Applied Business Statistics (Date : 1/7/2013)

"9 out of 10 doctors recommend Oral B"
"8 out of 10 people prefer to buy of brand Y cars"
"The average person on an average buys Z T shirts  every year"

We all have seen such statistics and relied upon them as customer. For most of us they merely represent a set of numbers but have we ever wondered that could be the most vital piece of information that can be put to use for various purposes to derive meaningful information. Statistics when properly defined denotes a scientific approach involving collection of random data, organizing such data and thereafter analyzing such data to interpret certain results thereof. It can refer to single facts such as the number of people who like black coffee or the percentage of cats that is white.

One important aspect about statistics as pointed out by our professor was that we should learn to observe the pattern involved in data and moreover an individual should know how to filter out the useful data through the process of ‘data mining’   

The analysis of such statistics can be carried out on the basis of variables involved namely

 CLASSIFICATION OF VARIABLES-:

Univariate - Univariate refers to an expression, equation, function or polynomial of only one variable.

Bivariate – Bivariate data is the data that contains two variables.

Multivariate – It involves more than one variable.

CONTINUOUS VARIABLE-:

Continuous variables is one which can have infinite number of different values between two given points. For Eg: There cannot be a continuous scale of children within a family. If height were being measured though, the variables would be continuous as there are an unlimited number of possibilities even if only looking at between 1 and 1.1 meters.

It can be divided into two categories :-

1)    Continuous Continuous Variable

2)    Discrete Continuous Variable

It is important to remember that discrete and continuous variables are so grouped based on the scale used to measure them and what is being measured. In most scientific experiments, a discrete scale is used to measure both discrete and continuous variables. Because there are an infinite amount of possibilities, this means that the measurements of continuous variables are often rounded off to make the data easier to work with.

For Eg:- During an experiment, the scientist often wants to observe the results of changing one variable. Only one variable is often changed, as it would be difficult to determine what had caused the relevant response if multiple variables were influenced.

CATEGORY VARIABLE-:

A category variable is a variable that can take on one of a limited, and usually fixed, number of possible values. Category variables are often used to represent categorical data. Commonly the word level is used to refer to one of the possible values of a categorical variable.
Examples of values that might be represented in a category variable:

• The blood type of a person: A, B, AB or O.
• The state that a resident of India lives in.
• The political party that a voter in a India might vote for: Congress, BJP, DMK, CPIM etc.
• The type of a rock: igneous, sedimentary or metamorphic.

Data Measurement-

The appropriateness of the data analysis depends on the level of measurement of the data gathered. The commonly used levels of data measurement are as follows

·        Nominal- A variable is said to be  nominal when its values represent categories with no intrinsic ranking .Examples are zip code ,last four digits of mobile numbers etc.

·        Ordinal - A variable is said to be ordinal when its values represent categories with some intrinsic ranking. Examples  are vehicle numbers

·        Interval - A interval variable is a measurement where the difference between two values is meaningful .Example of an interval is the difference between a temperature of 100 degrees and 90 degrees is the same difference as between 90 degrees and 80 degrees

·        Ratio-The ratio is termed  as the relation between two similar magnitudes with respect to the number of times the first contains the second.

·        Scale-A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.

TECHNIQUES OF REPRESENTATION OF DATA-:

·        Bubble Chart –  Bubble chart is a diverse version of a Scatter chart where data points are replaced with bubbles. Bubbles charts are generally used for presentation of financial data. Bubbles of different sizes depending upon the value are used for visual representation of data.

·        Boxplot –  A Boxplot is a technique of summarizing a set of data on an interval scale. It is used to show the shape of the distribution, its central value and variability. Data is depicted through quartiles. The spacings between the different parts of the box help indicate the degree of dispersion (spread) and skewness in the data, and identify outliers.

These are some of the key concepts which we were introduced to during the first two sessions of our "Applied Business Statistics" lecture. It leaves us with a desire to get further insight into the course.

Name :- Poulami Sarkar

Group Members :- Pawan Agarwal

                               Pragya Singh

                               Poulami Sarkar

                               Priyanka Doshi

                               Nilay Kohaley