How to Do Statistical Analysis of a Dataset: Smokers Protected from Diseases?

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Ma'Mon Abu Hammad

To perform statistical analysis on your dataset of 520 volunteers who are smokers and protected from certain diseases, you can follow these general steps:

Data Preparation: Ensure that your dataset is properly organized and cleaned. Check for missing values, outliers, and any inconsistencies in the data. If necessary, preprocess the data by recoding variables or transforming them to meet the assumptions of the analysis techniques you plan to use.

Define Research Questions: Clearly articulate the research questions or hypotheses you want to investigate. Identify the variables of interest and the specific analyses you intend to conduct.

Descriptive Analysis: Begin by conducting descriptive analysis to summarize the characteristics of the sample. Calculate measures such as means, standard deviations, frequencies, and percentages for relevant variables. This will help you gain an initial understanding of the data.

Inferential Analysis: Based on your research questions, select appropriate inferential statistical tests. Commonly used tests in this scenario might include: Chi-square test: If you have categorical variables, you can use the chi-square test to examine associations or differences between variables. t-test: If you have continuous variables and want to compare means between two groups, you can use the t-test. Correlation analysis: If you want to explore the relationships between continuous variables, you can calculate correlation coefficients (e.g., Pearson correlation) to assess the strength and direction of those relationships. Regression analysis: If you want to investigate the relationship between an outcome variable and predictor variables, you can perform regression analysis (e.g., linear regression, logistic regression).

Interpretation of Results: Once you have conducted the statistical analyses, interpret the results of your research questions or hypotheses. Assess the statistical significance of the findings, examine effect sizes, and consider the practical significance or implications of the results.

Consider Limitations: Reflect on any limitations of the study, such as sample representativeness, potential biases, or other factors that may affect the generalizability of the findings.

It is crucial to note that the specific analyses and techniques used will depend on the nature of your data, research questions, and assumptions of the statistical tests. Consulting with a statistician or data analyst can provide further guidance tailored to your specific research objectives and dataset.

Murugesan Arumugam

Dear Dr Ma'Mon Abu Hammad Thank you for your kind reply and detailed explanation. Let me try this. If I need any further help, I will get back to you.

David L Morgan

Can you say more about what you mean by "protected from certain diseases"?

Prof David L Morgan Thank you for your enquiry. Kindly consider my apology as the data is in a very preliminary stage of writing the manuscript, which is the reason I am unable to disclose it completely in public.

The exact information I want to convey is that we have screened 520 smokers for another study, and we have filed a patent for the study product.

Accidentally, during the study, we also identified that none of the smokers were affected by a particular disease (0% incidence). In this case, I would like to know how to calculate the statistics for this single group of only smokers' samples. Thank you once again

If everyone in your dataset is a smoker, I'm not sure what your independent variable would be.