Performance Measures: Three sets of measurements are provided. Residual Standard Error: This is the standard deviation of the residuals.
![Plot Plot](/uploads/1/2/5/4/125448816/713570843.png)
Smaller is better. Multiple / Adjusted R-Square: For one variable, the distinction doesn’t really matter. R-squared shows the amount of variance explained by the model. Adjusted R-Square takes into account the number of variables and is most useful for multiple-regression. F-Statistic: The F-test checks if at least one variable’s is significantly different than zero.
Sep 10, 2013 - Easy definition of how a normal probability plot works. That's why technology like Minitab or SPSS is a good idea to make these types of. Function for SPSS-type QQ-plot. The whole point of this demonstration was to pinpoint and explain the differences between a QQ-plot generated in R and SPSS, so it will no longer be a reason for confusion. Note, however, that SPSS offers a whole range of options to generate the plot.
This is a global test to help asses a model. If the p-value is not significant (e.g. Greater than 0.05) than your model is essentially not doing anything. statistical tests T: the T-test is a test for Interpreting coefficient independent variable or called a partial test.
If the p-value is significant (e.g. Smaller than 0.05). While the rest are explained by causes other than the cause model (100%-142.14% = 8.87%).Residual standard error: 11180, the smaller the value of the standard error of the regression models would make the more precise in predicting the dependent variable.T-test in multiple linear regression is intended to test whether the parameters (coefficient of regression and constants) allegedly for estimation of linear regression model equations/double is already a parameter that is right or not.
Right intention here is able to explain the behavior of these parameters in the independent variables affect the dependent variable. The parameters being estimated in linear regression include intersep (constants) and the slope (coefficients in linear equations). In this section, test t focused on the slope parameters (coefficient of regression) only.
So the t-test is a test of regression coefficient.Of two independent variables entered into the regression model all significant (T-test) at 0.05. From here it can be concluded that EKS variable was influenced by variable KURS and HRG equations mathematically. Constants of -4,067,496 States that if the independent variables are considered constant, then the average export reduced by 4,067,496.The regression coefficient posistif value meaning (HRG) at time of export Prices go up then the number of export (EKS) also experienced ascension. So also when the price is down then the number of exports also dropped. The increase in export prices of one unit will increase the number of exports of 7,815 tons, so did the opposite.The regression coefficient of KURS is positive has the same meaning with the regression coefficient of HRG.okay, next steps is we doing assumptions testfirst step is multicolinearity Test. The results of the test of normality can be seen from the image of a Normal P-P Plot below.
Need to be reminded that the assumption of normality is in the classical assumptions of the OLS approach is (data) residual linear regression models established normal distributed, not independent variable or dependent variable. Criteria a (data) distributed the residual normal or not Normal approach with a P-P Plot can be done by looking at the distribution of points on the image. If the distribution of these points approach or meetings on straight lines (diagonals) then it is said that (data) residual is distributed normally, but when spread those points away from the line and thus are not distributed normally.