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  • ALL TIME GB TOP SCORERS

     

    Name

    Score (%)

    City/Year

     
     

    Purvi Gupta

    100

    Del 2019

     

    Bhawana Sethi

    100

    Del 2015

     

    Adyan Prabhakaran

    100

    Hyd 2014

     

    Thirumoorthi.M

    99

    Chn 2019

     

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    99

    Mum 2019

     

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    Kol 2017

     

    Vidula Valavalkar

    99

    Hyd 2014

     

    Vishal Tillu

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    Mum 2014

     

    Yashwanth J G

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    Bng 2013

     

    Jyothi Kanuri

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    Hyd 2013

     

    Vrajesh Parekh

    99

    Mum 2013

     

    Gnanasekaran D

    99

    Chn 2012

     

    Benoy Ramachandran

    99

    Chn 2012

     

     

    Muthu Naveen S

    99

    Mum 2012

     

    Ketan Trivedi

    99

    Mum 2012

     

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    Devendra Singh Baghel

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    Varun Hemrajani

    99

    Pun 2010

     
         

    Here is the complete list of all time Lean Six Sigma Green Belt Top Scorers

  • ALL TIME BB TOP SCORERS

     

    Name

    Score (%)

    City/Year

     
     

    Kunal Obhrai

    98

    Del 2019

     

    Mahesh P K

    98

    Bng 2017

     

    Balaji M

    97

    Bng 2017

     

    Rohit Arora

    97

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    Amit Kumar Makkar

    97

    Del 2015

     

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    Del 2013

     

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    Hyd 2011

     

    Ashish Sharma

    96

    Pun 2019

     

    Sunil M. Bhat

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    Del 2017

     

    Apoorve Arya

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    Kumar Kaushal

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    Del 2019

     

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    95

    Hyd 2019

     

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    95

    Mum 2019

     

    Nithin Sandhyala

    95

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    Abhishek Arora

    95

    Del 2017

     

    Satishkumar Jain

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    95

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    95

    Pun 2017

     

    Rupinder Kaur Narang

    95

    Del 2016

     

    S Sujay Kumar

    95

    Mum 2016

     

    Kuljinder Kaur

    95

    Del 2015

     

    Vetrivendhan K P

    95

    Bng 2014

     

    Sunil Bissa

    95

    Chn 2013

     

    Mayank Gupta

    95

    Pun 2011

     

    Here is the complete list of all time Lean Six Sigma Black Belt Top Scorers

  • Posts

    • Very interesting answers to an equally interesting topic    There are two winners for this question - Johanan Collins and Sai Kotari. Johanan for the shear vastness of information provided with respect to  ANOVA and Sai for highlighting various related concepts like within and between variations plus confidence intervals.   Well done!
    • In many situations , we have to compare central tendencies(generally mean is compared) of multiple samples. For 2 sample, t test is widely used. We can use Z test for large sample size. ANOVA (Analysis of variance) is used in cases when we have to compare more than 2 means. Though we can use multiple t test but it will be time consuming and less efficient. ANOVA is a parametric test and requires various assumption for the populations samples to be compared. This includes assumption of Normality, Independence and approximately equal variance. ANOVA is not used in the Nominal data . Below are the steps to perform the ANOVA: Calculate the mean of all the samples Define the null and alternate hypothesis (e.g.: Ho= All samples have equal mean, Ha= All Sample means are not equal) Perform calculation to get the Sum of squares and mean squares based on degrees of freedom for within and between samples differences. Calculate the F statistic. Look up statistical Table , Compare the F statistic with the tabular values and conclude on the results.   ANOVA is a parametric test and hence can not be used in all situations. We can use Kruskal Wallis test which is the non parametric alternative to the One Way ANOVA. It compares the sum of ranks in the samples instead of mean.  
    • Analysis of variance (ANOVA) is used to assess the differences between means of 2 or more groups. It is a statistical hypothesis test to determine whether the means of at least two populations are different. Conditions for using ANOVA are: ·         a continuous dependent variable (Y) ad and a discrete independent variable (X) ·         should be a normal distribution. ·         Samples must be independent. ·         Population variances must be equal ·         Groups must have equal sample sizes.   Hypothesis: Ho: μ1 = μ2 = μ3 =… μn Ha: μ1 ≠ μn   A significant P value implies a low probability that the mean values for all groups are equal; it only tests for an overall difference between groups. Once the overall significance is arrived at, then we can use multiple comparison procedures for individual group comparisons.   The right AVOVA test to perform can be decided basis the number of independent variables that are included in the ANOVA test A.      One-way means the analysis of variance has one independent variable. Example: AHT of staff at different experience levels like <6 months and > 6 months B.      Two-way means the test has two independent variables (which can have multiple levels), Example, For Jam sales/week, independent variables of brand of Jams and how many calories it has. Another example: AHT of staff at different experience levels like 3-6 months, 6-12 and >12 months for Different experience background like Insurance, Banking, etc. o   The results from a Two- Way ANOVA will calculate a main effect and an interaction effect. o   The main effect is similar to a One -Way ANOVA: each factor’s effect is considered separately and with the interaction effect, all factors are considered at the same time. C.      MANOVA (multivariate analysis of variant) is another form of ANOVA for several dependent variables. For example, AHT for Task 1 for different Tenure groups/Experience background and AHT for Task 2 as well. Possibly to assess if the combination has any adverse impact on speed of Task 2. It tests multiple dependent variables at the same time by testing: a.      changes to the independent variables have statistically significant effects on dependent variables b.      interactions among dependent variables c.       interactions among independent variables For data that is not Normally distributed, we can use a non-parametric, analogue of one-way ANOVA (called Kruskal-Wallis ANOVA)
    • Q 440. Rootogram is a modified version of the good old histogram. What are the advantages of working with a rootogram? Explain using an example.   Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday.  When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting.  The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term
    • Parametric tests to compare variances for more than two populations are given below One Independent Variable – 1-Way ANOVA (Completely Randomized Design). Subjects are assigned randomly to treatments. This design contains only one independent variable with one or more classifications or treatment levels. In this design, the Total Sum of Squares (the variation in the data - SST) is divided into the variance from the treatment (Between Group Variation i.e the columns - SSC) and the error variance (unexplained by the treatment, i.e. within-group variation.  SSE). ANOVA compares the relative size of these two variances using the F Statistic which is the ratio between the two variances. SST = SSC + SSE Assumptions for ANOVA. Data is randomly drawn from a normally distributed population and the variance of the populations is equal. Use of Confidence Intervals.  In the event of the results being significant (p-value<Alpha) the researcher needs to do a post hoc or posteriori test to determine which group is statistically significant from the rest. In order to determine this the Confidence Intervals are plotted and significance is determined by examining the overlap between the confidence intervals. In addition, Tukey’s Honestly Significant Difference (HSD) test for Equal Sample Sizes, or the Tukey-Kramer Procedure for Unequal Sample Sizes. Example. The sample consist of the pipe diameter of 4 operators. The p-value < 0.05 indicates that one or more one operator is statistically significantly different from the other operators. Examining the 95% Confidence Interval of the Operators, it can be observed that the Confidence Interval of Operator 3 does not Overlap with other Operators. This is also evident from the Fisher Individual 95% Cis and Tukey Simultaneous 95% CIs and the Box Plot. Thus, we can conclude that Operator 3 is significantly different from the other 3 Operators.   Further, the Fisher Individual 95% Cis and Tukey Simultaneous 95% CIs have also bet the Operator 3 is a separate group from the remaining 3 Operators. One Independent Variable + 1 Block Variable – Randomized Block Design. The Randomized Block Design is similar to the Completely Randomized Design; however, it has a second variable, referred to as the Blocking Variable that is used to control for confounded or concomitant variables. These variables are not controlled by the researcher but affect the outcome and hence need to be blocked. The SSE (Error Sum of Squares) is segregated into SSR (Sum of Squares Blocks) and SSE (New Error Sum of Squares) SST = SSC + SSR + SSE Repeated Measure Design is a randomized block design in which each block level is an individual item/person and that person/item is measured across all treatments. It helps researchers to determine if the means of three or more measures from the same person are similar or different. The repeated measure ANOVA controls the between-subjects variance by removing it from the error term and measuring it separately.  ANOVA for Latin Square Design The Latin Square Design utilizes the Blocking principle. It is used to remove two nuisance sources of variability which are in the rows and columns of the square. The rows, and columns are the two restrictions on randomization. The model is totally additive, in that there is no interaction between the treatment, rows and columns. The Analysis of Variance divides the total sum of squares into the sum of squares for the rows, columns, treatment, and error. The F test ratio of MS(Treatment)/MS(Errors) is used to determine if there is no difference in the treatment means. (MS – Mean Sum of Squares) Randomized Incomplete Block Design. When it is not possible to run all the treatment combinations in each block, the randomized incomplete block design is used. When all treatments comparisons are equally important, the Balanced Incomplete Block Design is used. This design ensures that each block is selected in a balanced manner so that any pair of treatments are selected the same number of times as any other pair.   Two Independent Variables – Two-Way ANOVA In this design, two or more independent variables are explored at one time. These are also called factorial designs. In this design, each and every level is studied under the conditions of every level of all other treatments. The design can have 3,4, …, n independent variables being studied at one time. For example, the independent variables can be machines, operators, shifts, day of the week, suppliers, and raw materials. All of these independent variables can be done in one study. This will be a Six-Way ANOVA. The Completely Randomized Design, the Randomized Block Design, and the n-Way ANOVA all have one dependent variable. Chi-Square Goodness of Fit Test. This test is used to analyze the probabilities of multinomial distribution trials along a single direction. For example, to study Education with 4 possible outcomes, viz. illiterate, Primary School, Secondary School, Bachelors's and above, the single dimension is Education and the possible outcomes are the levels of education. Also, it is imperative that on one trial only one outcome can occur. The Chi-Square Goodness of Fit test measures the difference between the observed frequencies and expected frequencies. The Chi-Square Distribution is used to measure the significance. It is a one-tailed test since Chi-Square of Zero means the perfect agreement between observed and expected values. Chi-Square – Test of Independence. The Goodness of Fit test cannot be used to measure the analyse two variables at one time. For this purpose, the Test of Independence is done. Test of independence can analyse the frequencies of 2 variables with multiple categories to determine if the two are independent. Use Case. To determine if the type of pizza topping is independent of the customer's age or the citizenship is independent of investment in bitcoin. Non-Parametric tests to compare variances for more than two populations are given below One Independent Variable – Kruskal Wallis Test. This test is the nonparametric equivalent to the one-way ANOVA. It is used to check if 3 or more samples come from the same/similar or different populations. It can be used for Ordinal Data and is not based on the shape of the population distribution. It assumes that the groups are independent of each other and random selection of items in the groups.    The Kruskal Wallis Test for the above data shows a p-value of < 0.05 indicating that one of the operators is statistically significant from the others. Examination of the results shows that Operator 3 is significantly different from other operators. One Independent Variable – Mood’s Median Test. It is used instead of the Kruskal Wallis test when there are outliers present in the data. The results from the Mood’s Median Test indicate a p-value of 0.003 with is significant with an Alpha of 0.05. Confidence Interval Mood’s Median Test. On observing the 95% Midian Confidence Interval of the three operators, it can be seen that the CI of Operator 3 is distinct from the other operators. One Independent Variable + 1 Block Variable – Friedman Test This is the nonparametric equivalent to the randomized block design. If the normality of the data cannot be assumed or the data is ranked the Friedman test is used. It assumes independent blocks; no interaction is present between blocks and treatments, and the observations within each block can be ranked. In the Friedman Test below, the Day of the Week is Blocked. The p-value of 0.007 < Alpha of 0.05 indicates that one of the operators is statistically significantly different. On observation, it can be seen that Operator 3 is different from the rest of the Operators. Multivariate Analysis of Variance (MANOVA). This test is used when there are more than one continuous Dependent Variables. A one-way MANOVA would examine the effect of one independent variable on two dependent variables. For example, to study the effect of education on income and expenditure. A two-way MANOVA would examine the effect of two independent variables on two dependent variables. For example, to study the effect of education and citizenship on income and expenditure. Multivariate Analysis of Covariance (MANCOVA). This test includes covariates. It involves finding out the statistical significance between multiple dependent variables (continuous) and an independent (grouping) variable and controlling with additional variables called covariates. Covariates reduce the error term hence the effect of the covariate is removed from the relationship of the independent and dependent variables.   References https://medium.com/nerd-for-tech/everything-about-manova-and-mancova-4c1c237af464
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