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What processes are involved in the statistical analysis of clinical trial data with SAS?
Statistical analysis of clinical trial data with SAS often consists of many phases. First, data cleaning and validation are performed to verify data accuracy. The data is then summarised using descriptive statistics. Next, inferential statistics, such as hypothesis testing and confidence interval estimates, are used to evaluate treatment outcomes. Modelling approaches such as regression analysis or survival analysis can be used to investigate connections between variables. Finally, the findings are evaluated, and detailed reports are prepared for regulatory filings. Throughout this procedure, compliance with regulatory rules and standards is critical to ensuring the validity and trustworthiness of the analytical results.
SAS Online Training Institute, Power BI, Python Pune, India (saspowerbisasonlinetraininginstitute.in)

How can I discover outliers in time series data?
There are various approaches for identifying outliers in time series data. One typical strategy is to employ statistical approaches like the z-score or modified z-score method, in which data points that exceed a given threshold are marked as outliers. Another technique is to employ strong statistical metrics such as median absolute deviation (MAD) or Tukey's fences. Additionally, time series-specific procedures such as seasonal decomposition or residual analysis can aid in detecting anomalous trends. Visualisation approaches like as box plots, scatter plots, and time series plots with superimposed threshold lines can help identify outliers. Finally, machine learning methods such as Isolation Forest or One-Class SVM can be useful for detecting outliers in time series data.
SAS Online Training Institute, Power BI, Python Pune, India (saspowerbisasonlinetraininginstitute.in)