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When analyzing data quantitatively, a good research topic plays a key role. From the research question, the independent variable should be; physical exercise, while the dependent variable is depression symptoms. Adolescents form the target population and basis for collecting data. The null hypothesis assumes that adolescents engaging in physical exercise will likely have fewer depression symptoms. In contrast, the alternative hypothesis state that physical activity does not significantly impact depression symptoms among adolescents. It is true that two groups should be clustered from the population for observation, experiment, and investigation.
The intervention group includes adolescents exposed to physical exercise, while the control groups are those not exposed to physical activity (Winston, 2019). The findings for the two groups will then be analyzed using STATA, SPSS or ANOVA since the quantitative data needs analysis of specific software (Mishra et al., 2018). A decision tree can be used due to the nature of the research question or hypothesis in place, the measurement of the dependent or research variable, the number of groups or independent variable levels, and the research design
Based on the research question, there is a need to examine the differences between the two population groups; hence, using ratios and data values is appropriate for the independent sample. It is true that decision tree analysis aid in determining the sample test from the independent variables. However, the p-values and t-value can be determined by calculating the data set using excel or SPSS from the two groups (Marpa, 2019). The p-value is assumed to be 0.05 for the independent data set, while a 95% confidence level is used on the dependent variable should be added on the argument.
I agree that t-test investigates the relationship between physical exercise and depression symptoms from either of the existing population data. In conclusion, while examining the relationship between two variables, data is analyzed using software and ANOVA, which enables a researcher to determine the p-values, t-value, and z-value for establishing the hypothesis assumed in the research. I therefore agree with most of the arguments posted by the student regarding his analysis skills and the data set chosen. However, few areas need to be improved on the arguments and I have specifically discussed them in this discussion post.
References
Marpa, E. P. (2019). Common errors in algebraic expressions: A quantitative-qualitative analysis. International Journal on Social and Education Sciences, 1(2), 67-70. Web.
Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. (2018). Big data and supply chain management: A review and bibliometric analysis. Annals of Operations Research, 270(1- 2), 313-336. Web.
Winston, W. (2019). Microsoft excel 2019: Data analysis and business modelling (6th edition). Pearson Publishers. Web.
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