My project 2 is an individual that uses secondary data that i have retrieved from an online free source data provider Kaggle .
The focus of my case study is Students Performance in Exams.
I have conducted 4 inferential Statistical Test :
- Hypothesis testing
- Corellation
- Regression
- Chi-Square Test
Hypothesis Testing
In hypothesis testing, i want to determine weather the Average math score scored by students is =73.66.
From the testing, I have sufficient evidence to believe that the average math score is not equal to 73.66
Correlation
This test is carried out to identify the relationship between reading score and writing score.
The result from the test proves that therse is a strong positive relationship between reading and writing scores.
Regression
- Dependent variable -Reading score
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Independent variable- Writing score
The test results shows a linear relationship between reading and writing scores.
The estimated regression model is then calculated using
ŷ = 5.5995 + 0.9037x
R2 = 0.8722 which show that only 87.22 % of the variation in reading score is explained by the writing score.
Chi- Square Test of Independence
This test was carried out to determine the relationship between gender and test preparedness.
It was found out that test statistic does not fall in the critical region. I can conclude that at a 95% confidence interval there is sufficient evidence to prove the relationship between gender and test preparedness.
Conclusion
-
There is insufficient evidence to support the claim that the average math score scored by students is not equal to 76.66 marks.
- There is a strong positive relationship between the average writing score and average reading score.
- The estimated regression model is then produced in which we obtain ŷ = 5.5995 + 0.9037x.
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There is evidence of a relationship between gender and test preparedness.