In: Statistics and Probability
Class 1 |
Class 2 |
Class 3 |
|||
Rating |
GPA |
Rating |
GPA |
Rating |
GPA |
3.17 |
1 |
3.17 |
1 |
7.25 |
3.667 |
3.75 |
2.333 |
6.67 |
2.667 |
2.58 |
2 |
6.08 |
3.667 |
4.92 |
2.333 |
3.17 |
1.667 |
9 |
3 |
6.67 |
3.333 |
3.75 |
2.667 |
4.92 |
1.667 |
3.75 |
1.333 |
4.92 |
3 |
7.25 |
3.333 |
4.33 |
1.667 |
3.17 |
1.333 |
5.5 |
2.333 |
5.5 |
3 |
4.33 |
3 |
4.33 |
1.333 |
4.33 |
1.667 |
5.5 |
4 |
Rerun the analysis using effect coding and answer the following questions:
SOLUTION
Assuming we did not have data for GPA, we can run one way ANOVA(Analysis of variance) to determine whether the three classes differ on how they rated the teaching assistant.
Null hypothesis(H0): There is no significant difference in the mean rating among the three classes
Alternate hypothesis(H1): There is significant difference in the mean rating among the three classes.
= 5%
The ANOVA table is given below.
Source of variation | d.f | SS | MSS | F | P-value | F critical value |
Between Groups | 2 | 5.440558 | 2.720279 | 1.062452 | 0.363465 | 3.4668 |
Within groups | 21 | 53.76794 | 2.560378 | |||
Total | 23 | 59.2085 |
Conclusion: Accept the null hypothesis that there is no significant difference in the mean rating among the three classes.
Including GPA into consideration we get the regression equation for rating as follows.
X=class, Y=GPA and Z= rating.
Z=3.285-0.812X+1.371Y
ANOVA table from SPSS
Model | Sum of squares | df | Mean square | F | sig |
Regression | 39.831 | 2 | 19.916 | 21.583 | 0.000 |
Residual | 19.377 | 21 | 0.923 | ||
Total | 59.208 | 23 |
ceofficients
Model | B | Std Error | Beta | t | sig |
Contant | 3.285 | 0.692 | 4.745 | 0.000 | |
Class | -0.812 | 0.243 | -0.422 | -3.341 | 0.003 |
GPA | 1.371 | 0.225 | 0.771 | 6.105 | 0.000 |
Dependent variable :Rating
From the table we see that none of the coefficients are significant.
Hence there is no significant difeerence in rating.
For class 1 the regression equation for rating (assuming X=GPA and Y = rating)
Y= 1.477X + 2.053
For class 2 the regression equation for rating (assuming X= GPA and Y= rating)
Y =1.437X + 1.863
For class 3 the regression equation for rating (assuming X= GPA and Y= rating)
Y = 1.374 X +0.669
score prediction for the teaching assistant by a student who has a GPA of 2.0 and is in Class 3equation
we use the regression equation
X=class, Y=GPA and Z= rating.
Z=3.285-0.812X+1.371Y
Rating = 3.591 by substituting X=3 and Y=2