With the help of graphical method
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Answer:
°•°•°•°•Graphical method of linear programming is used to solve problems by finding the highest or lowest point of intersection between the objective function line and the feasible region on a graph.°•°•°•°
Explanation:
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Answer:
Graphical methods for assessing normality
Several graphical methods for verifying the assumption of normality have been proposed (D'Agostino, 1986). One commonly used method is the probability plot (Gerson, 1975), of which the quantile–quantile (Q–Q) plot is a special case. Another graphical method that is not as widely used as the probability plot is the normal density plot (Jones and Daly, 1995; Hazelton, 2003), which is easier to interpret than a probability plot because it is based on a direct comparison of a certain plot of the sample data vs. the familiar bell-shaped curve of the normal distribution.
While graphical examination of data can be extremely valuable in assessing a distributional assumption, the interpretation of any plot or graph is inherently subjective. Therefore, it is not sufficient to base the assessment of a distributional assumption entirely on a graphical device. Bernstein et al. (1999) evaluated the use of a bile acid-induced apoptosis assay as a measure of colon cancer risk. They determined that their apoptotic index (AI) “had a Gaussian distribution, as assessed by a box plot, quantile–quantile plot, and histogram” (p. 2354). However, each of these methods is a graphical technique, and different data analysts could interpret the plots differently. One should always supplement the graphical examination of a distributional assumption with a formal statistical test, which may itself be based on the results of the graphical device that was used. For example, correlation coefficient tests based on probability plots have been shown to have good power for detecting departures from normality against a wide variety of non-normal distributions (Looney and Gulledge, 1985). Formal tests of the distributional assumption can also be based on a normal density plot (Jones and Daly, 1995; Hazelton, 2003).
Interpretation of Spectral Maps Using Interactive Graphics
Interactive graphical methods can be used to explore multivariate data. These allow users with the required domain expertise to explore the data, when they are not yet prepared to draw conclusions based on statistical methods. A number of computer applications have been designed that support the interactive data exploration of spectral maps (SMA biplots). Coupled, interactive visualizations allow different graphical representations of the same data to be linked, such that an observation that is marked in one visualization is also marked in another. Dynamic querying or filtering of the data selects observations based on the values of specified variables, and optionally hides other observations. This allows closer inspection of certain subgroups in the data. Interactive graphical exploration also allows drilling down into the details of selected row or column items of the data table.
An example using the Golub data set is given in Figure 10. The two-dimensional scatter plot in panel (a) of the figure contributes a total of 26% of the interaction variance. By adding a third axis to the biplot, we can increase the contribution to 35% (panel (b)). We selected a gene that is distant from the origin using filters. This gene is indicated in both the scatter plots by means of an arrow. A profile chart with the logarithmized (base 2) expression levels of the selected gene in the 38 cells is given in panel (c).
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