*Areas of applications*

CONCEPTUAL OVERVIEW : RADAR GRAPHS

Radar graphs are similar to line graphs, except that they use a radial grid to display data items. A radial grid displays scale value grid lines circling around a central point, which represents zero. Higher data values are farther from the center point.

Radar charts are useful when you want to look at several different factors all related to one item. Radar charts have multiple axes along which data can be plotted.

For example, you could use a radar chart to compile data about a wide receiver on a professional football team. On one axis, you could plot the percentage of passes caught. Another axis would show his yards per completion; another, his completions per 100 plays; another, blocks made; and a final axis might show his interceptions.

If a team did this for all their wide receivers, they could easily spot the best player as well as each player's strengths and weaknesses.

In a radar chart, a point close to the center on any axis indicates a low value. A point near the edge is a high value. In the football example, we would high marks near the outside due to the nature of what was being measured. In other scenarios, you might want points near the center, or low values. When you're interpreting a radar chart, check each axis as well as the overall shape to see how well it fits your goals.

• Radar charts are commonly used by consultants to demonstrate how a client organization compares to its competitors in a given industry. The spider chart template provides a view of data comparing the client company's performance to that of its competitors' various areas, illustrating strengths and weaknesses.

• A spider chart shows how a team has evaluated several organizational performance areas. The investigation that feeds data to the chart should include varied perspectives to provide an overall realistic and useful picture of performance.

• In finance, radar chart be used to corporation’s profit and the variation of financial index. In quality management, it can also be used to measure the distance between expect quality and actual quality. In organization, it assesses the morale of staff, commitment to a process or relative teaching strengths.

NAME: ANJALI MEENA

GROUP NAME: FINANCE_3

What you do when you have a table with 3 related dimensions of data, how do you represent it on a flat 2D chart? The answer to this question is Bubble chart. Bubble chart helps us in putting 3 parameters in one elegant visualization..

A bubble chart is a type of chart where each plotted entity is defined in terms of three distinct numeric parameters.

The entities displayed on a bubble chart can be compared in terms of their size as well as their relative positions with respect to each numeric axis. Since both X and Y axis of the bubble chart is numeric scales, the position of plot is an indicator of two distinct numeric values. The area of the plot depends on the magnitude of a third numeric characteristic. One concern when rendering data with a bubble chart is that the area of a circle is proportional to the square of the radius. So if you scale the radius with your third data point, you will disproportionally emphasize the third factor. To get a properly weighted scale, one should take the square root of the magnitude of this third metric. However, many bubble charts are rendered without this correction.

A bubble chart can be considered a "variation of a scatter plot, in which the data points are replaced with bubbles. This type of chart can be used instead of a Scatter chart if your data has three data series, each of which contains a set of values^{”.}

^{Number of products is displayed along the horizontal (x) axis.}^{Sales amounts are displayed along the vertical (y) axis.}^{Market share percentages are represented by the size of the bubbles.}

Bubble charts are often used to present financial data. Bubble charts can facilitate the understanding of the social, economical, medical, and other scientific relationships

We should use a bubble chart Bubble, when we want specific values to be more visually represented in your chart by different bubble sizes. Bubble charts are useful when your worksheet has any of the following types of data:

**Three values per data point**Three values are required for each bubble. These values can be in rows or columns on the worksheet, but they must be in the following order: x value, y value, and then size value.**Negative values**Bubble sizes can represent negative values, although negative bubbles do not display in the chart by default. You can choose to display them by formatting that data series. When they are displayed, bubbles with negative values are colored white (which cannot be modified) and the size is based on their absolute value. Even though the size of negative bubbles is based on a positive value, their data labels will show the true negative value.**Multiple data series**Plotting multiple data series in a Bubble chart (multiple bubble series) is similar to plotting multiple data series in a Scatter chart (multiple scatter series). While Scatter charts use a single set of x values and multiple sets of y values, Bubble charts use a single set of x values and multiple sets of both y values and size values.

Bubble or bubble with 3-D effect - both bubble chart types compare sets of three values instead of two. The third value determines the size of the bubble marker. You can choose to display bubbles in 2-D format or with a 3-D effect.

References:

Gaurav Kumar

Marketing 2

Discriminant analysis a very versatile tool used for second level analysis. The main purpose of a discriminant function analysis is to predict group membership based on a linear combination of the interval variables. The procedure begins with a set of observations where both group membership and the values of the interval variables are known. The end result of the procedure is a model that allows prediction of group membership when only the interval variables are known. A second purpose of discriminant function analysis is an understanding of the data set, as a careful examination of the prediction model that results from the procedure can give insight into the relationship between group membership and the variables used to predict group membership. Example -predict whether patients recovered from a coma or not based on combinations of demographic and treatment variables. The predictor variables might include age, sex, general health, time between incident and arrival at hospital, various interventions, etc.

Discriminant analysis is most often used to help a researcher predict the group or category to which a subject belongs. For example, when individuals are interviewed for a job, managers will not know for sure how job candidates will perform on the job if hired. Suppose, however, that a human resource manager has a list of current employees who have been classified into two groups: "high performers" and "low performers." These individuals have been working for the company for some time, have been evaluated by their supervisors, and are known to fall into one of these two mutually exclusive categories. The manager also has information on the employees' backgrounds: educational attainment, prior work experience, participation in training programs, work attitude measures, personality characteristics, and so forth. This information was known at the time these employees were hired. The manager wants to be able to predict, with some confidence, which future job candidates are high performers and which are not. A researcher or consultant can use discriminant analysis, along with existing data, to help in this task.

For discriminant analysis, the predictor variable needs to be ordinal or scale. This way we can accurately analyze the effect of such variables on our hypothesis.Some of the famous applications of of this method are- Bankruptcy prediction, where a very famous methods – the Altman Z score model is still used. The Z score is used to predict whether the firm will survive or not.

Face recognition also uses discriminant analysis. Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template. The linear combinations obtained using Fisher's linear discriminant are called Fisher faces, while those obtained using the related principal component analysis are called eigenfaces.

Today was a day filled with graphics, trying to make it look interesting and easy to analyze. Initially we made some really sad graphs like the 2D bar graph, with data from the retail data set. Then we moved up slowly to 3D graphs and then the whole thing got really interesting when the 3D graph could be rotated. It is called the Interactive Graph where we take two attributes and then plot them on the graph.

Like sir explained in the class, Imagine that the OLAP cube is literally a cube and u cut a small portion of that cube for analysis. Here also we do the same wherein we segment the data into smaller cubes and analyze it from various angles. The process is called Online Analytic Process because we can quickly check up the details and get instant online results.

In order to make OLAP cubes, we should understand summary variable and grouping variable.

Summary variable is also called as scale. It is discrete i.e. round it off (if you are of the age 14.4 you tend to say 14). Here, age, monthly bill, SMS bill etc are considered. Grouping (Category) variable include gender, level of education, name of the service provider etc. You take both of these on the x axis and the y axis.

Although it was a disappointment to know that OLAP cube was not really a cube, it is a table which represents the data in a classified manner. Once the attributes are selected, a report will be generated which can be filtered according to the choice of comparison that the analyst requires. Along with this we also learned to analyze Bubble Graph, an example to this was the ICICI bubble graph analysis regarding Hedge funds, liquid funds, income etc. Then the report from a government website showed moving bubbles representing the no. of people employed and the trends in productivity and value added.

This type of graph is mainly used when we have more number of attributes. This is like Perception Mapping. In order to analyze and understand we should look into different attributes, as they are differentiated using different colors, from the closeness of it from the center to the outside. The further it is from the center the better it is. It helps in looking at the attributes from different perspective and interprets the data deeply

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