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Introduction to Statistics
Published in Michael Baron, Probability and Statistics for Computer Scientists, 2019
The main descriptive statistics of a sample can be represented graphically by a boxplot. To construct a boxplot, we draw a box between the first and the third quartiles, a line inside a box for a median, and extend whiskers to the smallest and the largest observations, thus representing a so-called five-point summary:five-point summary=(minXi,Q^1,M^,Q^3,maxXi).Often a sample mean X¯ is also depicted with a dot or a cross. Observations further than 1.5 interquartile ranges are usually drawn separately from whiskers, indicating the possibility of outliers. This is in accordance with the 1.5(IQR) rule (see Section 8.2.6).
Research Methods
Published in Nancy J. Stone, Chaparro Alex, Joseph R. Keebler, Barbara S. Chaparro, Daniel S. McConnell, Introduction to Human Factors, 2017
Nancy J. Stone, Chaparro Alex, Joseph R. Keebler, Barbara S. Chaparro, Daniel S. McConnell
Descriptive methods are used to collect data that identify and define the situation and can be used to make predictions, which correspond to the first two goals of psychological research (describe and predict). These descriptive data include measures of how many, how much, or how often and are typically reported as frequencies. Baseball fans are familiar with descriptive data, as baseball, compared to all other sports, has the greatest number of recorded descriptive statistics, including runs batted in, homeruns, errors, innings pitched, and strikeouts. Just like baseball fans, human factors researchers want to understand the situation. In our driving simulation, we might record descriptive data such as how many men and women participated, age of participants, how many times the individuals were distracted or crashed, how much time the individuals spent talking on the “phone,” and mean time to complete a road course. Common descriptive statistics include measures of central tendency or the “average” (e.g., mean, median, mode) and the dispersion of data (e.g., range, standard deviation). Review the supplemental material (Section 2.9) if these terms are not familiar.
Measures of Central Tendency: Means, Modes, Medians
Published in Alan R. Jones, Probability, Statistics and Other Frightening Stuff, 2018
We have explored what are meant by the group of Descriptive Statistics called the Measures of Central Tendency; principally the Mode, Median and Mean, but that the Mean breaks down into four distinct measures – the Arithmetic, Geometric, Harmonic and Quadratic Means, all with their own specific uses. We noted that these four Means always yield different values when applied to a set of data unless all the terms in the range are equal, in which case all four Means are also equal; in these circumstances we are better forgetting about Measures of Central Tendency as we will undoubtedly use a different term altogether – constant!
Inferential-Statistical Reevaluation of Spent Fuel Zircaloy Cladding Integrity
Published in Nuclear Technology, 2023
Analysis of the data by methods of descriptive statistics yields measures of properties of the sample, e.g., mean and median as measures of the property of centrality and variance and standard deviation as measures of the property of scale. Analysis of the data by methods of inferential statistics yields measures of properties of the underlying population, e.g., tolerance intervals and prediction intervals as measures of the combined properties of location and scale and relative likelihood estimates as measures of how well hypothetical probability distributions (models) of the population fit the data. Individually and collectively, measures of properties of a population serve to quantify the random behavior of future measurements, a predictive capability conferred by inferential statistics, not descriptive statistics.
The CoDIS Taxonomy for Brain-Computer Interface Games Controlled by Electroencephalography
Published in International Journal of Human–Computer Interaction, 2023
Gabriel Alves Mendes Vasiljevic, Leonardo Cunha de Miranda
This dimension describes which methods were employed to analyse the data collected from the subjects. In this dimension, the methods are divided into quantitative and qualitative. Quantitative methods are those that analyse data based on its numerical and categorical values using descriptive and/or inferential statistics. Descriptive statistics is used to describe the data based on measures that represents its population (e.g., mean, median, mode, standard deviation, distribution, and measures of variability). Inferential statistics uses a sample of the population to make inferences about the whole population and the differences between groups. This includes statistical tests such as t-tests, ANOVA, MANOVA and regression analysis.
Prediction of fly-rock during boulder blasting on infrastructure slopes using CART technique
Published in Geomatics, Natural Hazards and Risk, 2021
Narayan Kumar Bhagat, Aditya Rana, Arvind K. Mishra, Madan M. Singh, Atul Singh, Pradeep K. Singh
Statistical analysis is a process to draw inferences from the collected data samples. Prior to application of any advanced method for data analysis, mainly two types of statistical analysis viz. descriptive and inference are carried out to understand the data, identify the trends, locate the anomalies and visualize the raw data. Descriptive statistics delivers a data summary in the form of minimum, maximum, mean, median, mode, standard deviations and other information of a data sample. Hence, it enables us to present the data in a more logically with simplicity. Whereas, inferential statistics is carried out to study the data even further by making a hypothesis leading to rational decisions about the reality of the effects observed.