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Presentation of statistical data
Published in John Bird, Bird's Higher Engineering Mathematics, 2021
Statistics is the study of the collection, organisation, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments. Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, engineering, government and business. Statistical methods can be used for summarising or describing a collection of data. Engineering statistics combines engineering and statistics. Design of experiments is a methodology for formulating scientific and engineering problems using statistical models. Quality control and process control use statistics as a tool to manage conformance to specifications of manufacturing processes and their products. Time and methods engineering use statistics to study repetitive operations in manufacturing in order to set standards and find optimum manufacturing procedures. Reliability engineering measures the ability of a system to perform for its intended function (and time) and has tools for improving performance. Probabilistic design involves the use of probability in product and system design. System identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models. This chapter introduces the presentation of statistical data.
Quality Tools for Oil and Gas Industry
Published in Abdul Razzak Rumane, Quality Management in Oil and Gas Projects, 2021
Sigma is a Greek letter σ that stands for standard deviation. Standard deviation is a statistical way to describe how much variation exists in a set of data, a group of items, or a process. Standard deviation is the most useful measure of dispersion. Six Sigma means that a process to be capable at Six Sigma level, and the specification limits should be at least 6 σ from the average point. So the total spread between upper specification (control) limit and lower specification (control) limit should be 12 σ. With Motorola’s Six Sigma program, no more than 3.4 defects per million fall outside specification limits with process shift of not more than 1.5 σ from the average or mean. Six Sigma started as a defect reduction effort in manufacturing and was then applied to other business processes for the same purpose.
Human Health Studies
Published in Barry L. Johnson, Impact of Hazardous Waste on Human Health, 2020
Two important definitions that relate to the epidemiologic studies to be summarized in subsequent sections are population and sample. Population is a statistical term denoting all the objects, events, or subjects in a particular class (Hensyl, 1987). The application of this definition requires a determination of class and all members of the class. For example, a population could be represented by all persons in a community exposed to solvents released from a particular hazardous waste site. A population could also be all infants with birth defects born in a defined geographic area; for example, a state. Often it is impractical or unnecessary to investigate all objects, events, or subjects in a population, and a sample of the population is taken. A sample is a specified portion of a population selected for study with the hope that it is representative of the entire population (Peavy, 1996). The observations, measurements, and analyses of the sample are subjected to statistical methods to draw conclusions from the sample about the population’s characteristics.
High-rise apartment quality evaluation and related demographic factors: lesson from RentSafeTO programme
Published in Building Research & Information, 2023
ANOVA is a statistical method that is used to compare the differences between the mean value of two or more groups (St»hle & Wold, 1989). It uses a probability distribution to measure the variance. In statistics, the p-value refers to the probability, assuming the null hypothesis (H0) is correct, that the test statistic equals the observed value or a value even more extreme in the direction predicted by the alternative hypothesis (H1). H0 proposes that there is no difference between the groups studied, while H1 proposes that a difference exists. The p-value is used as an indicator for rejection according to the significance level of the results. The significance level (α) is the probability of rejecting the null hypothesis when it is true. For example, an α of 0.05 indicates a 5% risk of inferring that a difference exists when there is no actual difference. Thus, a smaller p-value implies that there is stronger evidence to support H1 (Alassaf & Qamar, 2020). ANOVA can be used to test for between-group differences in residential satisfaction (Chen, 2012). The study used one-way ANOVA to determine if a difference existed between private and social housing scores. P-values less than 0.05 were considered statistically significant.
Optimal Probabilistic Scheduling of a Proposed EH Configuration Based on Metaheuristic Automatic Data Clustering
Published in IETE Journal of Research, 2022
Hadi Hosseinnejad, Sadjad Galvani, Payam Alemi
Statistical inference is the method by which data processing is used to deduce the properties of a probability distribution. The observed data collection is known to be sampled from a larger population. Descriptive statistics should be contrasted to inferential statistics. Statisticians distinguish between three types of fully parametric, nonparametric, and semi-parametric assumptions [40]. By considering the mentioned methods the results analyzed by descriptive statistics in (Table 8). Results show the standard deviation (STD) 5.76 for electrical input’s mean which is 71.63 MW, and 3.01 for gas input’s mean that is 121.19 MW. This means the usage tolerance is for of periods are between 71.635.76 MW for electricity input and 121.193.01 MW for gas, because of the normal distribution of demands which mentioned before. Considering the STDs and applying the cost function by using the gas and electricity price as mentioned, the total difference of uncertainty will $12,135.19 (which is 14.59% increase in total cost that was $83,157.13). Also, it should notice that the proposed configuration in comparison to the base hub will cost $48.74 lower in every single day.
Predicting loads and dynamic responses of an offshore wind turbine in a nonlinear mixed sea
Published in Ships and Offshore Structures, 2021
We can notice from Figure 5 that the three wave time series have approximately the same mean value. However, the standard deviation values of the red curve and green curve wave time series in Figure 5 are quite different. In statistics, the standard deviation is a measure that is used to quantify the amount of variation or dispersion of a set of data values. A low standard deviation indicates that the data points tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values. The standard deviation values in Table 2 quantitatively show that in Figure 5 the red curve from the nonlinear simulation with bottom effects fits perfectly with the blue curve from the transformed linear simulation with bottom effects. These standard deviation values in Table 2 also show quantitatively that in Figure 5 the two curves from nonlinear simulations with and without bottom effects deviate quite substantially from each other.