section and the Automotive Division. A Senior Member and Fellow of the ASQ, Mike retains his certification as a Quality Engineer and Reliability Engineer. Mike has been honored as Greater Detroit Section Man of the Year. Mike resides in Aurora, Illinois with his wife Lynne.

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