Statistical Analysis

Statistical analysis is done in a conventional way. The information is presented either graphically of tabular form to describe the stories. A series of different analysis can be conducted through the use of statistics, for example, bi variate analysis analyses between 2 variables and determines the relationship between the 2 variables. A dependent variable (x) is an outcome variable of y variable. Using an example of health, health can be determined on the basis of the types of food consumed by a human and the volume of exercise done. 

• Uni variate - When you play with variables
• Bi variate - When you play with only 2 variables
• Multivariate - When you play with more than 2 variables

To be able to understand the variables even more, they are formed into a categorical format (Europe, Middle East, Far East. etc). This categories the information into groups, however, where the information is in moving trend format, then a continuous categorical form applies (years of experience with increasing age [age correlation with experience]).

When information is required to be compared with one group with another group, males are doing better than females, the use of T-Test is used, which helps understand better the correlation between x and the associates. Take for example cancer (x) and lung cancer (y) and the smoking behavior is floated and fluctuated in between. Here, we can say Y is a function of X because it changes. The "mean" is the model (which represents the behavior . If we apply this statement to the society, the government best fit the linear according to the society population best interest. A model of this format enables predictable  it can talk and provide you with ideal of the sustainability and environmental friendly reaction. 

Corefficient of determination 

The use of coefficient of determination is utilized when not being able to capture main important factors for explaining "y". The use of looking for regression estimates and it's significance whether strong or weak influence (e.g. x on y). The benefit of this helps to find out and shape the degree of influence. 

The significance will also depend on sample size (multiple linear regression), the number of variables (multi-nominal regression type) which influence each other and it is important to have a better statistical significance. The linearity of this aids the understand ability [(P-1)/P]. A more comprehensive element is the use of logit, which provides more clarity of changes between 2 groups. A logit regression (0,1) (yes, no).