How can you perform a hypothesis testing on Minitab?

There are multiple hypothesis tests available in Minitab and should be utilized as per data type and the objective.

Let’s take an example of one sample t-test.

One sample t-test

One sample t-test is used to compare population mean with a defined value using sample data.

The test uses sample standard deviation to calculate population standard deviation. If there is a large difference between the sample mean and the specified test mean (given value), then the test concludes that it is highly unlikely that population means will be anywhere near test mean.

Assumptions

1. Sample data follows normal distribution

2. Sample data is random

Analysis in Minitab

Let’s assume sample data of average handling time:

To validate if the goal of reducing population mean to 4 minutes is significant, we can use one-sample t-test.

H0: Hypothesized Mean (4 min) = Population Mean (µ)

H1: Hypothesized Mean > Population Mean (µ)

Step 1: Check if data follows normal distribution to ensure we can use t-test.

To check do to Stat -> Basic Statistics -> Graphical Summary

If p-value is greater than 0.05, that means data follows the normal distribution.

Step 2: To conduct one sample t-test, go to Stat -> Basic Statistics -> 1-sample t.

By default, the alternate hypothesis for 1-sample test in Minitab is ‘Hypothesized Mean ≠ Population Mean (µ),’ to change, go to ‘options’ while performing the test on Minitab as shown below.

Minitab result interpretation:

The following illustration shows the typical result of the t-test:

Data Interpretation: As P is 0.000 i.e. less than significant level of 0.05, hence reject null hypothesis which means set target is significant.

Below shows the flow charts that can be used to select right hypothesis testing:

1. Continuous Normal Data (Parametric Test)

2. Continuous Non-Normal Data (Non-Parametric Test)

Hope this helps!

You can buy my book Lean Six Sigma: Process Improvement Using Minitab – Green Belt Certification on Kindle.

Book link: Amazon.com: Lean Six Sigma: Process Improvement Using Minitab – Green Belt Certification eBook: Shweta Ravi: Kindle Store

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Is doing safety courses like Nebosh and Iosh or quality management courses like Six Sigma a good option after studying mechanical engineering?

NEBOSH stands for “National Examination Board in Occupational Health and Safety. It is UK based independent examination board providing certification in health, Safety, and Environment. This course provides broader knowledge on risk management, health, and safety.

IOSH stands for “Institution of Occupational Safety and Health.” It’s professional health and safety membership forum, conducts training for managers, supervisor. This training will help managers to take appropriate action to handle practical situations.

Both NEBOSH and IOSH are relevant if you are looking for long term career in health and safety.

Now, let’s look at Six Sigma relevance.

Six Sigma is a data-driven methodology first introduced in the 1970s. Six Sigma is based on data analysis, hence helps in reducing risk which one of the many reasons that Six Sigma is still preferred over other improvement methodologies. The objective is to remove defects at the source and bring down performance to ‘six sigma’ level i.e. 3.4 defects for every one million opportunities. Continuous effort is made to achieve stable and predictable process.

Jack Welch made it the center of business strategy at General Electrical. In today’s competitive world, Six Sigma has widely accepted as a philosophy for growth across different sectors.

Six Sigma applies to all departments and industries. It has a very broad scope. So, professionals at all level are taking this course to improve day-to-day business and hence their performance.

Long answer short, my recommendation is you should go for Six Sigma course.

What is 5S?

5S is a lean tool used for organizing the workplace. 5S represents 5 Japanese words all starting with ‘S,’ Seiri (Sort), Seiton (Set in Order), Seiso (Shine), Seiketsu (Standardize), and Shitsuke (Sustain). It was invented in Japan to enable Just in time production. Unlike general norms, 5S has application in manufacturing as well as service industries

  1. Seiri mean Sort i.e. segregating what is needed and not needed. This is the most challenging and important step of implementing 5S as the human tendency is to preserve everything. For example, Computer Desktop has so many files that may or may not be always required, which makes it difficult to find the desired file. Similar is the case with team shared drives.
  2. Seiton means Set in order i.e. defining a place for everything and keep it in its designated location. For example in shared drives, the nomenclature for folders should be such that anyone can understand where to save a particular file.
  3. Seiso means Shine i.e. keeping everything clean. Cleaning frequency should be defined to ensure everything is cleaned regularly. For example, shared drive should clean on a regular interval to ensure non-relevant files are archived.
  4. Seiketsu mean Standardize i.e. the instruction to maintain and monitor first three steps should be clearly defined. 5S is not a one-time job; it is a regular activity. Hence once the required frequency to repeat first three steps is agreed it should be standardized. Also, it helps in sharing best practices rather than re-inventing the wheel.
  5. Shitsuke mean Sustain. It is all about building self-discipline. All these steps should be done without being told to do. The process should be set to regularly audit and monitor for all the departments

How do we interpret the significance level and p-value under the concept of null hypothesis?

 

Significance level and P-value are related to the Type I error of Hypothesis testing, hence lets go back to basics.

Hypothesis testing is used to statistically validate a theory defined for a sample or among multiple samples. Unlike common belief, hypothesis testing does not help the researcher to choose between two options but researcher will always stick to null hypothesis unless there is significant evidence that alternate hypothesis is true.

Null Hypothesis (H0) is general ‘no effect’ hypothesis

Alternate Hypothesis (H1) is the condition that researcher is trying to prove right.

In hypothesis testing, there are two possible errors

1. Type I error: Rejecting null hypothesis when it is true.

For example, we are trying to validate if Average handling time (AHT) has improved post conducting refresher training.

Null Hypothesis: Refresh training has no effect on (AHT)

Alternate Hypothesis: AHT has improved post conducting refresher training.

Here, If a researcher is making type I error, s/he is acting assuming there is an effect. In our AHT example, company will start investing more on conducting refresher trainings assuming it will improve AHT.

Hence this error also referred as Producer’s risk.

The chances of making Type I error is denoted using α (alpha) symbol, which is the level of significance set for the hypothesis testing.

If α is 0.05, this means researcher is willing to accept 5% chances that s/he is wrong in rejecting the null hypothesis.

Statistical analysis generates a probability denoted as ‘p-value’ which refers the likelihood of rejecting the null hypothesis when it is correct.

Hence if the p-value is greater than α, we fail to reject null hypothesis considering that is the safest option for producer and we do not have enough evidence to prove that alternate theory is correct.

Now since we are taking about errors, let me complete article by adding bit explanation of Type II error as well.

2. Type II error: Failing to reject the null hypothesis when it is false.

Type II error is Consumer’s risk because even though there was an effect, the producer did not act on it hence the consumer did not get the better results.

The probability of making type II error is referred as β(Beta). The power of the test is in giving the correct result, i.e., accepting alternate hypothesis when null is false because that was the whole objective of conducting a hypothesis test. Hence the power of the test is referred using (1-β).

How do we interpret the significance level and p-value under the concept of null hypothesis?

What is Sigma Level and how to calculate it?

Sigma Level

Sigma level indicates the compliance rate of the process i.e. how effective process in avoiding defect or in other words is meeting client’s expectation. It is considered to be the positive way of representing the process capability

Short Term Sigma Level (Zst)

Short term sigma level is calculated using within standard deviation of the process. Zst represents the potential capability of the process i.e. how the process will perform if all short term variations are constant which is an ideal scenario.

Long Term Sigma Level (Zst)

Long term sigma level is calculated using the overall process standard deviation, hence representing the actual capability of the process. It is considered that over the period because of natural variation the short term sigma level is shifted by 1.5σ. Thus ZLT can also be calculated as

Sigma level can be calculated for both attribute data as well as continuous data.

Sigma Level for Attribute data

The sigma level for discrete data is calculated using DPU and DPMO.

The standard normal distribution (Z-distribution) is the tool referred to calculate the sigma level using DPMO.

Sigma Level

At 6σ level, the process is expected to make only 3.4 defects per million opportunities.

To calculate sigma level in Minitab, calculate compliance rate:

In Minitab, go to Calc -> Probability Distribution -> Normal, as we use standard normal distribution to calculate sigma level

Now, select ‘Inverse cumulative probability’ and updated compliance rate in ‘Input Constant.’ The ‘x’ value in the session window is the short term sigma level of the process if short-term attribute data is collected.

Sigma Level for Continuous data

Sigma level for continuous data is represented as the number of standard deviations (σ) can fit between the Mean and closest specification limit (SL). If Zst is six, that means six standard deviations (σ) can be accommodated between mean and SL. Higher Zst means lower the variation hence lower the DPMOs.

What is the difference between Lean Six Sigma and Six Sigma only?

What is the difference between Lean Six Sigma and Six Sigma only? by Shweta Ravi

Six Sigma is a data-driven methodology first introduced in the 1970s. Dr. Mikel Harry, the senior staff at Motorola, was first one to try statistical problem-solving. Later on Bill Smith, an engineer at Motorola designed six-step methodology to reduce variation and considered as the father of Six Sigma. Jack Welch then made it the center of business strategy at General Electrical. In today’s competitive world, Six Sigma has widely accepted as a philosophy for growth across different sectors.

Six Sigma is based on data analysis, hence helps in reducing risk which one of the many reasons that Six Sigma is still preferred over other improvement methodologies. The objective is to remove defects at the source and bring down performance to ‘six sigma’ level i.e. 3.4 defects for every one million opportunities. Continuous effort is made to achieve stable and predictable process.

Even before Six Sigma in 1930 in Japan, the owner of Toyota motors, Kiichiro Toyoda, asked Taiichi Ohno (Industrial Engineer) to look into Henry Ford, flow production model. This journey resulted in the introduction of Toyota Production System, which then became the foundation of ‘Lean Methodology,’ focused on reducing waste in the process.

In early 2000, the first concept of Lean Six Sigma introduced, which combines both the methodology to reduce waste and variation in the process.

Edward Deming has consolidated Six Sigma methodology in five step model widely known as DMAIC, (Define, Measure, Analysis, Improve, and Control), which continued for Lean Six Sigma as well. The objective of each step is now achieved using both statistical and lean methods.

What is the difference between Lean Six Sigma and Six Sigma only?

What is throughput time? Is it the same as lead time?

 

Throughput time is not a widely used word in Lean Six Sigma world.

‘Throughput’ is the number of items produced by a process in a given period of time. And ‘Cycle time’ is the average time required to produced one item by a process.

So if for Process A, cycle time is 10 mins per item.

Throughput of the process will be 6 items per hour.

Lead time is the time required to deliver final product to the client, i.e. time lag between initiation and completion of the request.

Assume, for A Process A, a request was raised on 20-Mar and product delivered to client on 22-Mar, the lead time is 2 days while the cycle time is only 10 mins, which means for rest of the time the request Is actually waiting in the queue for some reason/s. This gives you an opportunity for process improvement.

Another concept worth understanding is Takt time.

Takt time is the rate at which process should run in order to meet customer demand.

For example, on a particular day for Process A, the demand is 100 items.

Assuming 2 people working for 8 hours per day:

Total available time = 2*8 = 16 hours = 960 mins

Takt time = [Time available] / [ Number of units to be processed] = 16 hours / 100 items

= 9.6 mins per item

So with the current cycle time of 10 mins, you won’t be able to achieve demand. Hence the cycle time should always be less than takt time. Now, you can run another project to reduce process cycle time to 9.6 mins per item.

What is throughput time? Is it the same as lead time?