You should read some papers, there are numerous papers on this questions. Normally, first u can discussed about descriptive statistics, then correlation but remember that u should not copy exact tables of SPSS out put , there are certain editing require in the table to adjust format.  

Normally you need to attach in the paper the correlation diagram, the correlation formula and the R2 factor.

You should read some papers, there are numerous papers on this questions. Normally, first u can discussed about descriptive statistics, then correlation but remember that u should not copy exact tables of SPSS out put , there are certain editing require in the table to adjust format.  

Do not use excessive significant figures....

You can find this page very valuable: https://statistics.laerd.com/spss-tutorials/linear-regression-using-spss-statistics.php

If the regression analysis was performed with the objective of obtaining an equation, you must present at least:

- The equation resulting from the adjustment of the model [(refer to the significance of the equation (results of F test) and the estimates of its parameters (results of t test)]

- The values of the goodness of fit analysis criteria: coefficient of determination, root of the mean square error (RMSE) or standard error of the esimative (Syx), bias and the graphs of residuals distribution (verify if these comply with the statistical assumptions of normality, homocedasticity and absence of serial autocorrelation) to verify, mainly, if there are no tendencies of overestimation or underestimation.

Regards !

You should report R square first, followed by whether your model is a significant predictor of the outcome variable using the results of ANOVA for Regression and then beta values for the predictors and significance of their contribution to the model.

Mohammad -

Linear (including multiple linear) regression? 

I suggest:

1) a graphical residual analysis scatterplot

2) cross-validation; minimally a few data saved (not used for model selection or estimation of regression coefficients) to check against predictions

3) estimate variance of the prediction error - which varies by prediction under heteroscedasticity, which is natural

I suggest (1) as a minimum.  It may need to go in an appendix.

See https://newonlinecourses.science.psu.edu/stat462/node/81/

"Lesson 6" is on multiple regression.

Best wishes - Jim

Dear Mohammad

Refer to the attached article on "Energy efficiency of iron–boron–silicon metallic glasses in sulfuric acid solutions".

Also, refer to the following text:

-Motulsky H and Christopoulos A 2003 Fitting Models to Biological Data Using Linear and Nonlinear Regression: a Practical Guide for Curve Fitting (San Diego, CA: GraphPad Software Inc.) (www.graphpad.com)

The above references could be useful. The 1st reference has an example of the application of non-linear regression analysis. The second reference is an excellent text on regression analysis.

Your question was rather broad. Are you talking about Linear regression, Logistic regression or Cox regression?

The type of regression will depend on the type of data and study design.

Linear regression is used to analyze numerical( continuous, discrete) data.

The regression coefficient gives the change in value of one outcome, per unit change in the other. Regression coefficient, confidence intervals and p-values are used for interpretation.

For most qualitative studies, categorical/ordinal data are used. Logistic regression is used in the analysis of predictors of outcome. Odd ratios, confidence intervals and p-values are used for interpretation.

For follow-up studies, survival analysis is used to analyze time to event depending on study design. Cox regression is used for regression analysis. Hazard ratios, confidence interval and p-values are used for interpretation.

This page will help you for reporting a multiple linear regression in apa:

https://www.slideshare.net/plummer48/reporting-a-multiple-linear-regression-in-apa

And this for reporting a single linear regression:

https://www.slideshare.net/plummer48/reporting-a-single-linear-regression-in-apa?qid=1c96ba0e-0d76-46c3-9a9d-370b20f027ed&v=&b=&from_search=2

I hope they were helpful

Our attached Ms: we had explained in detail and clearly how regression analysis (in addition to correlation, ROC) is conducted and expressed.

hi

This seems obvious, perhaps, but discuss the results that are most germane to your research question first. Then discuss "control" variables in the context of the appropriateness of the model. For example, reasonable/sensible parameter values of your control variables provide some evidence that your model is reasonable. That would mean discussing your results in the context of others' findings in the literature. Also, keep in mind how results ore generally reported in the journal you are targeting. In some journals, you see results and fit statistics discussed in much detail, but in others, the discussion is more limited.

You can do a table for all the models with a library (Stargazer) in R software. In the link, you can find an example, also you can export the table in a file .doc. https://unc-libraries-data.github.io/R-Open-Labs/Extras/Stargazer/Stargazer.html

LAERD Statistics provides a step-by-step approach to reporting regression analysis in SPSS and STATA. You can explore using the link here: https://statistics.laerd.com/spss-tutorials/multiple-regression-using-spss-statistics.php

https://www.slideshare.net/plummer48/reporting-a-single-linear-regression-in-apa?from_action=save

It depends on the aims and context of your study and what you are trying to prove. Read other papers covering this area and get ideas.

The answers has helped a lot

I have benefitted from the answers. thank you all.

Thanks. I have also benefitted.

Which type of regression: Linear (tells about presence and direction (+ increase/-ve decrease) of relation, You see B (slope) SE; Beta coeff.( degree of change in the outcome variable for every 1-unit of the predictor (nondepend); R2 >50% with less SE is acceptable. Logistic regression is categorical gives wt of influence: maybe univ- or multivariant (1- or >2 predictors&outcome).

Regression Values to report: R 2 , F value (F), degrees of freedom (numerator, denominator; in parentheses separated by a comma next to F), and significance level (p), β. Report the β and the corresponding t-test for that predictors for each predictor in the regression Example Multiple regression analysis was used to test if the personality traits significantly predicted participants' ratings of aggression. The results of the regression indicated the two predictors explained 35.8% of the variance (R2 =.38, F(2,55)=5.56, p

You should read some papers, there are numerous papers on this questions. Normally, first u can discussed about descriptive statistics, then correlation but remember that u should not copy exact tables of SPSS out put , there are certain editing require in the table to adjust format.

You should read some papers, there are numerous papers on this questions. Normally, first u can discussed about descriptive statistics, then correlation but remember that u should not copy exact tables of SPSS out put , there are certain editing require in the table to adjust format.  

I have found this article about Multiple Regression Analysis in R very useful.

https://www.sheffield.ac.uk/polopoly_fs/1.536483!/file/MASH_multiple_regression_R.pdf

You should read some papers, there are numerous papers on this questions. Normally, first u can discussed about descriptive statistics, then correlation but remember that u should not copy exact tables of SPSS out put , there are certain editing require in the table to adjust format.