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Research Methods in Human Factors
Published in Robert W. Proctor, Van Zandt Trisha, Human Factors in Simple and Complex Systems, 2018
Robert W. Proctor, Van Zandt Trisha
As implied by the name, descriptive statistics describe or summarize the results of research. One concept that is fundamental to descriptive statistics is that of the frequency distribution. When we obtain many measurements of a variable, we can organize and plot the frequencies of the observed values. For example, if we have a group of people estimate the mental workload imposed by a task on a scale of 1–7, we can record the number of people who responded with each value. This record of the frequency with which each score occurred is a frequency distribution. A frequency distribution often is plotted in the form of a frequency polygon, as is shown in Figure 2.3. A relative frequency distribution, also shown in the figure, displays the same plot on the scale of the proportion (or percentage) of times that each score was observed. We can describe a score in terms of its percentile rank in the distribution. A percentile is a point on a measurement scale below which a specified percentage of scores falls. The percentile rank is the percentage of scores that falls below that percentile. We use percentile ranks for, among other things, creating tables of anthropometric data and applying these data in the design of equipment for human use.
Billing Data Statistics and Applications
Published in J. Lawrence, P.E. Vogt, Electricity Pricing, 2017
Time series billing data can be rearranged, viewed, and analyzed in a variety of fashions. A frequency distribution is one way in which to organize data systematically. A frequency distribution for a data set is developed by compiling the number of observations, such as bills, according to user defined intervals, such as kWh (per bill). For example, an analysis of July billing data for a given rate class might show that 1,961 bills had usage falling within the interval of 1 to 10 kWh, 1,953 bills had usage falling within the interval of 11 to 20 kWh, and so forth. A relative frequency distribution can be developed by dividing the observations of each interval by the total number of observations. Thus, if the total number of July bills for the given rate class was 24,500, the relative frequency for the 1 to 10 kWh interval would be 0.080 (or 8.0%), and so forth.
Discrete-Event Modeling and Simulation
Published in Devendra K. Chaturvedi, ®, 2017
Input data collection and analysis require major time and resource commitment in discrete-event simulation. In the simulation of a queuing system, typical input data are the distribution of time between arrivals and service time. In real-life applications, determining appropriate distributions for input data is a major task from the standpoint of time and resource requirements. Wrong or faulty input data lead to wrong results and mislead the modeler. The procedure used for input data modeling is as follows: Data collection—Collect the data for real-life system. Unfortunately, sometimes it is not possible to collect sufficient data due to system limitations or many other reasons. In such situations expert knowledge and opinion is used to develop some fuzzy systems to generate useful and good data.Identify the distribution of data—When data are collected or obtained, develop a frequency distribution or histogram of data.Select the most appropriate distribution family and their parameters.Check the goodness of fit.
A novel image encryption using random matrix affine cipher and the chaotic maps
Published in Journal of Modern Optics, 2021
Parveiz Nazir Lone, Deep Singh, Umar Hussain Mir
The histogram is a discrete function , where is the intensity value and is the number of pixels of the image with intensity [41]. Thus histogram is a plot of frequency distribution (or a graphical representation of the pixel intensity distribution at each intensity level). The histograms of the plain Barbara image are given in Figure 7(a)–(c) for each colour component, and the histograms of the corresponding cipher image are given in Figure 7(d)–(f) respectively. Thus the histograms in Figure 7(d)–(f) and (h) are significantly distinct from the original image and show a visual uniform distribution over the range. Besides, we also use Chi-square test to check the uniformity of the histogram by where is the observed value and is the expected value of the gray level. Assuming a significance level of for a 8-bit image, the critical value is . The average test value of the scheme is 290.7317, lower than critical value implies that the encrypted image has a uniform distribution.
Determinants of rainwater harvesting practices in rural communities of Limpopo Province, South Africa
Published in Water Science, 2023
Selelo Matimolane, Sheldon Strydom, Fhumulani I. Mathivha, Hector Chikoore
The completed questionnaires were screened for completeness, coded and captured into the Statistical Package for Social Sciences (SPSS) version 20.0 software for analysis. Frequency distribution included creating a frequency table to summarize and organize the data by recording every possible score of the respondents as a column of numbers and the frequency of occurrence of each score. The tables showed the number of frequencies with their percentages. The information was confined to the frequency table and was converted into a form of charts, tables, and graphs.
Occupational health and safety issues in the informal economic segment of Pakistan: a survey of construction sites
Published in International Journal of Occupational Safety and Ergonomics, 2018
Ishfaq Ahmed, Muhammad Zeeshan Shaukat, Ahmad Usman, Muhammad Musarrat Nawaz, Mian Sajid Nazir
Both observation and interview protocols were designed on the basis of existing literature [e.g., 28,35,39,40]. The survey questionnaire was shared with two researchers and academics in order to ensure its validity and ability to fetch true information. After discussion and incorporating all of the changes, the final version of the structured protocols was used for data collection. Data collected through these tools were analyzed using frequency distribution.