According to the model, does the additive type have any significant effect on reducing NOX emission? Can the effects of Cars or Drivers be ignored in this model? Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 18.000 2.309 4.151 7.794 0.00125 ** additiveB 4.000 1.155 6.000 3.464 0.01340 * additiveC 3.000 1.155 6.000 2.598 0.04077 * additiveD 1.000 1.155 6.000 0.866 0.41975 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) addtvB addtvC additiveB -0.250 additiveC -0.250 0.500 additiveD -0.250 0.500 0.500 Type III Analysis of Variance Table with Satterthwaite's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) additive 40 13.333 3 6 5 0.0452 *
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
- According to the model, does the additive type have any significant effect on reducing NOX emission? Can the effects of Cars or Drivers be ignored in this model?
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 18.000 2.309 4.151 7.794 0.00125 **
additiveB 4.000 1.155 6.000 3.464 0.01340 *
additiveC 3.000 1.155 6.000 2.598 0.04077 *
additiveD 1.000 1.155 6.000 0.866 0.41975
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Intr) addtvB addtvC
additiveB -0.250
additiveC -0.250 0.500
additiveD -0.250 0.500 0.500
Type III Analysis of Variance Table with Satterthwaite's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) additive 40 13.333 3 6 5 0.0452 *
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