• Utilize SPSS 26.0 statistical software to analyze the quantitative data collected from all study participants. Begin by performing comprehensive descriptive statistics to effectively summarize the central tendency and variability across the key datasets. Specifically, calculate the mean, standard deviation, maximum value, and minimum value for the physical health knowledge scores, physical fitness test results, and satisfaction scores within both the experimental group and the control group, both prior to and following the intervention. This initial step provides a crucial overview of the overall data distribution, aids in identifying any potential outliers or unusual patterns, and establishes a foundational understanding of the dataset characteristics for subsequent analyses. • Subsequently, conduct inferential statistical procedures to rigorously test the study hypotheses and explore potential relationships between variables. Initiate this phase by employing an independent sample t-test. Apply this test to compare the baseline differences in physical health knowledge scores, physical fitness test results, and satisfaction scores between the experimental group and the control group before the intervention commences, using a predetermined significance level of α=0.05. This critical comparison ensures that the two groups are statistically comparable at the outset, confirming the absence of significant pre-existing differences prior to the administration of the intervention. • Proceed next with paired sample t-tests to meticulously examine within-group changes over the intervention period. Conduct these tests separately for the experimental group and the control group, comparing the differences in physical health knowledge scores, physical fitness test results, and satisfaction scores recorded before the intervention with those recorded after the intervention, again applying the α=0.05 significance threshold. This analysis directly assesses the magnitude and statistical significance of changes occurring over time within each group individually, providing insight into the natural progression or any inherent group-specific effects. • Then, implement analysis of covariance (ANCOVA) to account for initial variations between participants and enhance the precision of the between-group comparison after the intervention. For this analysis, incorporate the pre-test (baseline) results as covariates. Analyze the adjusted differences in post-test results for physical health knowledge scores, physical fitness test results, and satisfaction scores between the experimental group and the control group, statistically controlling for these baseline scores. This sophisticated approach effectively eliminates the confounding influence of pre-existing differences among participants, thereby yielding a more accurate and unbiased evaluation of the true intervention effect, with statistical significance assessed at α=0.05. • Finally, execute bivariate correlation analyses to investigate potential linear associations between the measured variables. Analyze the pairwise correlations between physical health knowledge scores, physical fitness test results, and satisfaction scores using Pearson's correlation coefficient (r). This analysis explores the strength and direction of potential relationships and dependencies among these key outcome measures, with the significance of each correlation coefficient rigorously tested at the α=0.05 level. Throughout all inferential analyses (t-tests, ANCOVA, correlation), it is imperative to include thorough checks for underlying statistical assumptions, such as normality of distribution and homogeneity of variances (homoscedasticity), to ensure the validity and robustness of the reported findings.根据以上画一个流程图