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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.
  • 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.
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Standard Error of the Estimate or SEE

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Coefficient of Determination (R-Squared)

01
CFA L2: Quantitative Methods - Introduction
02
Quants: Correlation Analysis
03
Quants: Single Variable Linear Regression Analysis
04
Standard Error of the Estimate or SEE
05
Confidence Intervals (CI) for Dependent Variable Prediction
06
Coefficient of Determination (R-Squared)
07
Analysis of Variance or ANOVA
08
Multiple Regression Analysis
09
Multiple Regression and Coefficient of Determination (R-Squared)
10
Fcalc – the Global Test for Regression Significance
11
Regression Analysis and Assumption Violations
12
Qualitative and Dummy Variables in Regression Modeling
13
Time Series Analysis: Simple and Log-linear Trend Models
14
Auto-Regressive (AR) Time Series Models
15
Auto-Regressive Models - Random Walks and Unit Roots
16
ARMA Models and ARCH Testing
17
How to Select the Most Appropriate Time Series Model?

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Risk Management
Statistical Methods