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# Data Science

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# Do You Understand Your Survey Results?

Many companies spend considerable amounts of money on customer and employee surveys every year. The survey results are used to amend strategies, design new products and services, focus improvement activities, target staff development activities and … to celebrate success.

The question is: Can we always rely on what we see?

# Great, We Have Improved … or Not?

Many companies spend considerable amounts of money on customer surveys every year. Customer survey results are being used to amend strategies, design new products and services, focus improvement activities and

# Making Sense of Chi-Squared Test – Finding Differences in Proportions

The Chi-Squared test is used to check whether there is a significant difference between observed frequencies (discrete data) and expected frequencies for two or more groups (discrete data). For small sample sizes, Fisher’s Exact test is to be used instead. Both test are performed in a similar way. See for yourself.

# Making Sense of Test For Equal Variances

Three teams compete in our CHL business simulation. After completing Day One, it looks like the teams show a very different performance. Although the means look very similar, the variation is strikingly different. To test this assumption of different variation among the teams, the Test for Equal Variances is deployed.

# Make Use of Your Survey Data – Kano It

Survey data should be analysed with different tools at the same time in order to find the most appropriate method to show “patterns in data” that lead to conclusions. The Kano analysis or the Jaccard index offer additional insights into survey data.
Remember: Attaining the data is expensive, analysing them is cheap.

# Making Sense of the Two-Proportions Test

Consider a production process that produced 10,000 widgets in January and experienced a total of 112 rejected widgets after a quality control inspection. A project was deployed to fix this problem. In April, the process produced 8,000 widgets and experienced a total of 63 rejects. Did the process really improve? Read..

# Making Sense of Linear Regression

Linear regression is one of the most commonly used hypothesis tests in Lean Six Sigma work. Linear regression offers the statistics for testing whether two or more sets of continuous data correlate with each other, i.e. whether one drives another one. Additionally, it shows the influence of discrete variables, too.

# Making Sense of ANOVA – Find Differences in Population Means

ANOVA (analysis of variances) is a statistical technique for determining the existence of differences among several population means. The technique requires the analysis of different forms of variances – hence the name. It is testing whether means are different. Read about an application example here.

# Making Sense of Binary Logistic Regression

In some situations, Six Sigma practitioners find a Y that is discrete and Xs that are continuous. How can a regression equation be developed in these cases? Black Belt training indicated that the correct technique is something called logistic regression or binary regression. But this tool is often not well understood.

# Making Sense of the Two-Sample T-Test

More often than not, Lean Six Sigma practitioners have to find out whether two groups that look quite the same are really the same. If averages of two groups are in question and the data is continuous and normally distributed, the two-sample t-test offers an answer to the question. Read how to run this test in SigmaXL.

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