Basics of minitab 15 (english) v1
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Basics of minitab 15 (english) v1

on

  • 1,393 views

 

Statistics

Views

Total Views
1,393
Views on SlideShare
1,393
Embed Views
0

Actions

Likes
1
Downloads
90
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Basics of minitab 15 (english) v1 Presentation Transcript

  • 1. Minitab 15 (English)
  • 2. Minitab 15 : IntroductionMinitabIs an Application Software for Studying Statistical Tools and applying them forBusiness needs.It complements Six Sigma for its features and ease of implementation. Differentcustomized tools and techniques used in Six Sigma are available in Minitab.Minitab can also be used to plot different Graphs & Charts which are widely usedduring Six Sigma Project execution and different Business Processes.Trial version of Minitab 15 Free 30 Days Trial version of Minitab 15 can be downloaded from http://minitab.com/products/minitab/demo/ Limited customer support can also be availed during the trial period.
  • 3. Minitab 15 : IntroductionMenu BarTool BarSession Window Tables and Statistical outputs are displayed in Session windowMinitab Worksheet Worksheet is used for Data Input. This is similar to MS excel sheet. Use ‘Enter’ and ‘Tab’ key to navigate.Minitab ProjectOpening and Saving Minitab File• In Minitab, there are projects (Minitab Project, *.mpj) in which• Data is stored in Minitab Worksheet, *.mtw• Graphs are stored in Minitab Graphs, *.mgf• Results in Minitab Session, *.txt
  • 4. Minitab 15 : IntroductionBasic Tips Minitab uses different customized pre-defined functions to perform statisticaltests for analysis, hence very sensitive to input data. Before applying any tool / performing any test different pre-requisites need to beverified to get reliable output. Minitab can recognize 3 Data types : Numeric, Text and Date. Data types aredisplayed at column indicators as C1 (Numeric), C1-T (Text), C1-D (Date). Whileentering data consider the display of data type in column indicator to ensure thereis no mismatch. Minitab worksheet is used for data input. Output is displayed in SessionWindow (Table and Text) and through Graphs. Minitab uses default values (e.g. Alpha=0.05 for Hypothesis Testing) fordifferent tools and techniques. This parameters can be set at different levels asper the need and interpretation / inference should be made accordingly.
  • 5. Minitab 15 : Basic
  • 6. Topics• Graphical Summary• Normality Test• Histogram• Box Plot• Dot Plot• Pareto Chart• Cause & Effect Diagram
  • 7. Graphical Summary
  • 8. Graphical Summary S umma r y fo r T ic k e t R e s o lutio n T ime ( M ins ) A n d e rso n -D a rlin g N o rm a lity T e st A -S q u a re d 0 .2 9 P -V a lu e 0.594 M ean 55.297 S tD e v 4.617 V a ria n ce 21.314 S k e w n e ss 0 .1 4 5 2 8 0 K u rto sis -0 .3 6 5 2 8 0 N 50 M in im u m 44.910 1 st Q u a rtile 52.364 M e d ia n 55.387 3 rd Q u a rtile 57.965 45 50 55 60 65 M a xim u m 64.605 9 5 % C o n fid e n ce I n te rv a l fo r M e a n 53.985 56.609 9 5 % C o n fid e n ce I n te rv a l fo r M e d ia n 53.474 56.389 9 5 % C o n fid e n ce I n te rv a l fo r S tD e v 9 5 % C o n f id e n c e I n t e r v a ls 3 .8 5 6 5.753 M eanM edian 53.5 54.0 54.5 55.0 55.5 56.0 56.5
  • 9. Normality Test
  • 10. Histogram
  • 11. Normality Test - Histogram P r oba bility P lot of T ic k e t R e s olution T ime (M ins ) Norm a l 99 M ean 55.30 S tD ev 4.617 95 N 50 AD 0.292 90 P - V alu e 0.594 80 70Pe r c e n t 60 50 40 30 20 H is to gr a m o f T ic k e t R e s o lutio n T ime ( M ins ) 10 No r m a l 5 12 M ean 55.30 S tD ev 4.617 N 50 1 10 45 50 55 60 65 Tic ke t R e s o lut io n Time (M ins ) 8 Fr e q ue nc y 6 4 2 0 45 50 55 60 65 Tic ke t R e s o lu t io n Time ( M ins )
  • 12. Box Plot
  • 13. Dot Plot
  • 14. Box Plot - Dot Plot B o x pl o t o f T ic k e t R e s o lutio n T ime ( M ins ) 65Tic ke t R e s o lut io n Time (M ins ) 60 55 50 D o tpl o t o f T i c k e t R e s o lutio n T ime ( M ins ) 45 45 48 51 54 57 60 63 Tic ke t R e s o lu t io n Time ( M ins )
  • 15. Pareto Chart
  • 16. Pareto Chart P a r e to C ha r t o f D e fe c t T y pe s 180 100 160 140 80De fe c t Co unt 120 Pe r c e nt 100 60 80 40 60 40 20 20 0 0 De f e ct Ty p e s a x io n al or a k it y g er ic r al in nt l at og Er Le n tr th Sy io L e y io S O V im or ct pe r r d T em n o da un M Fu pr n R I m a St De f e ct C o unt 56 35 28 18 12 7 5 8 P e rce nt 33.1 20.7 16.6 10.7 7.1 4.1 3.0 4.7 C um % 33.1 53.8 70.4 81.1 88.2 92.3 95.3 100.0
  • 17. Cause & Effect Diagram
  • 18. Cause & Effect Diagram C a us e -a nd-E ffe c t D ia gr a m B ac k U p O p er atin g S y stem F i l e S yste m Ca p a ci ty Ta p e no t lo a de d CP U U ti l i za ti o n F a u l ty Me d i a Me m o r y U ti l i za ti o n S w a p U ti l i za ti o n D r i ve P r o b l e m S yste m P a n i c Ba cku p o ve r r u n S e r vi ce s n o t r u n n i n g S ch e d u l e r P r o b l e m P r i n ti n g I ssu e s F I l e p e r m i ssi o n T a p e s w r i te p r o te cte d Jo b fa i l u r e s I n c id en ts E D G Ap p l i ca ti o n a l e r ts P o w e r S u p p l y fa i l u r e s F T P jo b fa i l u r e D r i ve Br o ke n D e l a ye d p r o ce ss N e tw o r k E r r o r s Ap p l i ca ti o n i n te r fa ce fa i l u r e P r i n te r P r o b l e m Ap p l i ca ti o n se r vi ce sto p p e d Me m o r y P r o b l e m Ap p l i ca ti o n sch e d u l e LAN Ca r d P r o b l e m O r a cl e p r o ce sse s n o t r u n n i n g CP U P r o b l e m Li stn e r u n a va i l a b l e O r a cl e G O H Al e r ts D i sk P r o b l e mA p p lic atio n H ar d w ar e
  • 19. Minitab 15 : Intermediate & Advanced Tools
  • 20. Minitab 15 : ANOVA
  • 21. Topics• One-Way ANOVA• Two-Way ANOVA
  • 22. One-way ANOVA
  • 23. One-way ANOVAOne-way ANOVA: Defect Count versus Experience LevelSource DF SS MS F PExperience Level 2 52303.6 26151.8 363.72 0.000Error 12 862.8 71.9Total 14 53166.4S = 8.479 R-Sq = 98.38% R-Sq(adj) = 98.11% Individual 95% CIs For Mean Based on Pooled StDevLevel N Mean StDev --------+---------+---------+---------+-<1 Year 5 210.60 11.84 (-*-)>4 Years 5 66.80 7.98 (*-)1~4 Years 5 125.20 3.42 (-*-) --------+---------+---------+---------+- 100 150 200 250Pooled StDev = 8.48 B o x plo t o f D e fe c t C o unt 225 200 175 De fe c t Co unt 150 125 100 75 50 < 1 Ye a r > 4 Ye a r s 1 ~ 4 Ye a r s Exp e r ie n c e Le v e l
  • 24. Two-way ANOVA
  • 25. Two-way ANOVATwo-way ANOVA: Price versus City, StarsSource DF SS MS F PCity 3 21145.4 7048.5 4.12 0.017Stars 1 10224.5 10224.5 5.97 0.022Interaction 3 3411.0 1137.0 0.66 0.582Error 24 41079.0 1711.6Total 31 75859.9S = 41.37 R-Sq = 45.85% R-Sq(adj) = 30.05% Individual 95% CIs For Mean Based on Pooled StDevCity Mean +---------+---------+---------+---------DC 135.375 (--------*-------)La 135.375 (--------*-------)NY 198.125 (--------*-------)SF 151.375 (-------*--------) +---------+---------+---------+--------- 105 140 175 210 Individual 95% CIs For Mean Based on Pooled StDevStars Mean --+---------+---------+---------+-------2 137.188 (----------*---------)3 172.938 (---------*----------) --+---------+---------+---------+------- 120 140 160 180
  • 26. Minitab 15 : Control Charts
  • 27. Topics• I-MR• X-Bar/R• P-Chart• NP-Chart• U-Chart• C-Chart
  • 28. I-MR Chart
  • 29. I-MR Chart
  • 30. X-Bar/R Chart
  • 31. X-Bar/R Chart
  • 32. NP-Chart
  • 33. NP-Chart
  • 34. P-Chart
  • 35. P-Chart
  • 36. U-Chart
  • 37. U-Chart
  • 38. C-Chart
  • 39. C-Chart
  • 40. Minitab 15 :Capability Analysis
  • 41. Capability Analysis : Normal
  • 42. Capability Analysis : Normal
  • 43. Capability Analysis : Binomial
  • 44. Capability Analysis : Binomial
  • 45. Capability Analysis : Poisson
  • 46. Capability Analysis : Poisson
  • 47. Minitab 15 :Correlation-Regression
  • 48. Topics• Scatter Plot• Correlation• Regression
  • 49. Scatter Plot
  • 50. Scatter Plot
  • 51. CorrelationCorrelations: Productivity, ExperiencePearson correlation of Productivity and Experience = 0.871P-Value = 0.000
  • 52. Regression Analysis
  • 53. Regression AnalysisRegression Analysis: Productivity versus ExperienceThe regression equation isProductivity = - * + 11.5 ExperiencePredictor Coef SE Coef T PConstant -9.7 136.1 -0.07 0.945Experience 11.520 2.057 5.60 0.000S = 87.7955 R-Sq = 75.8% R-Sq(adj) = 73.4%Analysis of VarianceSource DF SS MS F PRegression 1 241811 241811 31.37 0.000Residual Error 10 77080 7708Total 11 318892
  • 54. Regression Analysis
  • 55. Regression AnalysisRegression Analysis: Effort versus Code Length, Defect Count, ...The regression equation isEffort = - 1277 + 458 Code Length + 136 Defect Count - 1.54 Resource ExperiencePredictor Coef SE Coef T PConstant -1277.3 360.6 -3.54 0.003Code Length 457.70 47.64 9.61 0.000Defect Count 135.72 61.28 2.21 0.042Resource Experience -1.544 4.579 -0.34 0.740S = 487.537 R-Sq = 86.9% R-Sq(adj) = 84.5%Analysis of VarianceSource DF SS MS F PRegression 3 25308562 8436187 35.49 0.000Residual Error 16 3803071 237692Total 19 29111633Source DF Seq SSCode Length 1 23966672Defect Count 1 1314871Resource Experience 1 27018Unusual Observations CodeObs Length Effort Fit SE Fit Residual St Resid 11 9.00 4631 3707 308 924 2.44RR denotes an observation with a large standardized residual.
  • 56. Minitab 15 :Hypothesis Testing
  • 57. Topics• 1-Sample-Z• 1-Sample-T• 2-Sample-T• Paired T• F-Test• Chi-square
  • 58. 1 Sample Z
  • 59. 1 Sample ZOne-Sample Z: Daily Call volume past monthTest of mu = 310 vs not = 310The assumed standard deviation = 10Variable N Mean StDev SE Mean 95% CI ZDaily Call volume past m 22 329.05 22.91 2.13 (324.87, 333.22) 8.93Variable PDaily Call volume past m 0.000
  • 60. 1 Sample T
  • 61. 1 Sample TOne-Sample T: Daily Call volume past monthTest of mu = 325 vs not = 325Variable N Mean StDev SE Mean 95% CI TDaily Call volume past m 22 329.05 22.91 4.88 (318.89, 339.20) 0.83Variable PDaily Call volume past m 0.417
  • 62. 2 Sample T
  • 63. 2 Sample TTwo-Sample T-Test and CI: Defects/10,000 Old Method, Defects/10,000 New MethodTwo-sample T for Defects/10,000 Old Method vs Defects/10,000 New Method N Mean StDev SE MeanDefects/10,000 Old Metho 35 252.9 18.7 3.2Defects/10,000 New Metho 20 225.5 14.8 3.3Difference = mu (Defects/10,000 Old Method) - mu (Defects/10,000 New Method)Estimate for difference: 27.4495% CI for difference: (17.66, 37.22)T-Test of difference = 0 (vs not =): T-Value = 5.63 P-Value = 0.000 DF = 53Both use Pooled StDev = 17.3927
  • 64. Paired T
  • 65. Paired TPaired T-Test and CI: Weight - Before, Weight - AfterPaired T for Weight - Before - Weight - After N Mean StDev SE MeanWeight - Before 11 85.14 3.32 1.00Weight - After 11 75.13 4.01 1.21Difference 11 10.008 3.037 0.91695% CI for mean difference: (7.968, 12.048)T-Test of mean difference = 0 (vs not = 0): T-Value = 10.93 P-Value = 0.000
  • 66. F Test
  • 67. F Test
  • 68. Chi-square
  • 69. Chi-squareChi-Square Test: England, Australia, Pakistan, SrilankaExpected counts are printed below observed countsChi-Square contributions are printed below expected counts England Australia Pakistan Srilanka Total 1 72 82 130 95 379 85.92 96.82 135.20 61.06 2.255 2.269 0.200 18.867 2 125 140 180 45 490 111.08 125.18 174.80 78.94 1.744 1.755 0.155 14.593Total 197 222 310 140 869Chi-Sq = 41.838, DF = 3, P-Value = 0.000
  • 70. Thank You