Statistical Analysis

Statistical Analysis: Techniques and Practical Implementation
├── Introduction
│   └── What is Statistical Analysis?
├── Setting Up the Environment
│   ├── Importing Necessary Libraries
│   └── Data Preparation
├── Fundamental Statistical Techniques
│   ├── Descriptive Statistics
│   ├── Inferential Statistics
│   └── Hypothesis Testing
├── Advanced Statistical Methods
│   ├── Regression Analysis
│   ├── ANOVA (Analysis of Variance)
│   └── Time Series Analysis
└── Conclusion
    └── Applications of Statistical Analysis

1. Introduction

What is Statistical Analysis?

Statistical Analysis involves collecting, analyzing, interpreting, presenting, and organizing data. It's a foundational aspect of data science and research.

2. Setting Up the Environment

Importing Necessary Libraries

import numpy as np
import pandas as pd
import scipy.stats as stats
import statsmodels.api as sm
from statsmodels.formula.api import ols

Data Preparation

Preparing or loading a dataset to apply various statistical techniques.

# Loading a sample dataset
data = pd.read_csv('sample_data.csv')

3. Fundamental Statistical Techniques

Descriptive Statistics

Summarizing and understanding the dataset.

# Basic descriptive statistics
print(data.describe())

Inferential Statistics

Making predictions or inferences about a population based on a sample.

# Example: T-test
t_statistic, p_value = stats.ttest_1samp(data['sample_column'], popmean=0)