Wiki User
∙ 10y agoWant this question answered?
Be notified when an answer is posted
When comparing the spread or variability rather than the location or mean. For example, men's heights and women's heights. You "know" that, on average, men will be taller but you may want to see if the variability within the two sets is the same or different.
Used when you have an experiment with several related dependent measures. Also used to analyze data from a within subject design.
The sum of the interior angles is (12-2)*180 = 1800 degrees. But within that constraint, each angle can be anything between (but excluding) 0 and 360 degrees.
3456 (cm)?3 Approximately 14.96 US gallons. When in use, and only filled to within 1 inch of the top, you should treat it as a 13 gallon tank.
The repeated measures design (also known as a within-subjects design) uses the same subjects with every condition of the research, including the control.[1] For instance, repeated measures are collected in a longitudinal study in which change over time is assessed. Other studies compare the same measure under two or more different conditions. For instance, to test the effects of caffeine on cognitive function, a subject's math ability might be tested once after they consume caffeine and another time when they consume a placebo.(Source Reference: - http://en.wikipedia.org/wiki/Repeated_measures )
Sets of data have many characteristics. The central location (mean, median) is one measure. But you can have different data sets with the same mean. So a measure of dispersion is used to determine whether there is a little or a lot of variability within the set. Sometimes it is necessary to look at higher order measures like the skewness, kurtosis.
The range, inter-quartile range (IQR), mean absolute deviation [from the mean], variance and standard deviation are some of the many measures of variability.
Not necessarily. Dispersion refers to how spread out or clustered items are within a given area, whereas density specifically measures the number of items within a unit area. High dispersion can occur in both high- and low-density areas depending on the distribution pattern of the items.
minimizes the within-class variability while at the same time maximizing the between-class variability.
Error bars within treatments typically show the variability or uncertainty in the data points related to that specific treatment. They can represent standard deviation, standard error, confidence intervals, or other measures of dispersion, depending on the study design and statistical analysis. Error bars provide a visual way to assess the precision and reliability of the data within each treatment group.
The manner in which members of a population are arranged in a particular area is know as dispersion. There are three main kinds of dispersion, which are clumped dispersion, random dispersion, and uniform dispersion.
stabilizing
Yes, nitrogen can participate in dispersion forces, also known as London dispersion forces. These are weak temporary forces that are caused by the motion of electrons within atoms or molecules. Nitrogen molecules have a symmetrical distribution of electrons, which can result in temporary dipoles and induce dispersion forces.
Subject-to-subject differences in within-subjects F refer to the variability in the data between different participants in a study. This difference can impact the within-subject F-value, which measures the effect of a factor within subjects while accounting for individual differences. High subject-to-subject differences can lead to a larger within-subject F-value, indicating a stronger effect of the factor being studied.
It is a diversification of traits within a species. An example of this is ladybugs with different numbers of spots.
Spatial distribution refers to how individuals or objects are arranged across a given area, while dispersion specifically refers to the extent to which these individuals or objects are spread out or clumped together within that area. In other words, spatial distribution describes the pattern of distribution, whereas dispersion quantifies the degree of spread within that pattern.
The standard deviation of color matching refers to the variability or dispersion of color values within a set of samples or data points that are being matched or compared. A higher standard deviation indicates a greater degree of variation in color values, while a lower standard deviation suggests more consistency or similarity in color matching.