Lecture 9 Data Analysis and Interpretation
"The goal is to turn data into information, and information into insight." - Carly Fiorina
9.3. Some examples of data analysis
Below are three examples for data analysis in research:
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Health Sciences: A research team collects data from a randomized controlled trial evaluating the effectiveness of a new drug for treating hypertension. The raw data includes measurements of blood pressure, heart rate, and other physiological parameters collected from patients at different time points. The team performs error analysis and data cleaning to ensure the accuracy and consistency of the data. They then use statistical methods to analyze the data, compare the treatment group with the control group, and investigate the trends and correlations in the data. Finally, they develop a model to explain the mechanisms of action of the new drug and propose possible new discoveries related to hypertension treatment.
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Social Sciences: A researcher collects survey data from a large sample of respondents to investigate the relationship between social media use and mental health outcomes. The raw data includes responses to different survey questions related to social media use, mental health symptoms, and demographic variables. The researcher performs error analysis and data cleaning to ensure the quality of the data. They then use statistical methods to analyze the data, compare different groups of respondents, and investigate the correlations and trends in the data. Finally, they propose a model to explain the relationship between social media use and mental health outcomes and suggest possible new interventions to improve mental health in social media users.
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Engineering: A research team collects experimental data from a wind tunnel to investigate the aerodynamic performance of a new aircraft wing design. The raw data includes measurements of lift, drag, and other parameters collected at different wind speeds and angles of attack. The team performs error analysis and data processing to extract relevant parameters and trends from the data. They then use mathematical models and physical theories to fit the data and derive a model that accurately describes the aerodynamic behavior of the wing. Finally, they propose possible new designs based on the insights gained from the data analysis to improve the performance of the aircraft.