Managing data

Seminar 5

Managing data
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Table of Contents
REFERENCES................................................................................................................................4
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Q-1 Regression between two variables
Regression Statistics
Multiple R 0.670244797
R Square 0.449228088
Adjusted R
Square 0.428829129
Standard Error 17.56400213
Observations 29
ANOVA
df SS MS F
Significance
F
Regression 1 6793.692 6793.692 22.02210775 6.95491E-05
Residual 27 8329.343 308.4942
Total 28 15123.03
Coefficient
s
Standar
d Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
-
8.5174228
8
14.4231
4 -0.59054
0.5597
40459 -38.1112558
21.076
41 -38.1113 21.07641
X Variable
1
1.5788647
92
0.33644
6
4.69277
2
6.9549
1E-05 0.888534417
2.2691
95 0.888534 2.269195
Q-2 Scatter diagram for variables
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Interpretation- It can be interpreted that significance value is P= 6.95 which is more than P =
0.3025. It means that null hypothesis is rejected. There is no relationship between retention and
graduation rate (%) in online colleges. The retention of student does not depends on graduation.
Besides that, R square value is .44 which is not good. It means only 44% of values fit in
regression analysis model.
Q-3 Did regression equation provided a good fit
It can be evaluated that estimated regression did not provided a good fit. It is because R
square value is .44 that shows only 41% of value fit in model (Brook and Arnold 2018).
Q-4 Would regression is used for forecasting
No, after reviewing outcomes I would not have used regression model for forecasting.
This is because model is used to find out relationship between variables and not for forecasting.
Also, it is seen that there is no relation between retention and graduation rate. So, I think that this
model would not have provided accurate and relevant outcomes.
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REFERENCES
Books and journals
Brook, R.J. and Arnold, G.C., 2018. Applied regression analysis and experimental design. CRC
Press.
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