A box plot is a graph that shows the frequency of numeric data values for a given variable. It indicates where most of the data is grouped and how much variation there is in the process. It is most useful when comparing between several data sets. This template allows you to enter up to 70 individual data points for two data sets, and the box plots will be displayed automatically to reflect your data.

Read more »# Tag Archives: probability

# Box Plot

A **box plot** is a graph that shows the frequency of numeric data values. It can be drawn either horizontally or vertically. It is referred to as a Box-and-Whisker Plot as it displays the data in a box-and-whiskers format. Box plots are widely used in statistics, scientific research, higher education, process improvement, and in social and human sciences.

# Histogram

A **histogram** is a graph which shows the frequency of continuous data values. It is a type of bar chart that can be drawn either vertically or horizontally. Histograms are widely used in statistics, process improvement, scientific research, economics, and in social and human sciences.

# Histogram Template

A histogram is a bar chart that represents the frequency distribution of data on a given variable. It indicates where most of the data is grouped and how much variation there is in the process. This template allows you to enter up to 100 individual data points, and the histogram will be displayed automatically to reflect your data. The more data there is, the more accurate the histogram will be.

Read more »# Normality Testing in Minitab

The Normal Distribution is the commonest and the most useful continuous probability distribution. Many statistical tests require that the distribution is normal or nearly normal. Several tools are available to assess the normality of data including: using a histogram to visually explore the data, producing a normal probability plot, and carrying out an Anderson-Darling normality test. All these tools are easy to use in Minitab statistical software. Read more »

# Probability Distributions in Minitab

There are different shapes, models and classifications of probability distributions including the ones discussed in the probability distributions article. It is always a good practice to know the distribution of your data before proceeding with your analysis. Once you find the appropriate model, you can then perform your statistical analysis in the right manner. Minitab can be used to find the appropriate probability distribution of your data. Read more »

# Normal Distribution

Continuous distributions describe variables that take values from a continuous range and can be measured with any degree of accuracy. The commonest and the most useful continuous distribution is the normal distribution. The **Normal Distribution** is a symmetrical probability distribution where most results are located in the middle and few are spread on both sides. It has the shape of a bell and can entirely be described by its mean and standard deviation. Read more »

# Probability Distributions

Most improvement projects and scientific research studies are conducted with sample data rather than with data from an entire population. A **Probability Distribution** is a way to shape the sample data to make predictions and draw conclusions about an entire population. It refers to the frequency at which some events or experiments occur. It helps finding all the possible values a random variable can take between the minimum and maximum statistically possible values. Read more »

# Risk Analysis

**Risk Analysis** is a process that helps identify and assess potential threats that could affect the success of a business or project. It allows to examine the risks and includes means to measure, mitigate and control them effectively. It is part of the larger process of risk management although risk management can also refer to the process of controlling and monitoring risks. Read more »

# Process Yield Measures

An ideal process must produce without defects and without rework. To expose these unnecessary and costly inefficiencies, you should have appropriate performance metrics to measure process yield, or otherwise, the true process yield might be underestimated. Process yield measures should be able to expose even the smallest inefficiencies in a process, which will enable operations to understand their true process yield in order to set realistic improvement targets.

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