16: F.4. Types of Data Analytics

Question 1

What is the residual in regression analysis?

Answer 1

it’s the difference between the actual result and the predicted results, its the error between them

Question 2

Match these types of Data analytics

1- Decriptive Analytics
2- Diagnostic Analytics
3- predictive Analytics
4- prespivtive Analytics

A- What is likely to happen, its see the future outcomes on patterns, predict the future trends and behaviour

B- What should we do?, recumended actions to deal with future secnarious,

C- Why did it happen?, investigate the reasone behaind the result,

D- What happend?, summarize and see the past events using raw data

Answer 2

A- Decriptive Analytics —–> What happend?, summarize and see the past events using raw data

here we see what happend in the past using dashboards, KPI’s, charts and so on

B- Diagnostic Analytics —-> Why did it happen?, investigate the reasone behaind the result,

here we see the corelation between the variables, to explore the causes and the relationships

C- predictive Analytics —-> what is likely to happen, its see the future outcomes on patterns, predict the future trends and behaviour

here we are trying to predect the trends and behaviour using statical modeling, machin learning, and forcasting

D- prespivtive Analytics —-> What should we do?, recumended actions to deal with future secnarious,

here we use it for decition making with the best posible outcomes, optimize algorithems, secnarios planing and so on

Question 3

What is the types of Analytical modeling?

Answer 3

1- Regretion analysis
A- linear regrestuion
B- Multiple regresiton
C- Logistic regrestion

2- Time series analysis: forcasting over time, finds trends overtime, like forcasting next monthe sales

3- Sensitivity Analysis: see how sensitive the outcomes are in chaging one invput variables. like if we increase the price by 10% how dose it affect the profit

4- Simulation Analysis or model: we use this to imitate real-world process by testing real input companation
Let’s say you want to simulate your profit based on:
Different selling prices
Different demand levels
Different cost of raw materials

The simulation model will:
Randomly generate combinations of these values
Calculate the result (e.g., profit) each time
Run this 1000+ times

Show you:
Best-case
Worst-case
Most likely outcome

5- What if analysis: use to test different hypotheses analysis, like what if we reduce the the cost by 15% how dose it affect the profit?

Note: what if analysis is difference from sensitivity analysis, What-if analysis is broader or used for bigger setuatin, like how our financial statement will look if we buy this company, sensitivity analysis is more specific its see how these variables affect the demand for example or it could be one variable but the more variables the best sensitivity analysis measre