Bivariate Statistical Measures

Bivariate Statistical Measures

1. Regression

   • Regression analysis is perhaps the most widely used statistical technique to investigate or estimate the relationship between dependent variables and a set of independent explanatory variables. This type of data analysis is also used as an umbrella term for a variety of data analysis techniques that are used in a qualitative research method to model and analyze many variables.
     • Dependent variable(s): It is the most important factor, which is trying to understand or predict.
     • Independent variable(s): This is the factor that you think can impact your dependent variable.

2. Correlation

   • Correlation is a type of association between two numerical variables, specifically it evaluates the trend (increasing or decreasing) in the data. Two variables are associated when one variable gives us information about the other. On the contrary, when there is no association, the increase or decrease of one variable does not tell us anything about the behavior of the other variable.
     • how to interpret
       • a positive value indicates a direct or positive relationship, a negative value indicates an indirect, inverse or negative relationship, a null value indicates that there is no trend between the two variables (it may be that there is no relationship or that the relationship is more complex than a trend, for example, a U-shaped relationship).
         • The magnitude indicates the strength of the relationship, and takes values between -1 to 1. The closer the value is to the extremes of the interval (1 or -1), the stronger the trend of the variables, or the smaller the dispersion. that exists at points around that trend. The closer the correlation coefficient is to zero, the weaker the trend, that is, there will be more dispersion in the point cloud. if the correlation is 1 or -1 we will say that the correlation is "perfect", if the correlation is 0 we will say that the variables are not correlated.

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