Session 1: Introduction about Statistics

          As we heard the word statistics figures, calculatons, various long mathematical formulas strike our mind at the first sight but the overview we got about the subject was entirely different than the perception we have already made about it.

          Why volume of coke can is 330ml,instead of being in some round figure? Why there is difference in position of buttons on shirts of males and females, etc.

           

          Statistics: Science of collection, presentation, analysis, and reasonable interpretation of data. Statistics presents a rigorous scientific method for gaining insight into data. For example, suppose we measure the weight of 100 patients in a study. With so many measurements, simply looking at the data fails to provide an informative account. However statistics can give an instant overall picture of data based on graphical presentation or numerical summarization irrespective to the number of data points. Besides data summarization, another important task of statistics is to make inference and predict relations of variables.

It is the taxonomy of statistics. We had learned it in the class. But we learned about only Descriptive Methods in detail.

The following types of analysis were classified:

(I) Univariate (analysis of single variable):  As the name suggests,  there is only one variable included in analysis here .

E.g. Age, graph.etc

(II) Bivariate(simultaneous analysis of two variables) :  It involves the analysis of two  variables for the purpose of determining the empirical relationship between them.

(III) Multivariate(simultaneous analysis of three or more variables). It involves analysis of more than one variable.

Types of variables:

Categorical and Continuous Variables

Categorical variables are also known as discrete or qualitative variables.

Any variable that is not quantitative is categorical. Categorical variables take a value that is one of several possible categories. As naturally measured, categorical variables have no numerical meaning. Examples: Hair color, gender, field of study, college attended, political affiliation, status of disease infection.

 Categorical variables can be further categorized as either Nominal,Ordinal,Interval, Ratio.

• Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows. So "type of property" is a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. Another example of a nominal variable would be classifying where people live in the USA by state. In this case there will be many more levels of the nominal variable (50 in fact).
• Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or ranked. So if you asked someone if they liked the policies of the Democratic Party and they could answer either "Not very much", "They are OK" or "Yes, a lot" then you have an ordinal variable. Why? Because you have 3 categories, namely "Not very much", "They are OK" and "Yes, a lot" and you can rank them from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the levels, we cannot place a "value" to them; we cannot say that "They are OK" is twice as positive as "Not very much" for example.
• Interval variables are variables for which their central characteristic is that they can be measured along a continuum and they have a numerical value (for example, temperature measured in degrees Celsius or Fahrenheit). So the difference between 20C and 30C is the same as 30C to 40C. However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable.
• Ratio variables are interval variables, but with the added condition that 0 (zero) of the measurement indicates that there is none of that variable. So, temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0C does not mean there is no temperature. However, temperature measured in Kelvin is a ratio variable as 0 Kelvin (often called absolute zero) indicates that there is no temperature whatsoever. Other examples of ratio variables include height, mass, distance and many more. The name "ratio" reflects the fact that you can use the ratio of measurements. So, for example, a distance of ten meters is twice the distance of 5 meters.
  PLANS & PRICING

Continuous variables are also known as quantitative variables. Continuous variables can be further categorized as either interval or ratio variables.

• Continuous continuous variable: They can have fractional values. eg. salary, interest rate etc.
•  Discrete continuous variable: Cannot have fractional values. eg. If we flip a coin and count the heads, the number would be any integer value between zero and +infinity.

References:
Wikipedia
Applied Business Statistics by Ken Black


Written by: Abhishek Panwala

Group Members:
Poorva Saboo

Raghav Kabra

Parita Mandhana

Pareena Neema