China
Africa
Explore chapters and articles related to this topic
Statistical and Graphical Foundation
Published in Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard, Thematic Cartography and Geovisualization, 2022
Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard
Measures of central tendency are used to indicate a value around which the data are most likely concentrated. Three measures of central tendency are commonly recognized: mode, median, and mean. The mode is the most frequently occurring value and is thus generally useful for only nominal data, such as on a land use/land cover map. The median is the middle value in an ordered set of data or, alternatively, the 50th percentile because 50 percent of the data are below it. For the murder rate data, the median is 9.7. Note its location in Figure 3.1B. The mean is often referred to as the “average” of the data and is calculated by summing all values and dividing by the number of values. Separate formulas are used to distinguish mean values for the sample and population, as follows:4Sample:X¯=∑i=1nXinPopulation:μ=∑i=1NXiN,
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
Besides the frequency distribution, we can calculate the “average” or central tendency. We measure central tendency in three ways: the mean, the median, and the mode. The mean is the arithmetic average. If five individuals responded to a survey item using a scale from 1 to 5 (very satisfied to very dissatisfied) and the responses were 1, 2, 3, 4, and 5, then the mean would equal the sum of all responses divided by 5, the number of respondents. In this case, the mean would be (1 + 2 + 3 + 4 + 5)/5 = 3. The median is the mid point of the distribution. If your data set included the following responses: 4, 3, 4, 5, 2, 4, 4, 1, 5, 6, 7, a frequency distribution table can often help you “see” the median (see Table 2.3). In this example, the median would be 3 because this splits your distribution in half—there are four values below and four values above the 3. Finally, the mode is the most frequently occurring number. From Table 2.3 we see that there are five threes, which are easier to see when plotted in a histogram, as shown in see Figure 2.7. Therefore, the mode also would be 3. It is possible to have more than one mode in a data set. Sometimes the data sets are bi modal where there are two modes, tri modal, which includes three modes, or multi modal, where there are more than three modes.
Research Methods and Statistics
Published in Monica Martinussen, David R. Hunter, Aviation Psychology and Human Factors, 2017
Monica Martinussen, David R. Hunter
The most common measure of central tendency is the arithmetic mean. This is commonly used when something is measured on a continuous scale. The arithmetic mean is usually denoted with the letter M (for “mean”) or X¯. An alternative is the median, which is the value in the middle, after all the values have been ranked from lowest to highest. This is a good measure if the distribution is skewed—for example, if the results include some very high or very low values. The mode is a third indicator of central tendency, which simply is the value with the highest frequency. Thus, it is not necessary that the variable be continuous to use this measure of central tendency.
Can you see the feel? The absence of tactile cues in clothing e-commerce impairs consumer decision making
Published in International Journal of Fashion Design, Technology and Education, 2023
Julia Wilfling, George Havenith, Margherita Raccuglia, Simon Hodder
To assess differences between the visual only and visual and haptic information data were checked for normal distribution. The data deviated from a normal distribution and differences were, therefore, tested using a Wilcoxon signed rank test (between subjects). A Kruskal–Wallis test was performed to look at differences in the perception of textiles between males and females. Dependent variables were defined as the textile attributes (rough, smooth, soft, etc.), the factor variable was sex. The medians of the data were calculated together with the 25th and 75th quartile. The median is less affected by outliers and skewed data and makes it a better option to measure the central tendency of respondents. Statistical analysis was performed using IBM SPSS Statistics 24 (IBM, USA). A probability level of p < 0.05 was defined as the threshold for significance.
An Efficient Document Clustering Approach for Devising Semantic Clusters
Published in Cybernetics and Systems, 2023
E. K. Jasila, N. Saleena, K. A. Abdul Nazeer
The method proposed has two phases- in which the first phase makes use of a sort-based approach for locating initial centroids, whereas, the second phase makes use of an efficient enhanced method for allocating data items to its appropriate clusters according to Jasila, Saleena, and Abdul Nazeer (2019). The arranged data set has been separated into k sets, in which k represents the no. of clusters. Further, the mean of every set is taken to compute the initial centroids. The “mean” denotes a measure of central tendency that serves as a point of reference for interpreting data items. It represents the information about the average value of a set of values and thereby shows where the data is located. The mean value of each set of sorted data items (initial centroids) helps to move similar data items to the proper clusters. The precise movement of data items to the clusters is achieved by the computed values of initial centroids. Thus, the sort-based method improves the movement of data items to exact clusters. Memoization techniques are utilized in the second stage of clustering to avoid redundant computations.
Analysis of Suitable Machine Learning Imputation Techniques for Arthritis Profile Data
Published in IETE Journal of Research, 2022
Uma Ramasamy, Sundar Santhoshkumar
The missing data category that belongs to MCAR is eligible to perform central tendency imputation. Central Tendency is the statistical method that quantifies how well a single value acts as an indicative measure of a whole distribution. The central tendency imputation belongs to the category of imputation with a single value and single imputation method. The incomplete data were replaced by mean, median, and mode values of an independent variable with missing values. The advantage of central tendency imputation is that it is easy to implement and faster to obtain the complete dataset. The downside of central tendency imputation is that the level of distortion in the original variance is higher and significantly impacts the correlation between independent variables.