# Kavitha Sundar

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## Profile Information

• Name
Kavitha S
• Company
Omega Healthcare Management Services Limited
• Designation
Manager - opex

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2. ## Statistical Significance

Question: What is the meaning of statistically significant difference? What some of the most important ways to utilize this concept in problem solving and decision making? Definition: A statistically significant is usually means statistically significant difference. It is defined as the chance of existence of relationship between two or more variables is because of some other cause but not definitely by the random cause. The hypothesis tests are used to prove the relationship using various tests. It is interpreted by P Value. In general, when a p value is less than or = 5%, it is called statistically significant. It does not mean that the finding is important to consider or the decision making taken is reliable. For Eg. 5000 coders are given enough training to check if there is any significant difference between male and female coder’s test scores. Lets say Mean for male is 97 and female is 99. We can use t test to compare the independent groups at .01 level of significance. The difference calculated is a very small difference. To say, it not even important as it is derived out of samples. It is because when u have the larger group for study, the difference would be smaller, which means the result is real not by any chances. Significance means Statistically it is important that the relationship exists between 2/more variables. P- Value: P-value is the level of marginal difference that it represents the chances / likelihood of the occurrence of a ny given event. When te p value is small it means there is a strong relationship / evidence in relation to alternative hypothesis. One-Tailed and Two-Tailed Significance Tests This is part of significant difference. It is important the hypothesis stated is of one tailed or two tailed. If it talks abnout the direction of relationship, it is one tailed probability. This is used to compare the group in one direction. Null hypothesis predicts the direction of the chance fixed. For Eg. Males are generally stronger then females. Female coders score higher than male coders. A two tailed would be stating its null hypothesis In the following way. Eg. Males and females are equal. Null hypothesis states there is no significant difference. Procedure Used to Test for Significance 1. Decide on the alpha value. 2. Conduct the study 3. Calculate the sample statistic 4. Compare the critical value obtained. Thanks Kavitha
3. ## Process Stability, Process Capability

Question: Differentiate between a stable process and a capable process. Is Process Stability supposed to be a pre-requisite for all type of processes? Explain with appropriate examples. Process stability: means consistently producing the output in the process over time. If a process is consistent over time & distribution of the data is within the control limits. then we call this process as “Stable within control”. If there is a shift from the mean but the data points fall into the control limits set is called “Stable – out of control”. This requires attention since there is a common cause variation. Process Capability – means a measure of the process and how capable the process is in maintaining the customer’s needs / expectations. It explains us how good or how bad the output is. Process capability is measured and represented in “Cp, Cpk, Pp, Ppk”. If the process is stable but not capable, the customers will still be satisfied but not pleased/delighted. Eg. A customer requires pant size of 40 for his usage. Also he says anything that fits him between 38 & 42 would be okay. In this case, the store consistently supplies him the size of 39. The customer is happy but not delighted. When the supply is accurate the customer would be delighted. Here the process is stable but not capable in meeting the customer’s expectation. You should always concentrate on a target to delight the customer and not on the range though given by them. Process stability Process Capability Definition Predictable output consistently. How well a process behaves to produce the output in future. Stability has nothing to do with capability Meet customer's expectation at all times.(100%) It compares the process performance agaings tehe specifications given by client. Capability has nothing to do with stability. Both has inherent relationship. Central tendancy measures Constant mean and constant variance is required to say a process is stable. Range and target is provided by the client. Limits Control limits(UCL & LCL ) are in place Spec limits (USL & LSL) are in place. Usage of variations Constant random variations / controlled variation is exhibited by a stable process Controlled and uncontrolled variations can be seen in the process. Grphical tool used Control chart / Scatter plot Histogram Interpretation A random fluctuation around the constant mean over a period of time is said to be the stable process. When the pattern is seen and variation is uncontrolled, though it falls within the control limits, the process is not stable. It is always important that we require controlled variation, and constant mean. if the histogram falls within the specification limits, the process is capable. If the variation is too high when mean is shifted below the target, then the process is incapable. Distribution Constant distribution is required to say the process is stable. Normal distribution is required to say process is capable. Order of stability / Capability Stability comes first only when stability and normality of the process is tested, a process capability is tested. Measurement index Root cause for variation would be identified. Cp, Cpk, Pp and Ppk is calculated. Usage of Cp & Cpk, Pp & ppk Use Cp, Cpk for samples and Pp & Ppk for population to arrive at the capability index. Definitions Cp= Process Capability. A measure for a centered process Cpk= Process Capability Index. Adjustment of Cp for the off centered process. Pp= Process Performance. A measure of process performance for the centered process. Ppk= Process Performance Index. Adjustment of Pp for the off centered process. Cp – Capability index will tell you how fit the data are into the USL & LSL. Also make sure the process is well centered around the average. If the process is centered – then Cp is equal to Cpk. If not centered, then Cp > Cpk. When the Cp & Cpk is > or = 1 the nthe process is capable. Cp & Cpk comparison to sigma Cp and Cpk of: · 1.00 is equal to 3 Sigma · 1.33 is equal to 4 Sigma · 1.67 is equal to 5 Sigma · 2.00 is equal to 6 Sigma . Is Process Stability supposed to be a pre-requisite for all type of processes? Deming has quoted” Only when the process is stable, the process is capable of producing output”. This means the capability cant be checked before studying the stability and normality. The order is like this… Study stability, normality and then capability of any given process. A stable process is always a prerequisite for all processes to meet customer’s expectation or calculating Process capability, because a process can’t be capable if the process is out of control. In a DMAIC process , a BB should always check 1. Cehck if the process is stable or not simply by using the control charts. 2. Check if the data is normal or non normal to calculate the capability. If the process is not stable , then start focusing into the root cause. Try to eliminate the roort causes identified from the control chart, make the process stable. Make sure the data is normal. Then calculate the capability. Remember if the process is out of control, then no use in calculating capability. Because capability depends on the data where the process happened when the data is collected. Always remember root cause identified is eliminated ,but not to improve the process, to get the process where it belongs to. Dr. Deming used to use a very simple analogy in his seminars: “If this building catches on fire, we must put out the fire. Putting out the fire does not improve the building. All it does is get the building back to where it should have been all along – no fires!” For Eg. When we apply for the credit card, the agent tells us that we would receive the kit within 7 working days. But here customer expects a shorter delivery. Though there are aware of the process, they expect the shortest. Anything that exceeds the time period mentioned by the agent, will annoy the customer. There the process is said to be unstable and the variation would be high. In such cases, the customer makes many phone calls to the agent ./ bank to check the status. This involves cost, time and human intervention. Also, this makes the business weaker sinceword of mouth is the best method to increase sales pitch in the bank. It ruins the sales of the company. This variation is to be identified and eliminated inorder to satisfy the customer, and put back the process where it had been / where it should be to improve this process. Conclusion: Then process has to be stable , the data is to be normal and then the process is calculated whether it is capable to meet the customer needs. Any process which is unstable can’t be capable to meet the customer’s expectation. In simple words thus, stability and capability need to be treated hand in hand in terms of interpretations, but at all times, the word stable needs to come before saying the word capable. thanks Kavitha

7. ## Continuous Data, Attribute Data

Question 4 in Episode 2: While continuous data is measured and attribute data is counted, there is sometimes confusion if some specific dataset should be considered continuous or attribute. Provide some examples of confusing datasets and your inference. Data – is defined as a collection of avalues / useful information that is required for any analysis to the receipient. Data is genereally used to prove / disprove hypothesis. Data is of two types basis statistics. It is Quantitative or Qualitative. Quantitative is descriptive data, which can be categorized into subgroups for analysis and qualitative is numerical which means either measurable / countable. Qualitative data is again divided into 2 types continuous and discrete data. For Eg. Charlie chaplin is fair, short, has small mustache, thin built and wears black colored jacket. – it is qualitative data. Charlie chaplin has one hat, one walking stick and 2 legs. – it is Quantitative –discrete data. Charlie chaplin aged 45 years is 57.2 kgs built and 4.8 inches tall . – it is quantitative continuous data. 4 types of measurement scales: It is divided into four categories – Nominal and ordinal, interval and ratio Ø Nominal data: It assigns a numerical value as an attribute to any object / animal / person / any non-numerical data. Ø Ordinal data: Any data which can be ordered and ranked is called ordinal data. This can’t be measured. Eg. 1. A horse is numbered in the race court, represents the nominal data. 2. The numbered winning horses are ordered and ranked as “1st, 2nd and 3rd place” in race club, which represents ordinal data. Another best examples is progress report of the student. Ø Interval: It is a numeric scale where we know order as well as the differences between values. There is no origin. Eg. Temperature of the room is set to be normal if it is between 25 and 28 degrees C. Time is another good example of an interval scale in which the increments are known, consistent, and measurable. Ø Ratio: Ratio scales are the ultimate nirvana when it comes to measurement scales because they tell us about the order, they tell us the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied. At the risk of repeating myself, everything above about interval data applies to ratio scales + ratio scales have a clear definition of zero. Good examples of ratio variables include height and weight. Qualitative data: It is otherwise called as categorical data. Quantitative data: It is divided into two contionus and discrete data. Difference between Continuous and discrete data: Continuous data Discrete data It is measureable on a scale It is countable The data falls within finite or infinite range The data has only finite numbers. Can be broken into subcategories Can't be broken since it is a whole number. The frequency is depicted in histogram, where skewness is shown clearly the values take a distinct value hence it is represented in bar diagram, skewness can't be seen. Values are allowed to group within the range The values are individual values. Eg. Temperature of the person, Height, Weight, Age, time, Cycle time taken to complete a task Eg. No. of cumputeers, No. of students, no. of books, no. of certificates, no. of errors, etc Confusion between Contionus and Discrete data: Eg. 1: Person Age Weight (Kgs) Height(Inches) Color Ajay 34 51 5.1 Wheatish Sharma 35 65.5 5.2 Fair Roshini 23 45.5 4.8 Wheatish Gaithri 53 72.5 4.8 Dark Linda 43 46.5 5.1 Fair Tanya 36 43 5.3 Wheatish Balu 27 56 5.6 Fair Vignesh 32 77 6.1 Dark Aarav 43 76 5.9 Wheatish Rithesh 45 64 5.3 Dark Qualitative data / categorical data: Categorize 10 people in the group into wheatish, dark, fair basis the color. This represents categorical data. Continuous data: Age , Height and weight of the people displayed above in the table depicts a good example of continuous data, where these numbers falls within the infinite ranges. Discrete data: No . of Wheatish – 4 No. of fair – 3 No. of dark – 3 Total no. of people – 10 Conclusion of Eg. 1: Though age is continuous numerical variables. Although the recorded ages have been truncated to whole numbers, the concept of age is continuous.) Number of aged people is a discrete numerical variable (a count). Age can be rounded down to a whole number, if so it represents the discrete data. Though it falls under discrete(when all data is shown as whole integers), it is actually a continuous data because it has ranges. Age is not a constant factor, though the DOB is constant. Basis the context / concept of the requirement – lets say to fill a form, the exact age is required. In such case, though age is discrete, it is continuous. “12 years, 153 days” really means a continuous age that is between 12Y152.5D and 12Y153.5D.” Eg. 2 : Income is another example of continuous data. Eg. 3: “ In practice, percentage data are often treated as continuous because thepercentage can take on any value along the continuum from zero to 100%. In addition, dividing a percentage point into two or more parts still makes sense.Discrete data are easy to collect and interpret. % is always to be considered as continuous but it depends on the concept. If I have to track the error percentage, the right metric is as below.. Error % = No of errors (Discrete) Total charts audited.(Discrete) Hence Error % is discrete. Another example: If I have to track the availability of the machine, the formula is as follows… Availability % = Total hours available (Continuous) / Expected hours of production for 8 hours(Continuous) Hence Availability % is continuous, since time is continuous. Conclusion: It depends…. In certain situations, discrete data may take on characteristics of continuous data. But, if counts are large, distribution of values are relatively wide, and the the values are distributed across the values, you can “pretend” it is continuous and use the appropriate tools. Thanks Kavitha
8. ## Correction, Corrective Action and Preventive Action

Descriptions Correction Corrective action Preventive action Definitions Action taken to eliminate the defect identified. Action performed to identify & eliminate the root cause to prevent the recurrence of the defect. Action taken to prevent the potential / suspectable causes of the defects and its occurences Waste Classification - Rework Rework is required Fix the bug to prevent its recurrence to avoid rework, investing time and human resources to work on the root cause Proactive measure taken inorder to avoid the potential causes of defects / rework Focus on Current issues / problems Current issues / Problems Proactive ameasures to Futuristic problems Situations where we deploy what When the product is unused or defect identified before it reaches the customer The product that does not meet the customer expectation after it reaches the customer. This situation is to be considered serious. 1. Potential defects / errrors 2. When we want to improve the processes by creating a defect free / zero error environment ( Also called developmental action) Frequency of the defect / problem once in a while Very often but after the shipment Since it is a proactive measure, the defect is yet to occur in this case. Tools used - Why why analysis, Fish bone analysis, Process flows, Is/Is not FMEA, Control impact matrix Type of solution Immediate short term solution Long term solution Procedure Fix it immediately 1. Identify the defect / error 2. Fix the bug first. 3. Analyse & Identify the root cause 4. Find & Implement the solution 5. Document and create the control to sustain the improvements. 1. List all potential failures 2. Create control for all failurs identified 3. Implement solutions 4. Document 5. Create control plan and if required, Reengineer the process & Document. Examples 1. Quality of the chart with error % of 10%, wheras the acceptable is 5% Once in a while occurred, but corrected the chart during an audit and billed to client Identify the root cause - Coder is not aware of the concept since it was a new update. Hence corrective action of training was taken to all the coders to prevent its recurrences. Rule based tool for computer assisted coding tool is implemented with coding updates, sothat proactively the errors are arrested while coding itself and the errors are rectified before it goes out to the client. Example 2 . A government regulated shut down restaurant in terms of violation of rules and regulations of sanitary practices Immediate correction of adapting the policies provided Requirement to separate work areas, establish acceptable processes and perform cleaning tasks by creating the check lists, display the process steps in the kitchen area for all to adapt to rules. A automated machineries to perform the actions listed. A regualr trainings & frequent audits to confirm the rules are adhered to. Conclusion: Are there situations where both preventive action and corrective action are undesirable and correction is the only preferred action? There are no such situations that only correction is preferred and CAPA is not required. Any organisation who wants to delight the customer would rather opt CAPA instead of correction. A immediate fix would not be a permanent solution to in the high tech environment. Hence CAPA is required to delight the customer and Correction is required if defect is identified and to satisfy the customer. Thanks Kavitha
9. ## Check sheet

Check sheet is a document / form that is used to collect the data in a real situation, where the data is created. The check sheet collects both quantitative and qualitative data. It is a structured easiest way of data collection process. A properly designed check sheet will have answers to 5 “W” – what, when, why, where and Who. A check sheet is a very useful process improvement and problem solving tool. 7 QC tools was designed by Ishikawa. When he had improve his processes, he gave a lecture to his engineers on the statistical tools and techniques which were used for problem solving. Later he realized that the statistics are way high to understand and implement. Then he implemented his thoughts and created 7 basic quality tools which will help the team to identify the problem from the workflow of process using process flow, collect relevant data using check sheet and stratification, categorize them using cause & effect matrix / fish bone, analyses and find a solution using pareto, histogram to understand the magnitude of the problem and scatter diagram, implement solution and create control plan. Check sheets are of 5 types – Process check sheets, defect by location check sheet, defect check sheet, stratified defect check sheet and cause & Effect diagram check sheet. Check Sheet Procedure 1. Decide what to observe / record 2. Decide how long the data to be observed 3. Design the form and label it accordingly 4. Trial it for short period to make sure if the data is sufficient to perform data analyses. 5. Keep that as a reference for future data collection process. Check sheets are also called as defect concentration diagram since it helps to collect the data that is defect. Ex. For the data collection process. Date Reason 10/4/2017 10/5/2017 10/6/2017 10/7/2017 Incorrect coding III III II I Query error IIII IIII III II Typo error II I II I Conclusion: The Check sheet is required for education of quality as 7 QC tools. It depends on the data collection process to modify or have the predefined form.