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- What is Quantitative Methods?
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- Formulation of Research Questions or Hypotheses: Quantitative methods begin with the formulation of specific research papers or hypotheses that aim to investigate relationships between variables or test theories.
- Designing Research Studies or Experiments: Researchers design studies or experiments to collect data that will address the research questions or hypotheses. This involves selecting appropriate samples, variables, and research methods to ensure the validity and reliability of the findings.
- Data Collection and Analysis: Quantitative methods involve collecting numerical data through structured surveys, experiments, or observations. This data is then analyzed using statistical techniques such as regression analysis, ANOVA, or correlation analysis to identify patterns, trends, and associations among variables.
- Interpreting and Reporting Findings: Once the data analysis is complete, researchers interpret the findings in the context of the research questions or hypotheses. They report the results using descriptive statistics, inferential statistics, and graphical representations to communicate the implications of the findings effectively.
- Establishing Causality:Quantitative methods aim to establish causal relationships by analyzing the impact of one variable on another. Through experimental design or statistical control, researchers seek to identify the factors influencing the observed outcomes.
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Example 1: Correlation Analysis
Problem: Analyze the relationship between study hours and exam scores for 20 students. Calculate the correlation coefficient (r).
Solution:
If the correlation coefficient is r = 0.75, it indicates a strong positive relationship. Students who study more tend to achieve higher exam scores.
Example 2: Regression Analysis
Problem: Predict monthly sales based on advertising expenditure. Using regression, if the equation is Y = 5000 + 2.5X, it suggests that a $1 increase in advertising leads to a $2.50 increase in sales.
Solution:
In the regression model Y = 5000 + 2.5X:
- The intercept (5000) is the expected sales when advertising is zero.
- The slope (2.5) indicates a $2.50 increase in sales for every $1 increase in advertising.
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Owen Sandoval
Master’s in Statistics
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MSc in Statistics
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Master’s in Statistics
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