• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
Finance Train

Finance Train

High Quality tutorials for finance, risk, data science

  • Home
  • Data Science
  • CFA® Exam
  • PRM Exam
  • Tutorials
  • Careers
  • Products
  • Login

Confidence Intervals (CI) for Dependent Variable Prediction

CFA® Exam Level 2

This lesson is part 5 of 17 in the course Quantitative Methods
  • 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.
    • H0 = 0 is the null hypothesis when testing either parameter and you will look to reject this in significance, (note: typically the greater emphasis is on the slope coefficient, as b1 value not statistically different from zero indicates no relationship between Y and X).
    • tcalc = the standard script for the output of your significance test on the regression model’s parameters and its absolute value must exceed the designated tcritical on a two tailed significance test.
Previous Lesson

‹ Standard Error of the Estimate or SEE

Next Lesson

Coefficient of Determination (R-Squared) ›

Join Our Facebook Group - Finance, Risk and Data Science

Posts You May Like

How to Improve your Financial Health

CFA® Exam Overview and Guidelines (Updated for 2021)

Changing Themes (Look and Feel) in ggplot2 in R

Coordinates in ggplot2 in R

Facets for ggplot2 Charts in R (Faceting Layer)

Reader Interactions

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Primary Sidebar

In this Course

  • 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?

Latest Tutorials

    • Data Visualization with R
    • Derivatives with R
    • Machine Learning in Finance Using Python
    • Credit Risk Modelling in R
    • Quantitative Trading Strategies in R
    • Financial Time Series Analysis in R
    • VaR Mapping
    • Option Valuation
    • Financial Reporting Standards
    • Fraud
Facebook Group

Membership

Unlock full access to Finance Train and see the entire library of member-only content and resources.

Subscribe

Footer

Recent Posts

  • How to Improve your Financial Health
  • CFA® Exam Overview and Guidelines (Updated for 2021)
  • Changing Themes (Look and Feel) in ggplot2 in R
  • Coordinates in ggplot2 in R
  • Facets for ggplot2 Charts in R (Faceting Layer)

Products

  • Level I Authority for CFA® Exam
  • CFA Level I Practice Questions
  • CFA Level I Mock Exam
  • Level II Question Bank for CFA® Exam
  • PRM Exam 1 Practice Question Bank
  • All Products

Quick Links

  • Privacy Policy
  • Contact Us

CFA Institute does not endorse, promote or warrant the accuracy or quality of Finance Train. CFA® and Chartered Financial Analyst® are registered trademarks owned by CFA Institute.

Copyright © 2021 Finance Train. All rights reserved.

  • About Us
  • Privacy Policy
  • Contact Us