• Users Online: 142
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

 Table of Contents  
Year : 2018  |  Volume : 4  |  Issue : 1  |  Page : 33-36

Linear regression analysis study

Department of Anthropology, University of Delhi, New Delhi, India

Date of Web Publication4-May-2018

Correspondence Address:
Khushbu Kumari
Department of Anthropology, University of Delhi, New Delhi
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpcs.jpcs_8_18

Rights and Permissions

Linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. Linear regression measures the association between two variables. It is a modeling technique where a dependent variable is predicted based on one or more independent variables. Linear regression analysis is the most widely used of all statistical techniques. This article explains the basic concepts and explains how we can do linear regression calculations in SPSS and excel.

Keywords: Continuous variable test, excel and SPSS analysis, linear regression

How to cite this article:
Kumari K, Yadav S. Linear regression analysis study. J Pract Cardiovasc Sci 2018;4:33-6

How to cite this URL:
Kumari K, Yadav S. Linear regression analysis study. J Pract Cardiovasc Sci [serial online] 2018 [cited 2023 Mar 22];4:33-6. Available from: https://www.j-pcs.org/text.asp?2018/4/1/33/231939

  Introduction Top

The concept of linear regression was first proposed by Sir Francis Galton in 1894. Linear regression is a statistical test applied to a data set to define and quantify the relation between the considered variables. Univariate statistical tests such as Chi-square, Fisher's exact test, t-test, and analysis of variance (ANOVA) do not allow taking into account the effect of other covariates/confounders during analyses (Chang 2004). However, partial correlation and regression are the tests that allow the researcher to control the effect of confounders in the understanding of the relation between two variables (Chang 2003).

In biomedical or clinical research, the researcher often tries to understand or relate two or more independent (predictor) variables to predict an outcome or dependent variable. This may be understood as how the risk factors or the predictor variables or independent variables account for the prediction of the chance of a disease occurrence, i.e., dependent variable. Risk factors (or dependent variables) associate with biological (such as age and gender), physical (such as body mass index and blood pressure [BP]), or lifestyle (such as smoking and alcohol consumption) variables with the disease. Both correlation and regression provide this opportunity to understand the “risk factors-disease” relationship (Gaddis and Gaddis 1990). While correlation provides a quantitative way of measuring the degree or strength of a relation between two variables, regression analysis mathematically describes this relationship. Regression analysis allows predicting the value of a dependent variable based on the value of at least one independent variable.

In correlation analysis, the correlation coefficient “r” is a dimensionless number whose value ranges from −1 to +1. A value toward −1 indicates inverse or negative relationship, whereas towards +1 indicate a positive relation. When there is a normal distribution, the Pearson's correlation is used, whereas, in nonnormally distributed data, Spearman's rank correlation is used.

The linear regression analysis uses the mathematical equation, i.e., y = mx + c, that describes the line of best fit for the relationship between y (dependent variable) and x (independent variable). The regression coefficient, i.e., r2 implies the degree of variability of y due to x.[1],[2],[3],[4],[5],[6],[7],[8]

  Significance of Linear Regression Top

The use of linear regression model is important for the following reasons:

  1. Descriptive – It helps in analyzing the strength of the association between the outcome (dependent variable) and predictor variables
  2. Adjustment – It adjusts for the effect of covariates or the confounders
  3. Predictors – It helps in estimating the important risk factors that affect the dependent variable
  4. Extent of prediction – It helps in analyzing the extent of change in the independent variable by one “unit” would affect the dependent variable
  5. Prediction – It helps in quantifying the new cases.

  Assumptions for Linear Regression Top

The underlying assumptions for linear regression are:

  1. The values of independent variable “x” are set by the researcher
  2. The independent variable “x” should be measured without any experimental error
  3. For each value of “x,” there is a subpopulation of “y” variables that are normally distributed up and down the Y-axis [Figure 1]
  4. The variances of the subpopulations of “y” are homogeneous
  5. The mean values of the subpopulations of “y” lie on a straight line, thus implying the assumption that there exists a linear relation between the dependent and the independent variables
  6. All the values of “y” are independent from each other, though dependent on “x.”
Figure 1: Scatter plot of systolic blood pressure versus age.

Click here to view

  Coefficient of Determination, R2 Top

The coefficient of determination is the portion of the total variation in the dependent variable that can be explained by variation in the independent variable(s). When R2 is + 1, there exists a perfect linear relationship between x and y, i.e., 100% of the variation in y is explained by variation in x. When it is 0< R2<1, there is a weaker linear relationship between x and y, i.e., some, but not all of the variation in y is explained by variation in x.

  Linear Regression in Biological Data Analysis Top

In biological or medical data, linear regression is often used to describe relationships between two variables or among several variables through statistical estimation. For example, to know whether the likelihood of having high systolic BP (SBP) is influenced by factors such as age and weight, linear regression would be used. The variable to be explained, i.e., SBP is called the dependent variable, or alternatively, the response variables that explain it age, weight, and sex are called independent variables.

  How to Calculate Linear Regression? Top

Linear regression can be tested through the SPSS statistical software (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.) in five steps to analyze data using linear regression. Following is the procedure followed [Table 1], [Table 2], [Table 3], [Table 4]:
Table 1: SPSS table

Click here to view
Table 2: SPSS output with R2

Click here to view
Table 3: Analysis of variance with P

Click here to view
Table 4: SPSS equation variables

Click here to view

Click Analyze > Regression > Linear > then select Dependent and Independent variable > OK (enter).

Example 1 – Data (n = 55) on the age and the SBP were collected and linear regression model would be tested to predict BP with age. After checking the normality assumptions for both variables, bivariate correlation is tested (Pearson's correlation = 0.696, P < 0.001) and a graphical scatter plot is helpful in that case [Figure 2].
Figure 2: Starting Data Analysis ToolPak. Click the OFFICE button and choose Excel options.

Click here to view

Now to check the linear regression, put SBP as the dependent and age as the Independent variable.

This indicates the dependent and independent variables included in the test.

Pearson's correlation between SBP and age is given (r = 0.696). R2 = 0.485 which implies that only 48.5% of the SBP is explained by the age of a person.

The ANOVA table shows the “usefulness” of the linear regression model with P < 0.05.

This provides the quantification of the relationship between age and SBP. With every increase of 1 year in age, the SBP (on the average) increases by 1.051 (95% confidence interval 0.752–1.350) units, P < 0.001. The constant here has no “practical” meaning as it gives the value of the SBP when age = 0.

Further, if more than one independent variable is added, the linear regression model would adjust for the effect of other dependent variables when testing the effect of one variable.

Example 2 – If we want to see the genetic effect of variables, i.e., the effect of increase in per allele dose of any genetic variant (mutation) on the disease or phenotype, linear regression is used in a similar way as described above. The three genotypes, i.e., normal homozygote AA, heterozygote AB and homozygote mutant BB may be coded as 1, 2, and 3, respectively. The test may be preceded, and in a similar way, the unstandardized coefficient (β) would explain the effect on the dependent variable with per allele dose increase.

Example 3 – Using Excel to see the relationship between sale of medicine with the price of the medicine and TV advertisements.

[Table 5] contains data which can be entered into an Excel sheet. Follow instructions as shown in [Figure 2], [Figure 3], [Figure 4].
Table 5: Excel data set

Click here to view
Figure 3: The Tool Pak. Choose Add Ins > Choose Analysis ToolPak and select Go.

Click here to view
Figure 4: The regression screen. Choose Data > Data Analysis > Regression. Input y Range: A1:A8. Input X Range: B1:C8. Check Labels, Residuals, Output Range as A50.

Click here to view

As shown in [Table 6], Multiple R is the Correlation Coefficient, where 1 means a perfect correlation and zero means none. R Square is the coefficient of determination which here means that 92% of the variation can be explained by the variables. Adjusted R square adjusts for multiple variables and should be used here. here. [Table 7] shows how to create a linear regression equation from the data.
Table 6: Summary output

Click here to view
Table 7: Analysis of variance

Click here to view

  Conclusion Top

The techniques for testing the relationship between two variables are correlation and linear regression. Correlation quantifies the strength of the linear relationship between a pair of variables, whereas regression expresses the relationship in the form of an equation. In this article, we have used simple examples and SPSS and excel to illustrate linear regression analysis and encourage the readers to analyze their data by these techniques.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Schneider A, Hommel G, Blettner M. Linear regression analysis: Part 14 of a series on evaluation of scientific publications. Dtsch Arztebl Int 2010;107:776-82.  Back to cited text no. 1
Freedman DA. Statistical Models: Theory and Practice. Cambridge, USA: Cambridge University Press; 2009.  Back to cited text no. 2
Chan YH. Biostatistics 201: Linear regression analysis. Age (years). Singapore Med J 2004;45:55-61.  Back to cited text no. 3
Chan YH. Biostatistics 103: Qualitative data – Tests of independence. Singapore Med J 2003;44:498-503.  Back to cited text no. 4
Gaddis ML, Gaddis GM. Introduction to biostatistics: Part 6, correlation and regression. Ann Emerg Med 1990;19:1462-8.  Back to cited text no. 5
Mendenhall W, Sincich T. Statistics for Engineering and the Sciences. 3rd ed. New York: Dellen Publishing Co.; 1992.  Back to cited text no. 6
Panchenko D. 18.443 Statistics for Applications, Section 14, Simple Linear Regression. Massachusetts Institute of Technology: MIT OpenCourseWare; 2006.  Back to cited text no. 7
Elazar JP. Multiple Regression in Behavioral Research: Explanation and Prediction. 2nd ed. New York: Holt, Rinehart and Winston; 1982.  Back to cited text no. 8


  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]

This article has been cited by
1 Study on early accurate diagnosis and treatment of COVID -19 with smart phone tracking using bionics
Shweta Gupta, Adesh Kumar
[Pubmed] | [DOI]
2 EBHI: A new Enteroscope Biopsy Histopathological H&E Image Dataset for image classification evaluation
Weiming Hu, Chen Li, Md Mamunur Rahaman, Haoyuan Chen, Wanli Liu, Yudong Yao, Hongzan Sun, Marcin Grzegorzek, Xiaoyan Li
Physica Medica. 2023; 107: 102534
[Pubmed] | [DOI]
3 The role of Chinese products demand and supply in reducing market cost and improving technological performance: Empirical evidence from South Africa, Nigeria, and Egypt
Oluwole Nurudeen Omonijo, Zhang Yunsheng
Cogent Business & Management. 2023; 10(1)
[Pubmed] | [DOI]
4 Application of novel hybrid machine learning systems and radiomics features for non-motor outcome prediction in Parkinson’s disease
Mohammad R Salmanpour, Mahya Bakhtiyari, Mahdi Hosseinzadeh, Mehdi Maghsudi, Fereshteh Yousefirizi, Mohammad M Ghaemi, Arman Rahmim
Physics in Medicine & Biology. 2023; 68(3): 035004
[Pubmed] | [DOI]
5 Design and Performance Analyses of Evacuated U-Tube Solar Collector Using Data-Driven Machine Learning Models
Astarag Mohapatra, P. K. S. Tejes, Chatur Gembali, B. Kiran Naik
Journal of Solar Energy Engineering. 2023; 145(1)
[Pubmed] | [DOI]
6 Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative Filtering
Samin Poudel, Marwan Bikdash
Big Data Mining and Analytics. 2023; 6(1): 1
[Pubmed] | [DOI]
7 Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images
Neeraj Venkatasai L. Appari, Mahendra G. Kanojia
International Journal of Hybrid Intelligent Systems. 2022; : 1
[Pubmed] | [DOI]
8 Prediction of Survivability for Open-Source with Supervised Machine Learning
Sohee Park, Ryeonggu Kwon, Gihwon Kwon
The Journal of Korean Institute of Information Technology. 2022; 20(12): 167
[Pubmed] | [DOI]
9 An Application of Fuzzy Multiple Linear Regression in Biological Paradigm
Saima Mustafa, Shumaila Ghaffar, Murrium Bibi, Muhammad Ghaffar Khan, Qaisara Praveen, Harish Garg, Mahamane Saminou, Zakia Hammouch
Complexity. 2022; 2022: 1
[Pubmed] | [DOI]
10 Modelling and Forecasting Fresh Agro-Food Commodity Consumption Per Capita in Malaysia Using Machine Learning
Rayner Alfred, Christylyn Leikson, Bonaventure Boniface, Geoffrey Harvey Tanakinjal, Assis Kamu, Mori Kogid, Stephen L. Sondoh, Nolila Mohd Nawi, Nalini Arumugam, Ryan Macdonell Andrias, Mohammed Shuaib
Mobile Information Systems. 2022; 2022: 1
[Pubmed] | [DOI]
11 The relationship between school-age children’s interoceptive awareness and executive functioning: An exploratory study
Caitlin Bishop, Ted Brown, Mong-Lin Yu
British Journal of Occupational Therapy. 2022; : 0308022622
[Pubmed] | [DOI]
12 Strategic planning in secondary schools in Rangwe sub-county, Kenya: Influence on student learning outcomes
John James Juma, Milcah Nyaga, Zachary N. Ndwiga
Management in Education. 2022; : 0892020622
[Pubmed] | [DOI]
13 Regional scale analysis of land cover dynamics in Kerala over last two decades through MODIS data and statistical techniques
Vijith H., Ninu Krishnan MV., Alhassan Sulemana
Journal of Environmental Studies and Sciences. 2022;
[Pubmed] | [DOI]
14 Assessing land erosion and accretion dynamics and river bank line shifting of upper reach of Hooghly river of West Bengal, India
Abhijit Paul, Manjari Bhattacharji
Sustainable Water Resources Management. 2022; 8(5)
[Pubmed] | [DOI]
15 Recent trends of smart agricultural systems based on Internet of Things technology: A survey
Dunia Abas Gzar, Ali Majeed Mahmood, Maythem Kamal Abbas Al-Adilee
Computers and Electrical Engineering. 2022; 104: 108453
[Pubmed] | [DOI]
16 Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy
Sruthi Sivabhaskar, Ruiqi Li, Arkajyoti Roy, Neil Kirby, Mohamad Fakhreddine, Nikos Papanikolaou
Journal of Applied Clinical Medical Physics. 2022;
[Pubmed] | [DOI]
17 Investigation on Viscosity Behavior of Anionic Polyacrylamide Copolymer in Brine Solutions for Slickwater Fluids Applications at High Salinity and Hardness Conditions
Dileep Kumar Balaga, Navneeth Kumar Korlepara, Aditya Vyas, Sandeep D. Kulkarni
Journal of Energy Resources Technology. 2022; 144(11)
[Pubmed] | [DOI]
18 COVID-19 outbreak data analysis and prediction
R. Anandhan, T. Nalini, Shwetambari Chiwhane, M. Shanmuganathan, P. Radhakrishnan
Measurement: Sensors. 2022; : 100585
[Pubmed] | [DOI]
19 Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR)
Fatima Ezzahra Yatim, Imane Boumanchar, Bousalham Srhir, Younes Chhiti, Charafeddine Jama, Fatima Ezzahrae M'hamdi Alaoui
Waste Management. 2022; 153: 293
[Pubmed] | [DOI]
20 Data-driven approach towards identifying dyesensitizer molecules for higher power conversion efficiency in solar cells
Ganapathi Rao Kandregula, Dhinesh Kumar Murugaiah, N. Arul Murugan, Kothandaraman Ramanujam
New Journal of Chemistry. 2022;
[Pubmed] | [DOI]
21 Application of MLR-PRN model for estimation of arsenic concentration in drinking water: a case study for Izmir City
Serdar Gündogdu
Urban Water Journal. 2022; : 1
[Pubmed] | [DOI]
22 Factors influencing accounting research output in South Africa’s universities of technology
Mzwandile Mbambo, Odunayo Olarewaju, Thabiso Sthembiso Msomi, Collins G. Ntim
Cogent Business & Management. 2022; 9(1)
[Pubmed] | [DOI]
23 Primary and secondary cardiac tumors: clinical presentation, diagnosis, surgical treatment, and results
Alessio Campisi, Angelo Paolo Ciarrocchi, Nizar Asadi, Andrea Dell’Amore
General Thoracic and Cardiovascular Surgery. 2022;
[Pubmed] | [DOI]
24 A Review on Drought Index Forecasting and Their Modelling Approaches
Yi Xun Tan, Jing Lin Ng, Yuk Feng Huang
Archives of Computational Methods in Engineering. 2022;
[Pubmed] | [DOI]
25 Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models
Jayanta Kumar Basak, Bhola Paudel, Na Eun Kim, Nibas Chandra Deb, Bolappa Gamage Kaushalya Madhavi, Hyeon Tae Kim
Agronomy. 2022; 12(10): 2487
[Pubmed] | [DOI]
26 Feature–Classifier Pairing Compatibility for sEMG Signals in Hand Gesture Recognition under Joint Effects of Processing Procedures
Mohammed Asfour, Carlo Menon, Xianta Jiang
Bioengineering. 2022; 9(11): 634
[Pubmed] | [DOI]
27 A Quantitative Study of the Impact of Organizational Culture, Communication Management, and Clarity in Project Scope on Constructions’ Project Success with Moderating Role of Project Manager’s Competencies to Enhance Constructions Management Practices
Muhammad Muneer, Nawar Khan, Muhammad Awais Hussain, Zhang Shuai, Adnan Ahmad Khan, Rashid Farooq, Muhammad Aamir Moawwez, Muhammad Atiq Ur Rehman Tariq
Buildings. 2022; 12(11): 1856
[Pubmed] | [DOI]
28 Analyzing Greece 2010 Memorandum’s Impact on Macroeconomic and Financial Figures through FCM
Stavros P. Migkos, Damianos P. Sakas, Nikolaos T. Giannakopoulos, Georgios Konteos, Anastasia Metsiou
Economies. 2022; 10(8): 178
[Pubmed] | [DOI]
29 Effect of Money Supply, Population, and Rent on Real Estate: A Clustering Analysis in Taiwan
Cheng-Hong Yang, Borcy Lee, Yu-Da Lin
Mathematics. 2022; 10(7): 1155
[Pubmed] | [DOI]
30 Review of Methods, Applications and Publications on the Approximation of Piecewise Linear and Generalized Functions
Sergei Aliukov, Anatoliy Alabugin, Konstantin Osintsev
Mathematics. 2022; 10(16): 3023
[Pubmed] | [DOI]
31 Re-orientation and simple understanding of regression analysis for student nurses: When and why to use
Anindita Mandal, SureshK Sharma
Indian Journal of Continuing Nursing Education. 2022; 0(0): 0
[Pubmed] | [DOI]
32 Forecasting Liquidated Damages via Machine Learning-Based Modified Regression Models for Highway Construction Projects
Odey Alshboul, Mohammad A. Alzubaidi, Rabia Emhamed Al Mamlook, Ghassan Almasabha, Ali Saeed Almuflih, Ali Shehadeh
Sustainability. 2022; 14(10): 5835
[Pubmed] | [DOI]
33 Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling
Samin Poudel, Marwan Bikdash
Big Data Mining and Analytics. 2022; 5(3): 192
[Pubmed] | [DOI]
34 Optimizing the Tolerance for the Products with Multi-Dimensional Chains via Simulated Annealing
Chen-Kun Tsung
Symmetry. 2021; 13(10): 1780
[Pubmed] | [DOI]
35 A simple method for correction of the systematic error in calculating biological age by the multiple regression equation
Anatoly Pisaruk
Ageing & Longevity. 2021; (1 2021): 26
[Pubmed] | [DOI]
36 A simple method for correction of the systematic error in calculating biological age by the multiple regression equation
Anatoly Pisaruk
Ageing & Longevity. 2021; (1 2021): 26
[Pubmed] | [DOI]
37 Alternative mathematical method for calculating biological age
Anatoly Pisaruk
Ageing & Longevity. 2021; (2): 1
[Pubmed] | [DOI]
38 Determining the relation between the count number and x-ray energy level in pyroelectric materials using linear regression analysis
Saadet Sena Egeli, Yalcin Isler
Journal of Intelligent Systems with Applications. 2021; : 58
[Pubmed] | [DOI]
39 A New Nanomaterial Based Biosensor for MUC1 Biomarker Detection in Early Diagnosis, Tumor Progression and Treatment of Cancer
Fulden Ulucan-Karnak,Sinan Akgöl
Nanomanufacturing. 2021; 1(1): 14
[Pubmed] | [DOI]
40 Influence of Financial Variables on the Development of Rural Communes of Eastern Poland in 2009–2018
Andrzej Pawlik,Pawel Dziekanski,Jaroslaw W. Przybytniowski
Risks. 2021; 9(8): 145
[Pubmed] | [DOI]
41 Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study
Baozhong Wang,Jyotsna Sharma,Jianhua Chen,Patricia Persaud
Energies. 2021; 14(4): 1052
[Pubmed] | [DOI]
42 Multi-Objective Optimization of WEDM of Aluminum Hybrid Composites Using AHP and Genetic Algorithm
Amresh Kumar,Neelkanth Grover,Alakesh Manna,Raman Kumar,Jasgurpreet Singh Chohan,Sandeep Singh,Sunpreet Singh,Catalin Iulian Pruncu
Arabian Journal for Science and Engineering. 2021;
[Pubmed] | [DOI]
43 Prediction Mechanisms to Improve 5G Network User Allocation and Resource Management
Christos Bouras,Rafail Kalogeropoulos
Wireless Personal Communications. 2021;
[Pubmed] | [DOI]
44 Deceleration of the development of city gas connections amidst the covid-19 pandemic in the metropolitan area
A Prima,O Ridaliani,A Hamid,H Pramadika,H P Sanusi,A Rinanti
IOP Conference Series: Earth and Environmental Science. 2021; 802(1): 012014
[Pubmed] | [DOI]
45 Prospect of coal-based methanol market in Indonesia
T Suseno, D F Umar
IOP Conference Series: Earth and Environmental Science. 2021; 882(1): 012073
[Pubmed] | [DOI]
46 Empirical analysis of regression techniques by house price and salary prediction
U Bansal,A Narang,A Sachdeva,I Kashyap,S P Panda
IOP Conference Series: Materials Science and Engineering. 2021; 1022: 012110
[Pubmed] | [DOI]
47 The role of predictive analytics to explain the employability of management graduates
Ramakrishnan Raman, Dhanya Pramod
Benchmarking: An International Journal. 2021; ahead-of-p(ahead-of-p)
[Pubmed] | [DOI]
48 Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support model
Odey Alshboul, Ali Shehadeh, Maha Al-Kasasbeh, Rabia Emhamed Al Mamlook, Neda Halalsheh, Muna Alkasasbeh
Engineering, Construction and Architectural Management. 2021; ahead-of-p(ahead-of-p)
[Pubmed] | [DOI]
49 Applying machine learning approach to predict students’ performance in higher educational institutions
Mohammed Nasiru Yakubu, A. Mohammed Abubakar
Kybernetes. 2021; ahead-of-p(ahead-of-p)
[Pubmed] | [DOI]
50 Role of COVIDsafe app and control measures in Australia in combating COVID-19 pandemic
Hafiz Syed Mohsin Abbas, Xiaodong Xu, Chunxia Sun
Transforming Government: People, Process and Policy. 2021; 15(4): 708
[Pubmed] | [DOI]
51 Instructional leadership as a controlling function in secondary schools in Rangwe Sub County, Kenya: Influence on students’ learning outcomes
John James Juma, Zachary N Ndwiga, Milcah Nyaga
Educational Management Administration & Leadership. 2021; : 1741143221
[Pubmed] | [DOI]
52 The influence of culture on the development of youth entrepreneurs in a selected suburb in Cape Town
Nashwin Davids,Robertson Tengeh,Rodney Duffett
EUREKA: Social and Humanities. 2021; (2): 24
[Pubmed] | [DOI]
53 Implementation of Data-Driven Machine Learning Models for Design and Performance Optimization of Evacuated U-Tube Solar Collector
B. Kiran Naik, Astarag Mohapatra, P. K. S. Tejes, Chatur Gembali
SSRN Electronic Journal. 2021;
[Pubmed] | [DOI]
54 Nanotoxic Effects of Silver Nanoparticles on Normal HEK-293 Cells in Comparison to Cancerous HeLa Cell Line
Xiongwei Liu,Kuizhong Shan,Xiaxia Shao,Xianqing Shi,Yun He,Zhen Liu,Joe Antony Jacob,Lichun Deng
International Journal of Nanomedicine. 2021; Volume 16: 753
[Pubmed] | [DOI]
55 Modeling and parameter optimization of the papermaking processes by using regression tree model and full factorial design
TAPPI Journal. 2021; 20(2): 123
[Pubmed] | [DOI]
56 Prospects for the creation and use of paired and multiple correlation and regression models in beekeeping
O. Galatiuk,A. Lakhman,T. Romanishina,V. Behas
Naukovij věsnik veterinarnoď medicini. 2021; (1(165)): 58
[Pubmed] | [DOI]

Intention to Screen for Cervical Cancer Among Child Bearing Age Women in Bahir Dar City, North-West Ethiopia: Using Theory of Planned Behavior

Wallelign Alemnew, Getu Debalkie, Telake Azale
International Journal of Women's Health. 2020; Volume 12: 1215
[Pubmed] | [DOI]
58 An Economic Analysis on Years of Schooling of the Children Related to Financial Support from Family and Govt. & Non-Govt. Institutions
Vartika Tanania, Shipra Shukla, Shambhavi Singh
British Journal of Arts and Humanities. 2020; : 665
[Pubmed] | [DOI]
59 Estimation of Moisture Content in XLPE Insulation in Medium Voltage Cable by Frequency Domain Spectroscopy
A. K. Das,N. Haque,A. K. Pradhan,S. Dalai,B. Chatterjee,A. Mukherjee
IEEE Transactions on Dielectrics and Electrical Insulation. 2020; 27(6): 1811
[Pubmed] | [DOI]
60 Country’s Entrepreneurial Environment Predictors for Starting a New Venture—Evidence for Romania
Carmen Paunescu,Elisabeta Molnar
Sustainability. 2020; 12(18): 7794
[Pubmed] | [DOI]


Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  In this article
   Significance of ...
   Assumptions for ...
   Coefficient of D...
   Linear Regressio...
   How to Calculate...
   Article Figures
   Article Tables

 Article Access Statistics
    PDF Downloaded7208    
    Comments [Add]    
    Cited by others 60    

Recommend this journal