Simple linear regression is effectively the same thing as bivariate correlation. If this is what you're after, then you can just run a correlation matrix with all 9 variables unless you have a reason to examine the unstandardized coefficients (which in this case, you can just run 8 separate simple regression models).

Unless there's conceptual or methodological reasons to run them separately, usually multiple regression with all the explanatory variables in one single model is preferred so that you can examine the unique contributing effect of each variable controlling for the rest.

Dear Abdul-Naafik Mohammed ,

Are you referring to multiple linear regression? This has one DV and multiple IVs. It is an extension of simple linear regression and has similar assumptions. It is a good idea to begin with descriptive statistics, such as a scatter plot of your DV against each IV in turn. This will give you a feel for your data. You should look for cigar shaped distributions whic indicate that the assumptions of linear regression are probably satisfied.

There is a lot more to it than this, such as the choice of model, multicollinearity, the choice of iterative technique and model validation. Unfortunately I don't have any free resources on the subject. I would recommend the chapters in Field's and Tabachnik & Fidell's books.

if using more than 1 independent variable, we will use multiple linear regression.

Dear Abdul-Naafik Mohammed

If you are using MATLAB, try

https://in.mathworks.com/help/stats/regress.html

for Python, try

https://towardsdatascience.com/multiple-linear-regression-python-101-af459110a8af

regards,

Dear Abdul-Naafik Mohammed

It is effective. I have been doing the same thing for ship repairing activities. In one case, there were 10 independent variables for single dependent variable. You may try with "Stepwise regression technique" following "forward selection method ".

Thank you all for your answers.

I really appreciate

Maybe you can alernatively consider the recursive least squares algorithm (RLS). RLS is the recursive application of the well-known least squares (LS) regression algorithm, so that each new data point is taken in account to modify (correct) a previous estimate of the parameters from some linear (or linearized) correlation thought to model the observed system. The method allows for the dynamical application of LS to time series acquired in real-time. As with LS, there may be several correlation equations with the corresponding set of dependent (observed) variables. For RLS with forgetting factor (RLS-FF), acquired data is weighted according to its age, with increased weight given to the most recent data.

Years ago, while investigating adaptive control and energetic optimization of aerobic fermenters, I have applied the RLS-FF algorithm to estimate the parameters from the KLa correlation, used to predict the O2 gas-liquid mass-transfer, hence giving increased weight to most recent data. Estimates were improved by imposing sinusoidal disturbance to air flow and agitation speed (manipulated variables). The power dissipated by agitation was accessed by a torque meter (pilot plant). The proposed (adaptive) control algorithm compared favourably with PID. Simulations assessed the effect of numerically generated white Gaussian noise (2-sigma truncated) and of first order delay. This investigation was reported at (MSc Thesis):

Thesis Controlo do Oxigénio Dissolvido em Fermentadores para Minimi...

It is effective. Use stepwise regression technique followed by forward selection method