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# QE Tools Software Tutorial

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### QE Tools Software Tutorial

1. 1. QE Tools Software Tutorial QE TOOLS An excel-based Six Sigma statistical software add-in. qetools.com 1
2. 2. Table of Contents QETools Menu Items............................... 3 VIII. Measurement Systems Analysis Tools..... 66 1. Gage R&R Template............................................... 67 I. Getting Started ....................................... 4 2. Repeated Measurement Template ........................... 73 3. Attribute Agreement Analysis Template ................... 77 II. Six Sigma Methods – Tool Roadmap....... 11 IX. Control Charts ..................................... 80 III. Process Analysis – Qualitative Tools ....... 14 1. X-bar/Range Chart .................................... 81 1. SIPOC Diagram...................................................... 16 2. Individual/Moving Range Chart ....................... 85 2. Cause-and-Effect Diagram ...................................... 17 3. P-Chart ................................................. 88 3. Control Plan Template ............................................ 18 4. NP-Chart ............................................... 92 IV. Process Capability Summary .................. 20 5. U-Chart ................................................. 94 1. Sigma Level Calculator ........................................... 22 6. C-Chart ................................................. 98 2. DPM Calculator – Normal........................................ 24 X. Tabulation ..........................................100 3. Process Capability Summary ................................... 25 1. Cross Tabulation ...................................... 101 V. Descriptive Statistics ............................. 36 2. Binary Cross Tabulation .............................. 105 VI. Graphical Tools..................................... 40 XI. Hypothesis Tests .................................109 1. Run Chart.............................................................. 41 1. Test Two Variances (F-Test) ........................ 110 2. Pareto Analysis ...................................................... 44 2. Test Two Means (Independent 3. Histogram ............................................................. 48 Sample t-Test) ....................................... 114 4. Box Plot (single or multi) ........................................ 51 3. Paired t-Test .......................................... 118 5. Scatter Plot............................................................ 56 4. Test Two Proportions ................................ 121 Note: Not all tools are shown in this tutorial. VII. Correlation and Simple Regression ......... 59 1. Correlation ............................................................ 60 See help files for additional examples. 2. Simple Regression.................................................. 63 2
3. 3. QE Tools Menu Items 3
4. 4. I. Getting Started
5. 5. Getting Started – Excel Menu QE Tools appears as aamenu QE Tools appears as menu option in the main Excel toolbar. option in the main Excel toolbar. 5
6. 6. Getting Started – New Data Sheet QE Tools uses its own data sheet QE Tools uses its own data sheet when performing analyses. when performing analyses. You may begin by creating an You may begin by creating an initial blank datasheet using the initial blank datasheet using the New Data Sheet menu pick. New Data Sheet menu pick. 6
7. 7. Data Sheets A new, pre-formatted worksheet is inserted with the A new, pre-formatted worksheet is inserted with the name DataSheet. name DataSheet. After you create aadata sheet, you can add and After you create data sheet, you can add and manipulate data in most of the ways familiar to you in manipulate data in most of the ways familiar to you in Excel (e.g., copy, paste, add formulas, etc.). Excel (e.g., copy, paste, add formulas, etc.). Note: You must define aavariable name for each data Note: You must define variable name for each data series in the Row: “Variable Name”. series in the Row: “Variable Name”. Optional, you may include upper and lower Optional, you may include upper and lower specification limits (USL and LSL) as well as aatarget specification limits (USL and LSL) as well as target (nominal) value for each variable. (nominal) value for each variable. These will automatically be referenced for those These will automatically be referenced for those tools that require specification limits for analysis. tools that require specification limits for analysis. 7
8. 8. Data Format in “DataSheet” Data variables may either be values or Data variables may either be values or calculations of other variables. calculations of other variables. Examples: Examples: ‘TotalVisit’ list values ‘TotalVisit’ list values ‘TotalWait’ and ‘WattoVisit’ are formula. ‘TotalWait’ and ‘WattoVisit’ are formula. Data for any variable may be constructed Data for any variable may be constructed using standard Excel formulas. using standard Excel formulas. 8
9. 9. Variable Names When entering variable names, QE Tools may update after you enter them or paste from another worksheet. QE Tools uses an algorithm to standardize variable names. The algorithm ensures that: Certain characters are not allowed in variable names. The following characters are stripped from variable names: :, , /, ?, *, [, ], ‘ (apostrophe), <space> Variable names are no longer than 16 characters. Names that are longer are shortened by using the first 8 and last 8 characters of whatever is entered. Duplicate names are not allowed to insure QE Tools knows which variable you wish to analyze. 9
10. 10. Number of Worksheet Warnings QETools warns of having too many active worksheets because performance may be diminished with increasing file size. After 30 worksheets, QE tools issues a warning message. Recommend creating a second analysis file or removing unused worksheets. 10
11. 11. II. Six Sigma Methods – Tool Roadmap 11
12. 12. Six Sigma Tool Roadmap QE Tools provides a Six Sigma problem solving roadmap with common analysis steps and hyperlinks to analysis tools and templates. 12
13. 13. Example: Measure Phase Blue Text Represent Hyperlinks To Various Analysis and Templates in QE Tools 13
14. 14. III. Process Analysis – Qualitative Analysis Tools Working with ideas / text 14
15. 15. Process Analysis – Qualitative Tools SIPOC Diagram Cause-Effect Diagram * Sample Templates Provided QFD - House of Quality* FMEA Table* Process Control Plan Manufacturing or Transactional* 15
16. 16. Process Analysis Tools > SIPOC Sample Excel Data File: qetools-sampledata.xls Select Variables Using SIPOC Dialogue Box SIPOC Diagram - Loan Process OUTPUT: Suppliers Inputs Process Outputs Customers • Appraisers • Lender • Loan Documents • Mortgage Programs Customers • Insurance • Interest Rates • Mortgage • External Companies Underwriter • Title Companies • Type of Loan • Lending Institution • Government • Loan Value Step 1: Step 2: Step 3: Step 4: Final: •Prepare •Process •Underwrite •Clear •Close Loan Loan Loan Loan Conditions 16
17. 17. Process Analysis Tools > Cause-Effect Diagram May enter data Directly in dialogue Box. However, we recommend entering reasons for each cause category in data sheet column. 17
18. 18. Process Analysis Tools > Control Plan Select Control Plan Template 18
19. 19. QE Tools Control Plan Template Note: worksheets may be modified per user preference. 19
20. 20. IV. Process Capability Summary 20
21. 21. Process Capability Summary Data Analysis Tools Sigma Level Calculator DPM Calculator - Normal Process Capability Graphical Summary* Variable is Normal Variable is Non-Normal – Best Fit with Weibull Distribution Variable is Binary – Assume Binomial Distribution Note: Process Capability Graphical Summary includes: summary statistics, observed DPM, expected DPM (distribution), histogram, run charts, box plot, control charts where applicable 21
22. 22. Process Capability Summary > Sigma Level Calculator 22
23. 23. Sigma Level Calculator - Example Three different methods are available to calculate the “sigma level” Three different methods are available to calculate the “sigma level” depending on the format of information available from your process. Enter depending on the format of information available from your process. Enter the appropriate information in white boxes and sigma level is calculated the appropriate information in white boxes and sigma level is calculated automatically. automatically. 23
24. 24. Process Capability Summary > DPM Calculator - Normal IfIfdata may be assumed to data may be assumed to be normal, you may input be normal, you may input the average, standard the average, standard deviation and specification deviation and specification limits in white boxes and QE limits in white boxes and QE Tools automatically Tools automatically estimates Defects per estimates Defects per million. million. 24
25. 25. Process Capability – Graphical Summary* Different Process Capability Summaries are available depending on data / distribution. Continuous Variable and Normal Distribution Continuous Variable and Non-Normal – Best Fit with Weibull Distribution Binary Variable – Distribution assumed Binomial *Note: Process Capability Graphical Summary includes: summary statistics, observed DPM, expected DPM (distribution), histogram, run charts, box plot, control charts where applicable 25
26. 26. Process Capability Summary - Normal 26
27. 27. Process Capability Summary – Normal – Dialogue Box Select one or more variables from the variable Select one or more variables from the variable list to analyze (note: each variable is output list to analyze (note: each variable is output to its own results worksheet). to its own results worksheet). Select type of control charts to Select type of control charts to display on the results worksheet display on the results worksheet (note: subgroup size is assumed 11 (note: subgroup size is assumed for “Ind / /Moving Range”. for “Ind Moving Range”. Options –– Options -- show out-of-control patterns. -- show out-of-control patterns. -- manual scale run chart -- manual scale run chart -- enter specification limits ififnot -- enter specification limits not already entered on “data sheet”. already entered on “data sheet”. 27
28. 28. Process Capability Summary – Normal – Using Data Ranges Optionally select aarange of data to Optionally select range of data to analyze from aaworksheet other than analyze from worksheet other than the DataSheet (note: the first row is the DataSheet (note: the first row is assumed to be aalabel used as the assumed to be label used as the variable name). variable name). 28
29. 29. Process Capability Summary – Normal Results The output contains several sections: The output contains several sections: • • Statistical summary Statistical summary • • Expected Defects per Million Expected Defects per Million (distribution) (distribution) • • Observed Defects per Million Observed Defects per Million • • Histogram Histogram • • Run chart Run chart • • Box plot Box plot • • Control charts Control charts Sample Excel Data File: qetools-sampledata.xls Output: Time in Waiting Room 29
30. 30. Results – Summary Stats - Histogram Notice that the Upper Specification Notice that the Upper Specification Limit (USL) from the datasheet is Limit (USL) from the datasheet is displayed on the chart and displayed on the chart and summarized in the data output. summarized in the data output. 30
31. 31. Results – Run Chart – Box Plot The Run Chart provides aatime trend. The Run Chart provides time trend. Box Plot summarizes basic Box Plot summarizes basic distribution. Example shown is skewed distribution. Example shown is skewed right (more points >>median). right (more points median). 31
32. 32. Process Capability Summary – Non-normal (Weibull) 32
33. 33. Results- Non-normal (Weibull) The output contains: The output contains: • • Statistical summary Statistical summary • • Expected Defects per Million Expected Defects per Million (distribution) (distribution) • • Observed Defects per Million Observed Defects per Million • • Histogram Histogram • • Run chart Run chart • • Box plot Box plot 33
34. 34. Process Capability Summary – Binary (Binomial) 34
35. 35. Process Capability Summary – Binary (Binomial) Dialogue Box Select two variables for the Select two variables for the analysis (one variable represents analysis (one variable represents the number of units and the the number of units and the second is for the number second is for the number defective). defective). Do not enter defective Do not enter defective percentages ––QE tools percentages QE tools automatically calculates. automatically calculates. Alternatively, select one variable Alternatively, select one variable for the number defective and for the number defective and enter aaconstant sample size. enter constant sample size. Specify aatarget for the process Specify target for the process (note: the target does not figure (note: the target does not figure into any calculations but does into any calculations but does appear on the results worksheet appear on the results worksheet for reference). for reference). 35
36. 36. V. Descriptive Statistics 36
37. 37. Descriptive Statistics > Basic Descriptive Statistics 37
38. 38. Descriptive Statistics > Basic Descriptive Statistics Dialogue Box Select one or more variables Select one or more variables from the variable list to analyze. from the variable list to analyze. Stats may also be calculated by Stats may also be calculated by aa“grouping variable.” Select aa “grouping variable.” Select grouping variable from the grouping variable from the variable list or select aarange variable list or select range from aaworksheet. from worksheet. 38
39. 39. Descriptive Statistics > Basic Descriptive Statistics – Results Basic statistics are calculated for one or more variables that Basic statistics are calculated for one or more variables that are entered into the analysis. are entered into the analysis. One Variable Multiple Output Variables One Output Variable Stratified by Grouping Variable Here, output is shown for the Here, output is shown for the variable “TotalWait” with aagrouping variable “TotalWait” with grouping variable of “Team” (which has variable of “Team” (which has values of “A,” “B” and “C”). values of “A,” “B” and “C”). 39
40. 40. VI. Graphical Tools 40
41. 41. Graphical Tools > Run Chart 41
42. 42. Graphical Tools > Run Chart Dialogue Box Select one or more variables from the variable Select one or more variables from the variable list to analyze (note: all output appear on aa list to analyze (note: all output appear on single run chart). single run chart). Optionally, modify the scale Optionally, modify the scale setting for the Y axis. setting for the Y axis. 42
43. 43. Run Chart – Sample Results Single Variable Or, You May Select Multiple Variables 43
44. 44. Graphical Tools > Pareto Analysis 44
45. 45. Graphical Tools > Pareto Analysis Dialogue Box Select aadata and aacategory variable to Select data and category variable to create aaPareto chart. create Pareto chart. Optionally, modify the output Optionally, modify the output setting for the left and right Y setting for the left and right Y axes. axes. 45
46. 46. Graphical Tools > Pareto Analysis Data Format Category Sum Data* *Note: sum data may be calculated by summing up data columns for different categories, or using the tabulation tool inside QETools. 46
47. 47. Graphical Tools > Pareto Analysis – Sample Result Tool Option: Show Relative and Cumulative Frequency 47
48. 48. Graphical Tools > Histogram 48
49. 49. Histogram – Dialogue Box Replace Select aavariable from the variable list to Select variable from the variable list to analyze (note: ififselect more than one analyze (note: select more than one variable, each variable is output to its own variable, each variable is output to its own results worksheet). results worksheet). Use either Absolute or Relative Use either Absolute or Relative Frequency for Y-axis. Frequency for Y-axis. 49
50. 50. Histogram – Sample Results The output contains: The output contains: • • Frequency table (frequency of observations Frequency table (frequency of observations falling within aacertain data range or bin) falling within certain data range or bin) Slide the slider to Slide the slider to • • Frequency count: (previous bin ~~ dynamically adjust the bin Frequency count: (previous bin dynamically adjust the bin current bin] current bin] widths (and update the widths (and update the • • Histogram data table and the Histogram data table and the histogram) histogram) Enter aa“1ststBin” and/or Enter “1 Bin” and/or “Width” value to adjust the “Width” value to adjust the output to your liking. “1ststBin” output to your liking. “1 Bin” and the slider adjustment (for and the slider adjustment (for bin width) can be used bin width) can be used simultaneously. simultaneously. 50
51. 51. Graphical Tools > Box Plot (single or multi) 51
52. 52. Box Plot (single or multi) – Dialogue Box Example Single 52
53. 53. Box Plot (single) – Sample Results 53
54. 54. Graphical Tools > Box Plot (multi) Dialogue Box -- Include Grouping Variable 54
55. 55. Box Plot (multi) – Sample Results 55
56. 56. Graphical Tools > Scatter Plot 56
57. 57. Graphical Tools > Scatter Plot Dialogue Box Select an input (X) and output Select an input (X) and output (Y) variable to plot. (Y) variable to plot. Alternatively, select aa2-column Alternatively, select 2-column data range from any worksheet data range from any worksheet (include data labels in the first (include data labels in the first row, and use Column 11for X, row, and use Column for X, Col 22for Y). Col for Y). Scatter plot chart options: Scatter plot chart options: • • Trend line ––show linear, Trend line show linear, quadratic, or no trend line. quadratic, or no trend line. • • Show R2 2 Show R • • Show best fit equation Show best fit equation 57
58. 58. Graphical Tools > Scatter Plot Sample Results Correlation Coefficient, R R-squared Linear Model R R2 0.86 0.73 Timeinwatingroom TotalWait Scatter Plot 160 y = 1.6054x + 17.045 2 140 R = 0.7313 120 100 TotalWait 80 60 40 Note: Scatter Plot also provided 20 under simple regression tool. 0 0 20 40 60 80 100 Timeinwatingroom 58
59. 59. VII. Correlation and Simple Regression 59
60. 60. Regression and Correlation > Correlation Matrix 60
61. 61. Regression and Correlation > Correlation Matrix Dialogue Box Select one or more variables from the Select one or more variables from the variable list to analyze. variable list to analyze. Variables must contain numeric data to Variables must contain numeric data to be included in aacorrelation matrix. be included in correlation matrix. “Text” data will be omitted [more]. “Text” data will be omitted [more]. Enter aathreshold to highlight data Enter threshold to highlight data with aastrong correlation with strong correlation (|correlation| >>threshold will be (|correlation| threshold will be highlighted). Typically, aa0.7 highlighted). Typically, 0.7 cutoff is standard to indicate aa cutoff is standard to indicate strong correlation. strong correlation. 61
62. 62. Regression and Correlation > Correlation Matrix, R, Results Matrix shows all pairwise comparisons of selected variables (Max 50 variables). Data with aacorrelation stronger than the threshold is emphasized with bold Data with correlation stronger than the threshold is emphasized with bold text (note: either aastrong positive or strong negative will be emphasized). text (note: either strong positive or strong negative will be emphasized). 62
63. 63. Regression and Correlation > Simple Linear Regression 63
64. 64. Regression and Correlation > Simple Regression Dialogue Box Select the variables to analyze. Select the variables to analyze. X and Y variables must have the X and Y variables must have the same N or the analysis will same N or the analysis will terminate. terminate. 64
65. 65. Regression and Correlation > Simple Linear Regression Results Response optimizer allows you to input an X and Response optimizer allows you to input an X and solve for Y (or Vice Versa) based on best fit equation. solve for Y (or Vice Versa) based on best fit equation. Alternatively, you may use slider buttons directly in graph. 65 Alternatively, you may use slider buttons directly in graph.
66. 66. VIII. Measurement Systems Analysis Tools 66
67. 67. 1. Measurement Systems Analysis > Gage R and R 67
68. 68. Measurement Systems Analysis > Gage R and R Template 68
69. 69. Measurement Systems Analysis > Gage R and R – Header Section Needed for Calculations Width = USL- LSL Enter Study Variation Multiplier: K=5.15 or 6 5.15 (predict 99% of the area under normal distribution curve), Or 6 (predict 99.73% of the area under normal distribution curve) 69
70. 70. Measurement Systems Analysis > Gage R and R – Data Entry Section Data Entry Section for Measurement System Study Max 10 parts, 3 operators, 3 trials 70
71. 71. Gage R&R -- Sample Data 71
72. 72. Measurement Systems Analysis > Gage R and R -- Calculations See Notes for Formulas Sample Output 72
73. 73. 2. Measurement Systems Analysis > Repeated Measurements Study 73
74. 74. Measurement Systems Analysis >> Repeated Measurements Study Template Enter Data in White Cells 74
75. 75. Data Entry Max = 50 pairs Note: % tolerance calculations Requires a USL and LSL to obtain tolerance width for Note: Calculations in Yellow 75
76. 76. Sample Results Solid Line: Best Fit To remove best fit line, click on line on graph and hit delete. 76
77. 77. 3. Measurement Systems Analysis > Attribute Matching Study 77
78. 78. Measurement Systems Analysis > Attribute Matching Study - Template Enter Data -- Must include a Standard (or reference) in 1st Column Enter Alpha Enter Data for (Type I) error 1..3 Appraisers in additional columns Note: Calculations In yellow box Max 50 samples 78
79. 79. Measurement Systems Analysis > Attribute Matching Study - Template OK, if confidence intervals overlap 79
80. 80. IX. Control Charts 80
81. 81. 1) Control Charts > X-bar / Range 81
82. 82. Control Charts > X-bar / Range Dialogue Box – Data in 1 column Data Entry Options All data in 1 column Subgroup size every XX rows Sample Data Sheet Etc. 82
83. 83. Control Charts > X-bar / Range Dialogue Box – Data across columns Data Across Columns Each row is a subgroup Select 2 or more variables Sample Data Sheet 83
84. 84. Control Charts > X-bar / Range Sample Output Note: after creating A chart, you may Exclude Points and hit “UPDATE CHARTS” to reconfigure control charts. 84
85. 85. 2) Control Charts > Individual / Moving Range 85
86. 86. Control Charts > Individual / Moving Range Dialogue Box Select one or more variables from the Select one or more variables from the variable list to analyze (note: each variable is variable list to analyze (note: each variable is outputted to its own results worksheet). outputted to its own results worksheet). Data can also be selected using aa Data can also be selected using 1-column data range in any worksheet 1-column data range in any worksheet (Include aalabel name in the first row). (Include label name in the first row). Check this box to show out-of-control Check this box to show out-of-control data points on the control charts data points on the control charts 86
87. 87. Control Charts > Individual / Moving Range – Sample Output etc .. Note: after creating A chart, you may Exclude Points and hit “UPDATE CHARTS” to reconfigure charts. 87
88. 88. 3) Control Charts > P-chart 88
89. 89. Control Charts > P-chart Dialogue Box Select two variables –– Select two variables one with number of units inspected and one with number of units inspected and another with number of defective units. . another with number of defective units Do not enter the “Percent Defective (p)” –– Do not enter the “Percent Defective (p)” QETools calculates this in the analysis. QETools calculates this in the analysis. Alternatively, specify aaconstant sample Alternatively, specify constant sample size and select aasingle variable with the size and select single variable with the number of defective units. . number of defective units Data also can be selected using aa2- Data also can be selected using 2- column data range. column data range. (Col 1: Number of Units; (Col 1: Number of Units; Col2: Number of Defects; Col2: Number of Defects; Include label names in the first row). Include label names in the first row). 89
90. 90. Control Charts > P-chart Sample Results (Unequal Sample Size) Note: after creating a chart, you may Exclude Points and hit “UPDATE CHARTS” to reconfigure charts. Only appears if unequal sample size 90
91. 91. Control Charts > P-chart Sample Results (Constant Sample Size) Example with constant subgroup sample size P Chart 0.40 0.35 0.35 0.30 0.29 0.25 Sample p 0.23 0.20 0.15 0.10 1 3 5 7 9 11 13 15 17 19 21 23 25 Subgroup 91
92. 92. 4) Control Charts > NP-chart 92
93. 93. Control Charts > NP-chart Dialogue Box and Results Requires entering constant subgroup sample size 93
94. 94. 5) Control Charts > U-chart 94
95. 95. Control Charts > U-chart Dialogue Box Enter variable with the # units (subgroup sizes) or constant sample size. Enter # defects (errors) 95
96. 96. Control Charts > U-chart Sample Output (Unequal Sample Size) Note: after creating a chart, you may Exclude Points and hit “UPDATE CHARTS” to reconfigure charts. Only appears if unequal sample size 96
97. 97. Control Charts > U-chart with Constant Sample Size 97
98. 98. 6) Control Charts > C-chart 98
99. 99. Control Charts > C-chart Dialogue Box and Results Enter column of defects Control Chart for Attributes C Chart 30 27.31 25 20 15 15.50 Sample c 10 5 3.69 0 1 2 3 4 5 6 7 8 9 10 11 12 Subgroup 99
100. 100. X. Tabulation 100
101. 101. 1) Tabulation > Cross Tabulation 101
102. 102. Tabulation > Cross Tabulation – Dialogue Box Data Entry Options: Data Entry Options: One or more variables from One or more variables from Datasheet Datasheet Or, Or, Data range in excel worksheet Data range in excel worksheet Tabulation may also be Tabulation may also be performed using aa“grouping performed using “grouping variable.” variable.” A grouping variable may be A grouping variable may be selected from the variable list selected from the variable list or using aaworksheet range. or using worksheet range. 102
103. 103. Tabulation > Cross Tabulation – Sample Output (binary data) Sample output (binary data) Data are Data are tallied by aa tallied by Overall binary Overall binary grouping grouping data are summed data are summed variable variable to the right. to the right. (“LikelyReturn”) (“LikelyReturn”) Totals are calculated for each Totals are calculated for each variable in the analysis. variable in the analysis. 103
104. 104. Tabulation > Cross Tabulation – Sample Output (non-binary data) Data are Data are tallied by aa tallied by grouping grouping variable variable (“#Concerns”) (“#Concerns”) Group totals and Group totals and overall data are overall data are summed to the right. summed to the right. Totals are calculated for Totals are calculated for each variable in the each variable in the analysis. analysis. 104
105. 105. 2) Tabulation > Binary Cross Tabulation 105
106. 106. Tabulation > Binary Cross Tabulation – Dialogue Box Data Entry Options: Data Entry Options: One or more variables from One or more variables from Datasheet Datasheet Or, Or, Data range in excel worksheet Data range in excel worksheet Note: Data must be binary (0/1). Note: Data must be binary (0/1). Tabulation may also be Tabulation may also be performed using aa“grouping performed using “grouping variable.” variable.” A grouping variable may be A grouping variable may be selected from the variable list selected from the variable list or using aaworksheet range. or using worksheet range. 106
107. 107. Tabulation > Binary Cross Tabulation – Sample Output (no grouping variable) Data are tallied by 0/1 Data are tallied by 0/1 Binary data and Binary data and and totaled for each and totaled for each overall data are overall data are Overall DPMO is variable in the analysis. variable in the analysis. summed to the right. summed to the right. Overall DPMO is calculated when aa Row% and Column% are Row% and Column% are calculated when defect (failure) is also calculated. also calculated. defect (failure) is coded as 1. coded as 1. Ex: PoorMealService Ex: PoorMealService Row% ==111/381 Row% 111/381 Column% ==111/447 Column% 111/447 107
108. 108. Tabulation > Binary Cross Tabulation – Sample Output (w/ grouping variable) Data are Data are tallied by aa tallied by grouping grouping Data are tallied by 0/1 Data are tallied by 0/1 Grouped (binary) Grouped (binary) variable variable and totaled for each and totaled for each and overall data and overall data (“LikelyReturn”) variable in the analysis. are summed to the (“LikelyReturn”) variable in the analysis. are summed to the Overall DPMO is right. right. Overall DPMO is calculated when aa calculated when defect (failure) is defect (failure) is coded as 1. coded as 1. 108
109. 109. XI. Hypothesis Tests 109
110. 110. 1) Hypothesis Tests > Test Two Variances 110
111. 111. Hypothesis Tests > Test Two Variances Dialogue Box Data Entry Options: Data Entry Options: Two Variables from Datasheet Two Variables from Datasheet Data range in excel worksheet Data range in excel worksheet Or, Or, Using Summary Statistics Using Summary Statistics Need to enter ififone or two-tail Need to enter one or two-tail hypothesis test hypothesis test Two-tail: Two-tail: Alt Hypothesis is <> Alt Hypothesis is <> One-tail: One-tail: Alt Hypothesis is Max >>Min Alt Hypothesis is Max Min Alpha: Type I Ierror Alpha: Type error (default ==0.05) (default 0.05) 111
112. 112. Hypothesis Tests > Test Two Variances – Sample Results Statistical Test Conclusion: Returns either “Difference exists” or “No difference” 112
113. 113. Hypothesis Tests > Test Two Variances – Summary Data and Results Example: 1 Sided Test 113
114. 114. 2) Hypothesis Tests > Test Two Means - Independent 114
115. 115. Hypothesis Tests > Test Two Means – Independent Dialogue Box Data Entry Options: Data Entry Options: Two Variables from Datasheet Two Variables from Datasheet Data range in excel worksheet Data range in excel worksheet Or, Or, Using Summary Statistics Using Summary Statistics Need to enter ififone or two-tail Need to enter one or two-tail hypothesis test hypothesis test Two-tail: Two-tail: Alt Hypothesis is <> Alt Hypothesis is <> One-tail: One-tail: Alt Hypothesis is Max >>Min Alt Hypothesis is Max Min Alpha: Type I Ierror Alpha: Type error (default ==0.05) (default 0.05) 115
116. 116. Hypothesis Tests > Test Two Means – Independent Results 116
117. 117. Hypothesis Tests > Test Two Means – Summary Statistics 117
118. 118. 3) Hypothesis Tests > Test Paired Data 118
119. 119. Hypothesis Tests > Test Paired Data Dialogue Box Data Entry Options: Data Entry Options: Two Variables from Datasheet Two Variables from Datasheet Data range in excel worksheet Data range in excel worksheet Or, Or, Using Summary Statistics Using Summary Statistics Need to enter ififone or two-tail Need to enter one or two-tail hypothesis test hypothesis test Recommend 22tail for paired Recommend tail for paired Alpha: Type I Ierror Alpha: Type error (default ==0.05) (default 0.05) 119
120. 120. Hypothesis Tests > Test Paired Data Sample Results Hypothesis Test: Mean Difference = 0 Alternative Hypothesis: Difference <> 0 *Note: Repeated Measurements Analysis under Measurement Note: Box Plot of Differences Systems performs also performs a Paired t-test analysis 120
121. 121. 4) Hypothesis Tests > Test Two Proportions 121
122. 122. Hypothesis Tests > Test Two Proportions Dialogue Box Data Entry Options: Data Entry Options: Two Variables from Datasheet Two Variables from Datasheet (Note: each variable must be aa (Note: each variable must be column of 00and 1) column of and 1) Data range in excel worksheet Data range in excel worksheet Or, Or, Using Summary Statistics Using Summary Statistics Need to enter ififone or two-tail Need to enter one or two-tail hypothesis test hypothesis test Two-tail: Alt Hypothesis is <> Two-tail: Alt Hypothesis is <> One-tail: Alt Hypothesis is One-tail: Alt Hypothesis is Large Prop >>Small Prop Large Prop Small Prop Alpha: Type I Ierror Alpha: Type error (default ==0.05) (default 0.05) 122
123. 123. Hypothesis Tests > Test Two Proportions Sample Results Hypothesis: P1 = P2 (2-Tail) Hypothesis: P(Large) > P(small) Alt Hypothesis: P1 not equal P2 Alt Hypothesis: P(Large) <= P(small) Alt Hypothesis Identified 123
124. 124. Hypothesis Tests > Test Two Proportions Example Shown Below: Data fail normal approximation test for proportions Here, a warning message is given. 124
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