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Tools for Measurement
Published in Ron Basu, The Green Six Sigma Handbook, 2023
A histogram is a graphical representation of recorded values in a data set according to frequency of occurrence. It is a bar chart of numerical variables giving a graphical representation of how the data are distributed.
Continuous Improvement Toolkit
Published in Tina Kanti Agustiady, Elizabeth A. Cudney, Building a Sustainable Lean Culture, 2023
Tina Kanti Agustiady, Elizabeth A. Cudney
A histogram plots the frequency of values grouped together as a bar graph. Histograms are handy for determining location, spread, and shape. Outliers can easily be identified. The height equals the frequency and the width equals a range of values. A histogram with a bell-shaped curve is normal (Figure 8.34).
Quality Management Practices
Published in Sunil Luthra, Dixit Garg, Ashish Agarwal, Sachin K. Mangla, Total Quality Management (TQM), 2020
Sunil Luthra, Dixit Garg, Ashish Agarwal, Sachin K. Mangla
A histogram is the graphical representation of the data in which the parameters like defect, height, etc. are on the x axis and the frequency/occurrence of data is on the y axis. The range of data is divided into equal numbers of smaller sections and the frequency of this data is counted to prepare the frequency distribution table. With the help of this frequency distribution table, the vertical bars formed originated from the horizontal axis. The height of the vertical bars is directly proportional to the frequency of that smaller section. It is generally used to show the graphical representation of frequency of a sample, number of defects, population, etc. The histogram represents how frequently each value in a group of data occurs. It is used for analysis of a process just by reading the average value and degree of variation of graph. It provides the improvement area in a process. The objective of a histogram is to represent the distribution characteristics of the different parameter. It also represents the skewness of data as it can be symmetrical data, right-oriented skewness, or left-oriented skewness. Therefore, it can conclude that the histogram is very useful to understand the properties of data. It is useful to understand the required parameters.
Dependence of pre-treatment structure on spheroidization and turning characteristics of AISI1040 steel
Published in Cogent Engineering, 2023
Harisha S R, Sathyashankara Sharma, Ramakrishna Vikas Sadanand, Achutha Kini U, Raviraj Shetty, Sathish Rao U
Figure 10 shows the residual plot for TW. The model of turning characteristics for TW is suitable as signified by the points dropping on a conventional line in the normal probability plot, which in turn shows that the errors are normally distributed. Similarly, the plot of the residuals against the expected response is structureless, i.e., having no obvious pattern (Mongomery, 2017). The histogram shows a closely bell-shaped normal distribution. Furthermore, the high R-Sq and R-Sq (Adj) values indicate that the regression equations possess a good fit for the actual experiment conducted (Agrawal et al., 2015; Baskar et al., 2018). This equation can be used to predict TW involving factors with the range of values under study.
Brain tumor segmentation and classification using optimized U-Net
Published in The Imaging Science Journal, 2023
In the histogram feature extraction process, the pre-operative and post-operative MRI segmentsand are considered as input. The histogram features are extracted from the segments. Here, the histogram is plotted for the obtained segment. The histogram function utilizes an automated binning technique that returns the bins using a uniform width to cover the range of attributes and expose the underlying distribution shape. The histogram depicts the bins as rectangles so that the height of all rectangles represents the count of elements present in the bin. The histogram is a kind of bar plot considered for arithmetic data into bins. Once the histogram is created, one can modify the attributes of the histogram by altering its property values. This is beneficial for quickly altering the bin's properties or changing the display. The extracted histogram feature obtained from pre-operative and post-operative MRI segmentsand are denoted by and
A new approach to constructing SPT-CPT correlation for sandy soils
Published in Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2023
Yu-Chen Lu, Li-Wei Liu, Sara Khoshnevisan, Chih-Sheng Ku, C. Hsein Juang, Shi-Hao Xiao
The term is assumed to follow a normal distribution with a mean of and a standard deviation of . By substituting the data points into the above equation, the histogram of can be obtained, as shown in Figure 15. It is found that the histogram is approximately a bell-shaped distribution. For convenience, is assumed to be 0 in Equation (19), and is re-calibrated with the maximum likelihood principle, which equals 4.1.