Examples of using Multiple linear regression in English and their translations into Indonesian
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Multiple linear regression is the most common form of the regression analysis.
If you have more than one independent variable, use multiple linear regression instead.
The analysis tool used is multiple linear regression using time series data is 2009-2014.
This shows the availabledata has been qualified using multiple linear regression model.
The analysis method used is multiple linear regression analysis model with Error Correction Model ECM.
The next table shows the multiple linear regression estimates including the intercept and the significance levels.
Of course, this is just a simple regression and there are models that you canbuild that use several independent variables called multiple linear regressions.
But multiple linear regressions are more complicated and have several issues that would need another article to discuss.
This research is a quantitative approach using multiple linear regression with the help of program applications Eviews 10.
Structural equations modelling(SEM) is the term used to describe the generalmultivariate method of conducting factor analyses, multiple linear regression analyses and path analyses.
Foster and Rahmstorf(2011) used a multiple linear regression approach to filter out the effects of volcanic and solar activity and ENSO.
The result of the study shows that there is no deviation of classical assumption, indicating that the available datahas been qualified to be used as the multiple linear regression model.
The model used in this research is to use a multiple linear regression model assuming BLUE(Best Linear Unbiased Estimate).
Censoring and non-normality, which are characteristic of survival data, generate difficulty when trying to analyze thedata using conventional statistical models such as multiple linear regression.
Regression and classification methods include MLR(multiple linear regression) and projection using the PCA and PLS(partial least squares) models.
Multiple linear regression modelling tested whether the relations between regular violent video game play(coded by researchers) and adolescents' aggressive and helping behaviours(judged by parents) were positive, negative, linear, or parabolic.
Data analysis technique used are factor analysis, multiple linear regression analysis and classical assumption were processed using SPSS version 15.00.
The statistical methods used in this study is a simple linear regression analysis to test the effect of environmentalperformance against the Corporate Social Responsibility Disclosure and multiple linear regression analysis to test the effect of environmental performance and Corporate Social Responsibility Disclosure on corporate financial performance.
Foster and Rahmstorf(2011) used a multiple linear regression approach to filter out the effects of volcanic and solar activity, and the El Niño Southern Oscillation(ENSO).
While mathematically it is feasible to apply multiple regression to discrete ordered dependent variables,some of the assumptions behind the theory of multiple linear regression no longer hold, and there are other techniques such as discrete choice models which are better suited for this type of analysis.
The data collected will be tested using multiple linear regression models, to see salaries, opportunities to progress and students' perceptions about the accountant profession about the desire of students to have a career as a qualified accountant.
This research was carried out through the analysis of multiple linear regression with a 5% significance level, Data collection tool that used is observation and literature study with purposive sampling method.
Path analysis Model of path analysis is an extension of the multiple linear regression analysis or path analysis is the use of regression analysis to estimate the causal relationship between variables(causal models) that have been previously set by the theory(Ghozali, 2007:174).
Like Foster and Rahmstorf, Lean and Rind(2008) performed a multiple linear regression on the temperature data, and found that although ENSO is responsible for approximately 12% of the observed global warming from 1955 to 2005, it actually had a small net cooling effect from 1979 to 2005.
Like Foster and Rahmstorf, Lean and Rind(2008) performed a multiple linear regression on the temperature data, and found that although ENSO is responsible for approximately 12% of the observed global warming from 1955 to 2005, it actually had a small net cooling effect from 1979 to 2005.
Like Foster and Rahmstorf, Lean and Rind(2008) performed a multiple linear regression on the temperature data, and found that although volcanic activity can account for about 10% of the observed global warming from 1979 to 2005, between 1889 and 2006 volcanic activity had a small net cooling effect on global temperatures.
Like Foster and Rahmstorf, Lean and Rind(2008) performed a multiple linear regression on the temperature data, and found that although volcanic activity can account for about 10% of the observed global warming from 1979 to 2005, between 1889 and 2006 volcanic activity had a small net cooling effect on global temperatures.
Linear regression(simple and multiple).
Another way of confirming your technical analysis linear regression signals is by looking at multiple time frames.