# multiple

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Related to multiple regression: Multiple linear regression
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For discussion, we focus only on the multiple regression model to predict FVC against given age, height and weight.
Figure 5 reports the bottom section (rows 24-57) of the multiple regression output generated through the Regression tool.
In Section 2 we use some very basic theory to show how the univariate regression and multiple regression make different assumptions on the models.
Multiple Regression Model (MRM) was used to find out the factors that had significance impact on the IMR.
Using Baron and Kenny (1986) approach of mediation analysis, linear and multiple regressions were run to test the study variables.
Calculating the Pearson correlation coefficient between the data landslide factors as the dependent variable and efficient factors in landslide as independent variables have proven the minimal correlation between these two sets of variables necessary to perform multiple regression model.
To illustrate the computation of Equations 2 and 3, suppose a researcher is planning a multiple regression analysis with k = 4 predictor variables and wants to compute a 95% confidence interval for [[rho].
The expected amount of rainfall in mm for model period 1987-2011 was obtained by applying the multiple regression models (2.
In the view of such information, the aim of this research was to obtain a good prediction equation of body weight without multicollinearity problem by using factor and principal component scores in multiple regression analyses principal from morphological linear measurements not only taken easily from goat but also used as indirect selection criteria in the selection of superior animals with the aim of genetically progressing body weight.
The quality criteria of the multiple regression analysis were determined with the help of the coefficient of determination (R2) and Mean Square Error (MSE) (Eyduran et al.
Brookshire and Palocsay (2005) applied multiple regression analysis to determine significant factors that impact performance of students in an undergraduate management science course and found overall academic performance (GPA) had the strongest correlation with performance, while other variables included in the model: SAT math score, prerequisites (calculus and statistics), major, and instructor had a lesser significance on the performance.
A background in basic statistics through ordinary least squares regression is the minimal prerequisite, but background in multiple regression analysis and log-linear analysis will aid in understanding.

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