5PSYC001: Data Analysis for Psychology Learning Journal Coursework Sample
Introduction
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This assessment will help to draw a conclusion from the data analysis with the help of the SPSS tool. This tool is used to determine the significance between the two variables. The ANOVA tool is used to find the anxiety test and to examine the performance among the dependent as well as the independent variables.
From the performed test it can be analyzed that there were a total of 10 participants in this model. There were 3 participants whose standard deviation is obtained to be 1.732 and there were two participants whose deviation is obtained to be 0.70711.
the test has been performed in the ANOVA model where several results have been obtained of standard deviation. The value of standard deviation is 2.12132.
Reproducing this analysis
From the performed test in 2 ways ANOVA, it can be analyzed that there were a total 10 participants in this model from which the value of standard deviation is maximum as there were a total 3 participants. However, the second largest standard deviation is the value 1.52753.
From the above performed test it can be analyzed that there were a total of 10 numbers of counting. The standard deviation of the numbers is found to be 1.91485 and the other number is 1.41421. However, the mean of all the variables was close to each other. There were a total of 4 members in which the value of mean is 7.5 and there were 2 members whose mean is obtained to be 8. As per my point of view this has critical effect on outliers. From the performed test in 2 ways ANOVA, it can be analyzed that there were a total 10 participants in this model from which the value of standard deviation is maximum as there were a total 3 participants. However, the second largest standard deviation is the value 1.52753.
Conclusion
The assessment can be concluded by; the regression analysis was helpful to conduct the entire task. The analysis helps to understand the relationship between the two variables. This test was conducted in order to analyze the connection between one of the dependent variables and the other independent variable. This test is known to be helpful as these statistics can help to identify whether the test is specific and significant.
References
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Daoud, J. I. (2017, December). Multicollinearity and regression analysis. In Journal of Physics: Conference Series (Vol. 949, No. 1, p. 012009). IOP Publishing. Retrieved on 15th December 2020 from: https://iopscience.iop.org/article/10.1088/1742-6596/949/1/012009/pdf
Fraiman, D., & Fraiman, R. (2018). An ANOVA approach for statistical comparisons of brain networks. Scientific reports, 8(1), 1-14. Retrieved on 16th December 2020 from: https://www.nature.com/articles/s41598-018-23152-5
Hanley, J. A. (2016). Simple and multiple linear regression: sample size considerations. Journal of clinical epidemiology, 79, 112-119. Retrieved on 20th December 2020 from: http://www.med.mcgill.ca/epidemiology/hanley/Reprints/SimpleMultipleLinearRegressionSampleSize.pdf
Lang, H. (2016). Elements of regression analysis. Stockholm: KTH Mathematics. Retrieved on 25th December 2020 from: https://people.kth.se/~lang/regression_analysis.pdf
Lin, L., & Dobriban, E. (2020). What causes the test error? Going beyond bias-variance via ANOVA. arXiv preprint arXiv:2010.05170. Retrieved on 15th December 2020: https://arxiv.org/pdf/2010.05170
Nyaga, V. N., Aerts, M., & Arbyn, M. (2018). ANOVA model for network meta-analysis of diagnostic test accuracy data. Statistical methods in medical research, 27(6), 1766-1784. Retrieved on 25th December 2020 from: https://journals.sagepub.com/doi/pdf/10.1177/0962280216669182
Ranganathan, P., Pramesh, C. S., & Aggarwal, R. (2017). Common pitfalls in statistical analysis: logistic regression. Perspectives in clinical research, 8(3), 148. Retrieved on 10th December 2020 from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543767/
Xu, H., Xiao, K., Yu, J., Huang, B., Wang, X., Liang, S., ... & Huang, X. (2020). A Simple Method to Identify the Dominant Fouling Mechanisms during Membrane Filtration Based on Piecewise Multiple Linear Regression. Membranes, 10(8), 171. Retrieved on 20th December 2020 from: https://www.mdpi.com/2077-0375/10/8/171/pdf
