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The concepts of syn and anti addition are used to characterize the different reactions of organic chemistry by reflecting the stereochemistry of the products in a reaction. The type of addition that occurs depends on multiple different factors of a reaction, and is defined by the final orientation of the substituents on the parent molecule .
Anti-periplanar geometry will put a bonding orbital and an anti-bonding orbital approximately parallel to each other, or syn-periplanar. Figure 6 is another representation of 2-chloro-2,3-dimethylbutane (Figure 5), showing the C–H bonding orbital, σ C–H, and the C–Cl anti-bonding orbital, σ* C–Cl, syn-periplanar.
syn/anti peri/clinal. a torsion angle between 0° and ±90° is called syn (s) a torsion angle between ±90° and 180° is called anti (a) a torsion angle between 30° and 150° or between −30° and −150° is called clinal (c) a torsion angle between 0° and ±30° or ±150° and 180° is called periplanar (p)
In statistical process control (SPC), the ¯ and R chart, also known as an averages and range chart is a type of scheme, popularly known as control chart, used to monitor the mean and range of a normally distributed variables simultaneously, when samples are collected at regular intervals from a business or industrial process. [1]
Whereas statistics and data analysis procedures generally yield their output in numeric or tabular form, graphical techniques allow such results to be displayed in some sort of pictorial form. They include plots such as scatter plots , histograms , probability plots , spaghetti plots , residual plots, box plots , block plots and biplots .
The mean difference is the estimated bias, and the SD of the differences measures the random fluctuations around this mean. If the mean value of the difference differs significantly from 0 on the basis of a 1-sample t-test, this indicates the presence of fixed bias. If there is a consistent bias, it can be adjusted for by subtracting the mean ...
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In probability theory and statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1]. Copulas are used to describe/model the dependence (inter-correlation) between random variables . [ 1 ]