Examples of using Multiple regression in English and their translations into Indonesian
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Multiple regression analysis in Minitab.
Explain the assumptions of a multiple regression model;
Multiple regression analysis of the results revealed that.
They are simple regression and multiple regression.
Multiple regression analysis was performed to control for age.
People also translate
The calculation is the same as for a multiple regression.
Basically this same technique with multiple regression, where multiple variables combined to predict the criterion variable.
When there is more than one independent variable,the technique is called multiple regression.
Which would make this a multiple regression with two predictors.
Example: Multiple regression is used to show the relationship(or lack of relationship) between age, weight, and exercise on diet food sales.
Results of analysis by using multiple regression analysis showed that: 1.
Some multiple regression techniques, such as automated stepwise regression with backward elimination, are particularly vulnerable to this problem.
Therefore the probability(0.000) is much smaller than 0.05,then the multiple regression models can be used to predict the performance of employees.
However, unlike the multiple regression involving only one criterion variable, canonical correlation involves more than one criterion variable.
To their great merit,Marvell and Moody frankly acknowledged the problems with multiple regression, and made some suggestions for improvement.
Specifically, in hierarchical multiple regression the investigator specifies an a priori order of entry of the variables, ideally on theoretical grounds.
The estimates market value of your home that you willreceive from Pricing Scout is based on a multiple regression algorithm,” notes the website.
Multiple regression analysis may permit predictions about changes in communication variables that can be expected when the communication situation changes.
This is true even foradvanced correlation-based statistical techniques such as multiple regression analysis, which control for competing effects of several variables on one main dependent variable.
In multiple regression analysis these were linked to HbA1c, duration of diabetes and systolic blood pressure, and fasting plasma glucose, folate and C-reactive protein, respectively.
Examples of the curriculum include human factors, research methods,industrial and organizational psychology, multiple regression, analysis of variance, patient handling, physical health at work, and sports medicine.
This is particularly true for multiple regression models, where there is a temptation of adding many predictors to the model to see which ones come out as the“winners”.
So the researchers sought to control for other factors such as gender, race, age, education, politicalleaning and even personal feelings about Clinton and Trump using multiple regression analysis, a method to measure the relative impact of multiple independent variables.
The general purpose of multiple regression(the term was first used by Pearson, 1908) is to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable.
So the researchers sought to control for other factors such as gender, race, age, education, political leaningand even personal feelings about Clinton and Trump using multiple regression analysis, a method to measure the relative impact of multiple independent variables.
The basic function of multiple regression(the term was initially utilized by Pearson, 1908) is to find out more about the relationship in between numerous independent or predictor variables and a reliant or requirement variable.
In Linear Regression analysis, we can involve one independent variable and one dependent variable, which is commonly called a simple regression analysis. We can also involve more than one independent variables with thedependent variable which is commonly called multiple regression analysis.