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Lean Wiki is the latest online encyclopedia of lean tools and resources. It provides an easy way to access a range of high-quality articles on lean topics. The tools and resources are neatly organized into categories, making it easy for you to find what you need.

Latest Article: What Are Confidence Intervals and How to Calculate Them

Focused on lean tools, concepts and ideas, our Lean Wiki is a selection of key explanations to help you become familiar with the Lean methodology. We explore some of the key ideas behind Lean Thinking and explore core Lean terminology and principles.

Confidence Intervals
Last Update: What Are Confidence Intervals and How to Calculate Them
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Confidence Intervals

What Are Confidence Intervals and How to Calculate Them

Summary

Confidence intervals are a powerful tool for making well-informed business decisions. A confidence interval is an interval in which we can be certain of the parameters or values necessary to make accurate and reliable predictions about population parameters. By understanding what a confidence interval is, how it works and its importance to your organization, you’ll gain valuable insight into strategic decision-making that can help drive future success.

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Central Limit Therem

An Introduction to the Central Limit Theorem

The Central Limit Theorem (CLT) is a statistical theory that states that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population. Furthermore, if you plot the distribution of these sample means, it will form a normal distribution (bell curve), regardless of the shape of the original population distribution. The CLT is fundamental in statistics because it allows for considerable simplification and provides a method for making inferences about population parameters based on sample data.

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box cox transformation

The Box-Cox Transformation: What It Is and How to Use It

The Box Cox Transformation is a popular method of transforming non-normal dependent variables into a normal shape. This technique helps to stabilize variance and can improve the accuracy of any subsequent statistical tests or models. The transformation is named after statisticians George Box and David Cox who developed the technique in the 1960s. It involves taking the natural logarithm of a variable and then raising it to some power (lambda) which is determined by maximum likelihood estimation. The lambda value will depend on how skewed the data is, meaning that a different lambda will be used for different data sets. This transformation can be used in regression, ANOVA, and many other applications where there is a need to transform non-normal data into normal form.

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