Question

Please explain :

A. Review the Elements of Data Style. Categorize the analysis (or analyses) (descriptive, exploratory, inferential, predictive, causal, or mechanistic). Explain the reasoning behind your choices.

B. Address context, resources, and audience of the analysis in the article that you chose

Answer 1

A. Elements of Data style-

1. Descriptive- This is an abstract of the whole data without any further evaluation. If we take Indian census as an example the complete data is collected from the people on the basis of population method . Data is taken on type of residence,region,age,sex,literacy,income status etc. This descriptive data is useful to the governments to prepare the plans and welfare schemes for the people.

2. Exploratory- This step of exploratory is built on the descriptive analysis.For example the discovery of COVID -19 vaccine . Vaccine has been discovered by various medical organisations of Russia, America , Chinese . But to give confirmation on the discovered vaccine, different clinical trials are being performed by research institutes to confirm the usage of the vaccine and further production of the vaccine in clinical laboratories.

3. Inferential- This data is something exceeding the exploratory data. This data is based on usual statistics. The descriptive and exploratory data is availed in this inferential analysis.For example-water pollution For example water pollution in an area is interlinked with the health of the people. Water pollution increases in the number of bacterial diseases. This in turn infers on the immunity and the sickness of the general public. The data of the public health is collectd by the local bodies and health department along with the Ministry of Health who take preventive and controlling measures on the basis of reports. Research and experiments are done to check out if the relationship exists between the data collected and the existence of bacterial diseases.

4. Predictive- An inferential data analyses the relationships but predictive data analyses using a set of meausrements. Nowadays business organisations depend on prediction of competetion and prediction of fluctuations. Then only it is easy to face market competition and price fluctuations. Fashions & passion of the people keep changing regularly, so the manufacturers should be ready to grab the opportunity of marketing the products. So, for this purpose predictions are useful .

5. Causal- Causal data tells us about the effect on one when there is a change in the other.For example, when a clinical trial is performed on a plant suffering from disease is transplanted to a healthy group of plants it results in the infected plants outcome.So, unlike predictive or inferential data, causal data analyses both the effect and outcome between the two variables.

6. Mechanistic – Causal data identifies the relation between the two i,e the chances of effect only but not mandatory change. Whereas, mechanistic analysis expresses the impact of one stimulus how it leads to acomplete change or influence .For example, people who are alcoholic are prone to 100 percent chances of liver damage.This is extremely challenging and life risking. So, this impact of drinking on the health is nothing but mechanistic analysis.

B. The context and resources of my choice are of general life examples.