2 Effective Tools for Data Analysis
2 Effective Tools for Data Analysis
This is a categorization of tools used for data analysis. It divides the tools into two categories: graphical tools and numerical tools.
Graphical tools are used to visually represent data. They are helpful for understanding the distribution of data, identifying patterns and trends, and communicating findings to others.
The following are graphical tools:
- Histogram: Shows the distribution of data by frequency.
- Normality plot: Assesses how closely your data resembles a normal distribution.
- Run chart: Tracks data points over time to identify trends or shifts.
- Pivot table/Bar chart: A pivot table summarizes data, and a bar chart can be used to visually represent the summarized data.
- Pareto chart: Illustrates the relationship between two factors by showing that most of the effects (80%) come from a small number of causes (20%).
- Scatter plot: Shows the relationship between two variables.
- Dot plot: Similar to a scatter plot, but uses dots to represent data points.
- Box plot: Shows the distribution of data through quartiles.
- Multi-variate chart: A chart that displays multiple variables at the same time.
Numerical tools are used to perform calculations on data and draw statistical inferences.
The following are numerical tools:
- Confidence intervals: Estimate a range of values that are likely to contain the true population parameter.
- Hypothesis testing: A statistical method used to test claims about a population.
- ANOVA (Analysis of Variance): Compares means of more than two groups.
- Regression analysis: Analyzes the relationship between a dependent variable and one or more independent variables.
The distinction between graphical and numerical data analysis tools is not always strict. Some tools, like pivot tables, can be used for both purposes.