Results: cars3.sas

Metric Cars Data with proc glm

Analysis of Covariance

The GLM Procedure

The GLM Procedure

Data

Class Levels

Class Level Information
Class Levels Values
Country 3 Europ Japan US

Number of Observations

Number of Observations Read 100
Number of Observations Used 100

Metric Cars Data with proc glm

Analysis of Covariance

The GLM Procedure

 

Dependent Variable: lper100k Litres per 100 kilometers

Analysis of Variance

lper100k

Overall ANOVA

Source DF Sum of Squares Mean Square F Value Pr > F
Model 4 797.305741 199.326435 68.71 <.0001
Error 95 275.605859 2.901114    
Corrected Total 99 1072.911600      

Fit Statistics

R-Square Coeff Var Root MSE lper100k Mean
0.743123 13.87250 1.703266 12.27800

Type I Model ANOVA

Source DF Type I SS Mean Square F Value Pr > F
weight 1 746.6769100 746.6769100 257.38 <.0001
length 1 10.5937459 10.5937459 3.65 0.0590
Country 2 40.0350853 20.0175427 6.90 0.0016

Type III Model ANOVA

Source DF Type III SS Mean Square F Value Pr > F
weight 1 39.86971491 39.86971491 13.74 0.0004
length 1 16.61365020 16.61365020 5.73 0.0187
Country 2 40.03508534 20.01754267 6.90 0.0016

Metric Cars Data with proc glm

Analysis of Covariance

The GLM Procedure

Least Squares Means

Adjustment for Multiple Comparisons: Bonferroni

Least Squares Means

Country

lper100k

LSMeans

Country lper100k LSMEAN LSMEAN Number
Europ 13.2981897 1
Japan 13.8047068 2
US 11.8104679 3

Difference Matrix

Least Squares Means for Effect Country
t for H0: LSMean(i)=LSMean(j) / Pr > |t|

Dependent Variable: lper100k
i/j 1 2 3
1
 
 
-0.76727
1.0000
2.584495
0.0338
2
0.767266
1.0000
 
 
3.408986
0.0029
3
-2.5845
0.0338
-3.40899
0.0029