Minitab Training Manual Part II - Force Motors

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Minitab Training Manual – Part II

04.05.2021

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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)

Histogram

Minitab®

Practice Session Module-II

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

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

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.

Hypothesis Testing :Key concepts

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)

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

Hypothesis Testing : Drawing Conclusion

General Rule

P Low , Null Go

P High , Null is Guy

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

One Variance Test

Click here on “ hypothesis testing”

Step -1

One Variance Test

Click here on “ 1 –Sample standard Deviation ”

Step -2

One Variance Test

Select data from column

Put target value of Std Deviation

Select Hypothesis

Step -3

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

Output

Graphical representation of data

Output

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

Output

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

Two Variance Test

Select data in 2 columns

Step -1

Select -2 variance test

Step -2

Compare std dev. Of Vikas Ind. & Jay Bhavani

Mata Ind.

Select Hypothesis

Click “OK ”

Step -3

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

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

Output

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

Select “ hypothesis test”

Step -1

Select “standard deviation test ”

Step -2

Select data in required coloumns

Step -3

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

CI for 4 Standard Deviation

Output

www.forcemotors.com Name of the Presentation I dd.mm.yyyy 29 FORCE MOTORS LTD PUNE

Output

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

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

Click “Here ”

Data from Example

Step -1

Enter value of rows

Enter here “ mean value ”

Click “ hypothesis statement ” which is to be tested

Step -2

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

Output

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

Select “Here ”Hypothesis test

Step -1

Click “ 2 sample T test”

Step -2

Select “ hypothesis statement, you want to test”

Step -3

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

Output

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

Select Hypothesis Testing

Step -1

Select one way ANOVA

Step -2

Select Values in Separate columns

Step -3

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

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

Output

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

1P Test

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

Please enter data in 1 P test format

Output

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

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

Example: % of Rejection Before improvement

% of Rejection after improvement

2P Test

www.forcemotors.com Name of the Presentation I dd.mm.yyyy 04/06/16 FORCE MOTORS LTD PUNE

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

Select “ Hypothesis testing”

Step -1

Select “ 2 S” % defective Hypothesis testing

Step -2

Enter “ Rejection % before improvement”

Enter “ Rejection % after improvement”

Click “ OK”

Step -3

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

Output

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

Lot Qty Defective parts

500 15

400 20

2000 25

450 12

Data for test

Select Hypothesis testing

Step -1

Select Chi- square test % defective

Step -2

Fill table for total no tested & No of defectives

Step -3

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

Output

Thank You

Minitab Training Manual Part II - Force Motors

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Transcript of Minitab Training Manual Part II - Force Motors

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