DATA INSPIRED

[ADP] #4-4. Statistical Analysis

1. Importance of Statistical Analysis

  • Essential for understanding and interpreting data.
  • Supports data-driven decision-making by providing critical insights.

2. Descriptive Statistics

  • Measures of Central Tendency:
    • Mean: The arithmetic average of the data.
    • Median: The middle value of the data.
    • Mode: The most frequently occurring value.
  • Measures of Dispersion:
    • Variance: Indicates how much the data values spread out from the mean.
    • Standard Deviation: The square root of the variance.
    • Range: The difference between the maximum and minimum values.
  • Shape:
    • Skewness: The degree of asymmetry in the data distribution.
    • Kurtosis: The degree of peakedness in the data distribution.

3. Inferential Statistics

  • Hypothesis Testing:
    • Null Hypothesis (H0): The default assumption, usually stating no effect or no difference.
    • Alternative Hypothesis (H1): The research assumption, usually stating there is an effect or difference.
    • p-value: The significance level of the hypothesis test.
  • Confidence Intervals:
    • Provides an estimated range for a population parameter.
    • Typically uses a 95% confidence level.
  • Regression Analysis:
    • Simple Regression: Analyzes the relationship between a single independent variable and a dependent variable.
    • Multiple Regression: Uses multiple independent variables to predict a dependent variable.

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