Post- 2

Financial Database Management with R

 

In this post, we will be introducing some major types of analyses that you can employ to answer your financial questions. 

 

Read along, if you want to develop a basic understanding and explore the goals of the following six categories. 

  1. Descriptive

  2. Exploratory

  3. Inferential

  4. Predictive

  5. Causal

1. Descriptive analysis

What is it?

As the name suggests, descriptive analysis entails describing or summarizing a set of data. Whenever you get a new dataset to examine, this is usually the first kind of analysis you will perform. It helps to generate simple summaries about the samples and help you get a basic idea about your dataset.

Tools used:
We often use many tools used in descriptive analysis like mean, median, mode in our daily life. Some other common descriptive statistics include range, standard deviations or variance

Keep in mind:
One must always keep in mind that this type of analysis is only for SUMMARISING and not GENERALIZING the results of the analysis to a larger population.

Description of data is not equivalent to making interpretations. Interpretations require additional statistical steps which are discussed below.


 

2. Exploratory analysis

What is it?
The goal of exploratory analysis is to examine or explore the data and find relationships that weren’t previously known. Exploratory analyses explore how different measures might be related to each other but do not confirm that relationship as causative. 

 

Tools used:
Correlation Analysis is used to form these ‘connections’. 

 

Keep in mind:
“Correlation does not imply causation” 

Exploratory analyses, while useful for discovering new connections, should not be the final say in answering a question!  

Just because you observe a relationship between two variables during exploratory analysis, it does not mean that one necessarily causes the other.

 

3. Inferential analysis

What is it?
The goal of inferential analyses is to use a relatively small sample of data to infer about the population at large. It entails using a small amount of information to extrapolate and generalize that information to a larger group.

 

Tools used:
Inferential analysis typically involves using the data you have to estimate that value in the population and then give a measure of your uncertainty about your estimate.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


 

 

Keep in mind:
Accuracy in  inferring information about the larger population depends heavily on sampling scheme - if the data you collect is not from a representative sample of the population, the generalizations you infer won’t be accurate for the population.


4. Predictive analysis
What is it?
The goal of predictive analysis is to use current data to make predictions about future data. Essentially, you are using current and historical data to find patterns and predict the likelihood of future outcomes.

 

Tools used:

Regression Analysis is one tool that can be used under this analysis. In this analysis statistical equations are made to predict or estimate the impact of one variable on another. For instance, regression analysis can be used to determine how much profits a company can earn in the near future by analyzing the historic data.

Keep in mind: 

  1. Like in inferential analysis, accuracy in predictions is dependent on measuring the right variables. If you aren’t measuring the right variables to predict an outcome, your predictions aren’t going to be accurate. 

  2. Just like in exploratory analysis, just because one variable may predict another, it does not mean that one causes the other; you are just capitalizing on this observed relationship to predict the second variable.

 

5. Causal analysis

What is it?

In the analyses we’ve looked at so far is that we can only see correlations and can’t get at the cause of the relationships. Causal analysis fills that gap; the goal of causal analysis is to see what happens to one variable when we manipulate another variable - looking at the cause and effect of a relationship. Causal analysis is frequently seen  in scientific studies where the aim is to identify the cause of a phenomenon

Keep in mind:
One thing to note about causal analysis is that the data is usually analysed in aggregate and observed relationships are usually average effects; so, while on average giving a certain population a drug may alleviate the symptoms of a disease, this causal relationship may not hold true for every single affected individual.

 

 

P.S. Keep note that the ‘tool used’ section consists of ‘commonly’ used tool.