Minitab Training Manual Part II - Force Motors

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www.forcemotors.com Name of the Presentation I dd.mm.yyyy Minitab Training Manual – Part II 04.05.2021

Transcript of Minitab Training Manual Part II - Force Motors

Page 1: Minitab Training Manual Part II - Force Motors

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Minitab Training Manual – Part II

04.05.2021

Page 2: Minitab Training Manual Part II - Force Motors

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Prepared By : Subhash Sirsat

'Shriram Kulkarni

Minitab®

Practice Session Module-II

Supported By: Makarand Kanade

Sr. VP : ( Corporate Quality , R & D)

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Histogram

Minitab®

Practice Session Module-II

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Hypothesis Testing X-Attribute

Y is Variable Y is attribute

Variation -σ Average-σ Proportion -P

One to Standard 1 – Variance test 1 –t Test 1-P test

One to one

2- Variance test (F test- Normal distribution) , Levens test (for Non normal test)

2- t Test 2- P test

One to many

Equal variance test a) Bartlett’s test (for Normal data)

b) Levens test (for non

normal data)

1 Way Anova ( if variances are Equal)

Chi- Square Test

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Hypothesis Testing :Key concepts

Purpose of hypothesis testing is not to question calculated values , but to make judgment about the difference in

the values of two data sets.

Next step , is to write hypothesis : null hypothesis is a statement of innocence. Says , there is no difference of

in the values of two data sets.

“alpha” is called significance level for hypothesis testing ( generally 0.05)

(1-alpha) is called confidence level for hypothesis testing (generally 95%)

We need a certain minimum confidence level for hypothesis testing

e.g : Ho : E (A) = E(B) “ Efficiencies of both coaches A & B are same.

Alternate Hypothesis : Is statement of guilty for the above null hypothesis

We have 3 possible alternative hypothesis.

H1: E(A) < E(B) ( efficiency of A is less than B) one tailed (a=0.05)

H1: E(A) >E(B) ( Efficiency of A is greater than B)one tailed (a=0.05)

H1: E(A) not equal to E(B) two tailed (a=0.025)

H0 : Null Hypothesis

H1: Alternative hypothesis

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It works very much like a court case

When we have a suspect ; we have to take a decision whether he / She is innocent or

guilty .

Null Hypothesis is statement of innocence (Ho)

A accused is said to be innocent unless proved guilty

We have collect sufficient proofs to take decision about the innocence .

Null hypothesis is statement of No change or No Difference.

Alternative hypothesis challenges Null hypothesis; If Null hypothesis is proven wrong

alternative hypothesis must be right.

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Hypothesis Testing :Key concepts

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Types of Errors : Type – I Error :- To react on outcome as it comes from special cause , when it actually comes from common cause of variation ( also called alpha error ) “over control = tampering=over adjustment ” . When variation due to common cause , changing the process thinking that it is due to abnormality and adjusting the process , increases the variation . Type II Error : - also called beta error . To react to an outcome , when actually it comes from special cause . Treating special cause , as a common cause . Not doing analysis , even in the presence of special cause leads to unstable process which is unpredictable and increased

variations due to presence of abnormality .

CORRECT DECISION

( CONFIDENCE : 1 - alpha) Error ( TYPE – II )

( Beta Error) : 10 %

ERROR ( TYPE – I )

( Alpha Error ): 5 % CORRECT DECISION

(Power : 1- beta)

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Hypothesis Risk P Value Conclusions

H0 Ha A <0.05

We conclude that there is

strong evidence for us to

Reject Null Hypotheses &

Accept Alternative

hypothesis

H0 Ha B >/=0.05

We have not proved that null

hypothesis is true

We didn’t have enough

evidence to reject H0

We typically will either collect

more data or accept null

hypothesis by default

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Hypothesis Testing : Drawing Conclusion

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General Rule

P Low , Null Go

P High , Null is Guy

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Used to compare standard Deviation of data with target value of Standard Deviation (one to Standard)

Hypothesis Statement

H0: б=target б

Ha: б < target б

: б > target б

: б≠ target б

Example: Ho: бv > 0.003

Ha : бv< 0.003

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One Variance Test

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Click here on “ hypothesis testing”

Step -1

One Variance Test

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Click here on “ 1 –Sample standard Deviation ”

Step -2

One Variance Test

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Select data from column

Put target value of Std Deviation

Select Hypothesis

Step -3

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Since p value is less than 0.05 we can conclude with 95 % confidence that std deviation of bore dia is less than

0.003

Output

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Graphical representation of data

Output

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Report card: Checks data integrity ;sample size, & test validity

Output

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This test is used to compare standard Deviation of two data .

Hypothesis Statement

Ho :б1 = б2 Null Hypothesis

Ha : б1< б2 Alternate hypothesis

: б1> б2

: б1 ≠б1

Example: Comparing variation in samples of two suppliers 1) Vikas Industries 2) Jai Bhavani Mata.

Ho : бVikas</=бJai Bhavani

Ha: бVikas >бJai Bhavani

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Two Variance Test

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Select data in 2 columns

Step -1

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Select -2 variance test

Step -2

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Compare std dev. Of Vikas Ind. & Jay Bhavani

Mata Ind.

Select Hypothesis

Click “OK ”

Step -3

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Conclusion : Since p values is more than 0.05 we can not conclude that std

deviation of Vikas is greater than Std Deviation of Jai Bhavani Mata.

Output

Graphical representation of data

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Graphical representation of data

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Conclusion: for Unusual data check one point ; check reason for unusual data.

Output

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Equal Variance test is used for comparing more than two standard deviations For Example : Comparing variations in 4 machines. Ho : бM/C1=бM/C2=бM/C3= бM/C 4 Ha: At least one б is different

Equal Variance Test

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Select “ hypothesis test”

Step -1

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Select “standard deviation test ”

Step -2

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Select data in required coloumns

Step -3

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Conclusion: Since P value is more than 0.05 there is no significant different in All machines б

CI for 4 Standard Deviation

Output

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Output

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Note : Look at, standard deviations of the M/c- 1 , M/c- 2 , M/c. - 3 & M/c – 4 CI for all 4 data are almost same

Output

Graphical representation of data

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One sample t test is used to compare mean of data with Standard. Example : Average height of Indian Men is less than 165 cm . In this Example we want to check Whether Mean Oil pump pressure is greater than 8 Bar Ho: цp </= 8 Ha: цp > 8

1 Sample Test

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Click “Here ”

Data from Example

Step -1

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Enter value of rows

Enter here “ mean value ”

Click “ hypothesis statement ” which is to be tested

Step -2

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Since P value is greater than 0.05 we can not conclude that mean oil pump pressure is grater than 8 bar

Output

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Output

Graphical representation of data

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Output

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2 Sample- t test is used to compare means ( Averages) of two data. Example: Average Surface finish of Vikas Ind. parts is greater than Average Surface finish of JBMI parts. Hypothesis Statement Ho : цv < цj Ha: цv > цj

2 Sample Test

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Select “Here ”Hypothesis test

Step -1

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Click “ 2 sample T test”

Step -2

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Select “ hypothesis statement, you want to test”

Step -3

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Conclusion: Since P value is less than 0.05 with 95% confidence we can conclude that Surface finish Vikas is greater than JBMI

Output

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One way ANOVA is used to compare means of more than 2 samples Example : Compare Mean Bore Sizes of 3 machines A ,B,C Hypothesis Statement : Ho : all means are Equal Ha : At least one mean is different.

One Way ANOVA Test

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Select Hypothesis Testing

Step -1

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Select one way ANOVA

Step -2

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Select Values in Separate columns

Step -3

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Conclusion : Since P value is less than 0.05 we can conclude that means size of Machines are not same . Mean of Machine “A “is different than “B” & “C “

Output

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Output

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Since CI of all three mean are not same we can conclude all means are statistically different

Output

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Output

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1 P test is used to compare percentage with respect to Standard value Example: Whether Rejection % in Castings is less than 7 %

1P Test

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Parts with dents

type of defect Quantity Checked parts defective for

dents Target

Painting defect 5500 15 0.5 % rejection

Hypothesis Statement : Rejection % is less than 0.5% Ho: % Rej >/= 0.5 Ha : % Rej </= 0.5

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Please enter data in 1 P test format

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Output

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Output

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Output

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Conclusion : Since P value is more than 0.05 we can not conclude that rejection % is less than 0.5%

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2-P test is used to compare % of two data

Example: % of Rejection Before improvement

% of Rejection after improvement

2P Test

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Check whether REJECTION % after taking action is significant

Sr # Rejection / total

production %

1 Rejection after taking action 7 /170 4.12%

2 Rejection before taking action 11/105 10.47%

Ho: Rejection % after action >/= Rejection before action Ha: Rejection % after action < Rejection before action

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Select “ Hypothesis testing”

Step -1

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Select “ 2 S” % defective Hypothesis testing

Step -2

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Enter “ Rejection % before improvement”

Enter “ Rejection % after improvement”

Click “ OK”

Step -3

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Conclusion : Since P value is less than 0.05 . We can conclude that % rejection before improvement is significantly greater than after improvement “

Output

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Output

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Output

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Chi – Square test is used to compare more two % defective test

Ho: All % Rejections are same

Ha: At least one % Rejection is different

CHI-Square Test

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Lot Qty Defective parts

500 15

400 20

2000 25

450 12

Data for test

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Select Hypothesis testing

Step -1

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Select Chi- square test % defective

Step -2

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Fill table for total no tested & No of defectives

Step -3

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Since P values is less than 0.05 with 95% we can conclude that difference among % defective are significant

Output

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Output

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Thank You

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