Simple linear regression makes four key assumptions about the data: homogeneity of variance, independence of observations, normality, and a linear relationship between the independent and dependent variables. The assumptions are that the error in predictions does not change across independent variable values, observations were collected independently without relationships between them, the data follows a normal distribution, and the relationship between variables is best modeled with a straight line.

1. CONTINUATION…

2. Assumptions of Simple linear
Regression
Simple linear regression is a
parametric test, meaning that it
makes certain assumptions about
the data. These assumptions are:

3. Homogeneity of Variance
(Homoscedasticity)
The size of the error in our
prediction doesn’t change
significantly across the values of
the independent variable.

4. Independence of Observations
The observations in the dataset were
collected using statistically valid
sampling methods, and there are no
hidden relationships among
observations.

5. Normality
The data follows a normal
distribution.

6. The Relationship Between the
Independent and Dependent
Variable is Linear
The line of best fit through the data
points is a straight line (rather than a
curve or some sort of grouping
factor).

7. Water Management Practices and Environmental
Attitudes of Riparian Communities in Sapangdaku
River, Cebu Island, Philippines by Sanchez et. Al.
(2022)
DOI:http://dx.doi.org/10.15294/biosaintifika.v14i2.3
6185

8. Objectives of the Study:
1. To determine the socio-demographic profile,
anthropogenic activities, water utilization of the
residents, and waste disposal practices along the
Sapangdaku River.
2. To determine the correlation of the
environmental attitude between its four
constructs.

9. Respondents:
Riverside dwellers along the
Sapangdaku River participated in the
study. Through random sampling, 120
residents were selected as
respondents of the study.

10. Data:
1. Survey Questionnaire
2. Environmental Attitude Survey (EAS) extracted
from the Environmental Attitude Inventory (EAI)

11. Statistical Measures Used:
1. Descriptive Statistics (Frequency Count)
2. Pearson r Correlation

12. Results 1

13. Results 1

14. Results 1

15. Results 2

16. Limitations of the Methodology
The use of Pearson r to treat the
data on Environmental Attitude is
not appropriate.
Why?

18. Limitations of the Methodology
The data collected from EAI are
discrete because the statements
are answerable through a Likert
scale.

19. Limitations of the Methodology
Another limitation is the use of only
four constructs out of 12 constructs
from the original Environmental
Attitude Inventory.

20. Conclusion
When using simple linear
regression, two continuous
variables must be involved.

21. THANK YOU FOR
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