Data Analysis Important Topics Unit wise


Unit 1: Introduction and Overview**  


 Data and Types of Measurement**  

**Data:** Raw facts and figures collected for analysis.  

**Types of Measurement Scales:**  

1. **Nominal Scale** (Categories, e.g., Gender: Male/Female)  

2. **Ordinal Scale** (Ordered categories, e.g., Ratings: Good, Better, Best)  

3. **Interval Scale** (Equal intervals, no true zero, e.g., Temperature in °C)  

4. **Ratio Scale** (True zero, e.g., Height, Weight)  


Primary vs. Secondary Data**         |  

Methods of Collecting Primary Data**  

1. **Surveys** (Questionnaires, Interviews)  

2. **Observations** (Direct or Participant Observation)  

3. **Experiments** (Controlled studies)  

4. **Focus Groups** (Group discussions)  


Population vs. Sample**  

- **Population:** Entire group under study.  

- **Sample:** Subset of the population.  

- **Merits of Sampling:** Saves time, cost-effective.  

- **Demerits:** Sampling errors may occur.  


Sampling Methods**  

| **Random Sampling**            | **Non-Random Sampling**            |  

|--------------------------------|------------------------------------|  

| - Simple Random Sampling       | - Convenience Sampling             |  

| - Stratified Sampling          | - Judgmental Sampling              |  

| - Systematic Sampling          | - Quota Sampling                   |  


Sampling Size & Distribution**  

- **Sample Size Determination:** Depends on population variability, confidence level.  

- **Sampling Distribution:** Probability distribution of a sample statistic (e.g., mean).  


Sampling vs. Non-Sampling Errors**  

| **Sampling Errors** (Due to chance) | **Non-Sampling Errors** (Human errors) |  

|-------------------------------------|---------------------------------------|  

| - Random fluctuations              | - Data collection mistakes            |  

| - Reduced with larger samples      | - Poor questionnaire design           |  


 Designing a Questionnaire**  

- **Key Steps:**  

  1. Define objectives.  

  2. Use simple, unbiased questions.  

  3. **Pretesting:** Pilot survey to check clarity.  

 Types of Interview Techniques**  

1. **Structured Interviews** (Fixed questions)  

2. **Unstructured Interviews** (Open-ended)  

3. **Telephonic/Online Interviews**  


 Methods of Collecting Secondary Data**  

- Government reports (Census, RBI bulletins)  

- Journals, books, online databases (Google Scholar)  

- Company annual reports  


---

Unit 2: Data Processing & Representation**  


Data Processing (Editing, Coding, Classification, Tabulation)**  

- **Editing:** Removing inconsistencies.  

- **Coding:** Assigning numerical labels (e.g., Male=1, Female=2).  

- **Classification:** Grouping data into categories.  

- **Tabulation:** Presenting data in tables.  


### **13. Cross-Tabulation**  

- Analyzes relationships between two variables.  

- **Example:**  

  | Gender \ Income | High | Low |  

  |----------------|------|-----|  

  | Male           | 30   | 20  |  

  | Female         | 25   | 25  |  

Graphical Representation**  

1. **Bar Diagram:** Compares categories.  

2. **Pie Chart:** Shows proportions.  

3. **Histogram:** Displays frequency distribution.  

4. **Ogive:** Cumulative frequency curve.  

Unit 3: Statistical Analysis**  


Univariate Analysis (Central Tendency, Dispersion, Skewness, Kurtosis)**  

- **Mean, Median, Mode** (Central Tendency)  

- **Variance, Standard Deviation** (Dispersion)  

- **Skewness:** Measures asymmetry (Positive/Negative).  

- **Kurtosis:** Peakedness of distribution (Leptokurtic/Platykurtic).  


Bivariate Analysis (Correlation & Regression)**  

- **Correlation (r):** -1 to +1 (Strength & Direction).  

- **Regression Line:** \( Y = a + bX \) (Predicts Y from X).  


Estimation of Population Parameters**  

- **Point Estimate:** Single value (e.g., Sample Mean).  

- **Interval Estimate:** Range (Confidence Interval).  


---


Unit 4: Hypothesis Testing & Advanced Techniques**  


 Hypothesis Testing**  

- **Null (H₀) vs. Alternative (H₁) Hypothesis**  

- **Tests:**  

  1. **t-test:** Compare two means.  

  2. **Z-test:** Large sample mean testing.  

  3. **F-test:** Compare variances (ANOVA).  

  4. **Chi-Square:** Tests independence.  


ANOVA (One-Way & Two-Way)

- **One-Way ANOVA:** Tests one factor (e.g., Teaching methods).  

- **Two-Way ANOVA:** Tests two factors (e.g., Fertilizer & Irrigation).  


Index Numbers**  

Price Index (CPI, WPI):** Tracks price changes.  

Quantity Index:** Measures production changes.  


**Best of Luck!** 

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