Statistical Analysis Parametric tests
Budget: £20 – £250 GBP
i have 11 different sports, with 287 total participants, i have asked them do they get gut complaints and to tick the type of gut complaints (they can select multiple) or not, each sport has different numbers of participants, i have converted the results to percentages. I also have SPSS datasets and need a couple of other sections of the questionnaire analysed as well. Chat GPT says i need the following: Chi-Square Test of Independence (Sport vs. Gut Complaints):
Organize your data into a contingency table with sports as rows and gut complaints as columns.
Use statistical software (e.g., R, Python, SPSS) to run the chi-square test.
Interpret the p-value to determine if there's a significant association between sports and gut complaints.
Correlation Analysis (Age vs. Gut Complaints):
Calculate the correlation coefficient (e.g., Pearson correlation) between age (continuous) and gut complaints (binary).
Use statistical software to perform the correlation analysis.
Interpret the correlation coefficient and associated p-value.
Chi-Square Test or Logistic Regression (Gender vs. Gut Complaints):
Organize your data into a contingency table with gender as rows and gut complaints as columns.
Use the chi-square test or logistic regression (with gender as a predictor and gut complaints as the outcome).
Interpret the results to assess gender differences in gut complaints.
Logistic Regression (Predictors of Gut Complaints):
Identify potential predictors (e.g., training intensity, dietary habits).
Build a logistic regression model with gut complaints as the outcome and predictors as predictors.
Use statistical software to run the logistic regression analysis.
Interpret the coefficients and p-values to identify significant predictors.
Independent Samples t-test or Mann-Whitney U Test (Performance vs. Gut Complaints):
Separate athletes into two groups: those with gut complaints and those without.
Compare training performance between the two groups using the appropriate test.
Use statistical software to perform the test and interpret the results.
Correlation Analysis or Linear Regression (Training vs. Gut Complaints):
Calculate the correlation or build a linear regression model with training variables (e.g., duration, intensity) and gut complaints.
Use statistical software to perform the analysis.
Interpret the results to assess the relationship between training and gut complaints.
Chi-Square Test, Logistic Regression, or ANOVA (Diet vs. Gut Complaints):
Organize dietary data and gut complaints data.
Use the appropriate test (chi-square, logistic regression, or ANOVA) to analyze the relationship.
Interpret the results to understand the association between dietary factors and gut complaints.
Organize your data into a contingency table with sports as rows and gut complaints as columns.
Use statistical software (e.g., R, Python, SPSS) to run the chi-square test.
Interpret the p-value to determine if there's a significant association between sports and gut complaints.
Correlation Analysis (Age vs. Gut Complaints):
Calculate the correlation coefficient (e.g., Pearson correlation) between age (continuous) and gut complaints (binary).
Use statistical software to perform the correlation analysis.
Interpret the correlation coefficient and associated p-value.
Chi-Square Test or Logistic Regression (Gender vs. Gut Complaints):
Organize your data into a contingency table with gender as rows and gut complaints as columns.
Use the chi-square test or logistic regression (with gender as a predictor and gut complaints as the outcome).
Interpret the results to assess gender differences in gut complaints.
Logistic Regression (Predictors of Gut Complaints):
Identify potential predictors (e.g., training intensity, dietary habits).
Build a logistic regression model with gut complaints as the outcome and predictors as predictors.
Use statistical software to run the logistic regression analysis.
Interpret the coefficients and p-values to identify significant predictors.
Independent Samples t-test or Mann-Whitney U Test (Performance vs. Gut Complaints):
Separate athletes into two groups: those with gut complaints and those without.
Compare training performance between the two groups using the appropriate test.
Use statistical software to perform the test and interpret the results.
Correlation Analysis or Linear Regression (Training vs. Gut Complaints):
Calculate the correlation or build a linear regression model with training variables (e.g., duration, intensity) and gut complaints.
Use statistical software to perform the analysis.
Interpret the results to assess the relationship between training and gut complaints.
Chi-Square Test, Logistic Regression, or ANOVA (Diet vs. Gut Complaints):
Organize dietary data and gut complaints data.
Use the appropriate test (chi-square, logistic regression, or ANOVA) to analyze the relationship.
Interpret the results to understand the association between dietary factors and gut complaints.