Statistical Analysis of Data: Hypothesis Testing and Interpretation

This assignment presents a statistical analysis of data using chi-square tests to evaluate a hypothesis about agreement levels. The solution includes interpretations of p-values, null hypotheses, and Type 1 errors. The analysis explores the relationship between observed and expected data, determining whether the data supports the null hypothesis or suggests that the subjects were not guessing. The assignment also discusses distributional shapes, confidence levels, and the implications of alpha values in hypothesis testing. References to statistical methods and interpretations are included, demonstrating a solid understanding of statistical concepts and their application. The student has provided an interpretation of the results, providing valuable insights and demonstrating the ability to use statistical tools to analyze data and draw meaningful conclusions, with references to books and journals.

STATISTIC
INTERPRETATION

TABLE OF CONTENTS
Q 5....................................................................................................................................................3
Q 14..................................................................................................................................................3
Q 16..................................................................................................................................................3
Q 17..................................................................................................................................................3
REFERENCES................................................................................................................................5

Q 5
With the calculation of the chi square test it is clear that the the sample data is being matched
with the population. This chi- square test is being implemented for the analysis of data that is
whether the situation is that amount of agreement among LFB and Foxy is what is expected to
see in case they are guessing these answers (Wang and Zhao, 2017). For the analysis of this
statement the use of chi- square test was undertaken and this is pertaining to the fact that this is
tools which assist the researcher in calculating and comparing the two different variables. The
small chi- square test value depicts the fact that observed data fits the expected data extremely
well. On the other side, the very large chi square test means data is not fit well.
Q 14
With the description of the data the distributional shape that will be created is of negatively
skewed. This is majorly pertaining to the fact that in the given situation the high budget people
are very less in numbers. But on the other side, the people with low budget are many but they are
not having proper budget and money.
Further the standard deviation calculated in part (b) is related to the confidence level of 95 %.
This is pertaining to the fact that confidence level is referred to as the fact that if we are taking
100 samples and confidence level is 95 % when it reflects the fact that approximately 95 out of
the 100 confidence will contain the true mean value. Hence, this confidence level of 95 % is
likely to be range of true unknown parameter.
Q 16
With the above statement it is clear that when the p value is 0.023 then this reflects the fact that
2.3 % of the statement is null whereas the 97.7 % of the alternative is correct. This is pertaining
to the fact that when the p value is 0.05 then this states that 95 % the statement will be correct
and remaining 5 % the statement will not be correct. Hence, this simply applies in case of the p
value of 0.023 as well and because of this the
Q 17
In case when the alpha = 0.001 then this means that the null hypothesis is being selected. But the
Type 1 error occurs when the true null hypothesis is being rejected. This is pertaining to the fact

that when the lower value of alpha is there then it makes harder to reject the null hypothesis.
Thus, as a result of these lower values for alpha reduces the probability of Type 1 error (Zhao,
Hu and Wang, 2018).
In case the alpha is kept at 0.05 then this reflects the fact that here the type 2 error has occurred.
This is pertaining to the fact that type 2 error occurs when we fail to reject a false null
hypothesis. Hence, here the alpha has increased then this means that the null hypothesis is being
rejected.

REFERENCES
Books and Journals
Wang, Y. and Zhao, T., 2017. Statistical interpretation of soil property profiles from sparse data
using Bayesian compressive sampling. Géotechnique, 67(6), pp.523-536.
Zhao, T., Hu, Y. and Wang, Y., 2018. Statistical interpretation of spatially varying 2D geo-data
from sparse measurements using Bayesian compressive sampling. Engineering Geology,
246, pp.162-175.