Regression modeling of different proteins using linear and multiple
analysis
Shruti Jain
Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan-173234, India
Network Biology
ISSN 2220-8879
http://www.iaees.org/publications/journals/nb/online-version.asp
2017
7
4
80
93
International Academy of Ecology and Environmental Sciences
Hong Kong
1 September 2017
25 September 2017
1 December 2017
linear regression analysis
multiple regression analysis
marker proteins
PLS
There are different types of regression analysis. Out of which simple regression and multiple regressions was considered in this paper. For calculation purpose we have used PLS analysis which calculates squared r values. This paper considers eleven different proteins and one output. We have validated our results by calculating adjusted regression coefficient, predicted regression coefficient regression coefficient cross validation, rm^2 and F-test values. Later multiple regressions were used as we have different independent variable (proteins). For that analysis we have calculated the coefficient, standard error, standard coefficient, tolerance, t value and p value, variation explanation of predictors and estimators which gives percentage and cumulative percentage. Correlation matrixes were also shown at the end for eleven proteins and one output.
DOI 10.0000/issn-2220-8879-networkbiology-2017-v7-0008
http://www.iaees.org/publications/journals/nb/articles/2017-7(4)/regression-modeling-of-different-proteins.pdf