Question

Discussion question please respond By Day 3

Post a brief explanation of when an observed correlation might represent a true relationship between variables and why. Be specific and provide examples.

Below you will see a statement that was sent responding to response 1 how would you respond to response two please proive reference like response 1 this is a discussion.

Response 1:

If you collect India's steel production data and population of 10 consecutive year it will result in a very high correlation which means they have a strong linear relationship, but does it indicate that steel production causes population or vice versa..the rationale behind this phenomenon is that both population and steel production is increasing as time goes on here both the variables doesn't depend on each other, but they both are dependent on time so a high correlation never reflects a cause and effect relationship it only indicates an extent of linear interdependence(Stephanie, 2015).

Reference

Stephanie. 2018.Statistic how to. Retrieved from:http://www.statisticshowto.com/multicollinearity/

Response 2 : Thank you for your post. Correlation and linear regression are techniques utilized for examining the relationship between two quantitative variables.The goal of a correlation analysis is to see whether two measurement variables co vary, and to quantify the strength of the relationship between the variables, whereas regression expresses the relationship in the form of an equation.In regression analysis, the problem of interest is the nature of the relationship itself between the dependent variable (response) and the (explanatory) independent variable.

Answer 1

Correlation analysis is used to determine a sort of linear trend in the data: when one variable increases, does the other variable also increase, or decrease. We always try and figure out an upwards or downwards relationship between the variables. Suffice it to say that correlation analysis is done to check out the presence of a linear relationship in the data.

Linear Regression also builds on the same fundamentals as correlational analysis, but it fixes one variable as independent and other as dependent or response variable. It gives a more concrete mathematical relation which describes the best linear fit.

Source: https://www.datasciencecentral.com/profiles/blogs/difference-between-correlation-and-regression-in-statistics