This document discusses various quantitative analysis techniques for time series data, including regression analysis, trend models, seasonality models, autoregressive models, and techniques for addressing autocorrelation. It provides an overview of simple and multiple linear regression, assumptions of regression models, and methods for model specification testing including analysis of variance.

1. Qunatitative Analysis – I

3. Data Trend Model Seasonality Log-Linear Model Linear Model Auto-Regressive Model Regression Analysis Cross-sectional Data Time dependent Data Significant Autocorrelation among Residuals Yes No Significant Autocorrelation among Residuals Significant Autocorrelation among lagged residuals Yes Yes No No Make a Graph Correctly Specified Model

4. Types of Regression Models © Neev Knowledge Management – Pristine Careers Negative Linear Relationship Negative Linear Relationship Relationship NOT Linear No Relationship

5. © Neev Knowledge Management – Pristine Careers Random Error for this x value y x Observed Value of y for x i Predicted Value of y for x i x i Slope = β 1 Intercept = β 0 e i Sample Regression Function

6. Sample Regression Function © Neev Knowledge Management – Pristine Careers Notice the similarity with the Population Regression Function Can we do something of the error term? Estimate of the regression intercept Estimate of the regression slope Independent variable Error term

12. Data Trend Model Seasonality Log-Linear Model Linear Model Auto-Regressive Model Regression Analysis Cross-sectional Data Time dependent Data Significant Autocorrelation among Residuals Yes No Significant Autocorrelation among Residuals Significant Autocorrelation among lagged residuals Yes Yes No No Make a Graph Correctly Specified Model