Ensuring that each observation is unique.

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## Working with Number Datasets

### 1. Data Cleaning

Before analysis, it is essential to clean the dataset. Common cleaning tasks include:

- **Removing Duplicates**:
- **Handling Missing Values**: Deciding how to address any gaps in the data, such as imputing values or removing incomplete records.
- **Standardizing Formats**: Ensuring consistency in dominican republic number dataset units of measurement and data types.

### 2. Data Exploration

Exploratory Data Analysis (EDA) involves summarizing the main characteristics of a dataset using statistical graphics and other data visualization methods. Key techniques include:

- **Descriptive Statistics**: Calculating measures such as mean, median, mode, variance, and standard deviation.
- **Visualization**: Creating charts and graphs (e.g., histograms, scatter plots) to visualize distributions and relationships between variables.

### 3. Statistical Analysis

Once the data is cleaned and explored, various statistical techniques can be applied, including:

- **Regression Analysis**: Modeling the relationship between dependent and independent variables.
- **Hypothesis Testing**: Testing assumptions about populations based on sample data.
- **Correlation Analysis**: Evaluating the strength and direction of relationships between variables.