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Confidence Intervals (CI) for Dependent Variable Prediction | Finance Train

Quantitative Methods

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CFA L2: Quantitative Methods - Introduction

Quants: Correlation Analysis

Quants: Single Variable Linear Regression Analysis

Standard Error of the Estimate or SEE

Confidence Intervals (CI) for Dependent Variable Prediction

Coefficient of Determination (R-Squared)

Analysis of Variance or ANOVA

Multiple Regression Analysis

Multiple Regression and Coefficient of Determination (R-Squared)

Fcalc – the Global Test for Regression Significance

Regression Analysis and Assumption Violations

Qualitative and Dummy Variables in Regression Modeling

Time Series Analysis: Simple and Log-linear Trend Models

Auto-Regressive (AR) Time Series Models

Auto-Regressive Models - Random Walks and Unit Roots

ARMA Models and ARCH Testing

How to Select the Most Appropriate Time Series Model?

Confidence Intervals (CI) for Dependent Variable Prediction

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In all likelihood, your model will not perfectly predict Y.
The SEE can be extended to determine the confidence interval for a predicted Y value. A common CI to test for a predicted value is 95%.
Your regression parameters, the y-intercept (b0) and slope coefficient (b1) will need to be tested for significance before you can generate a confidence interval around your model’s project Y value around an expected X value.