Regression Intercept Confidence Interval, is a way to determine closeness of two factors and is used to check the reliability of estimation.
R=β0±t(1−α2,n−k−1)×SEβ0
Where −
β0 = Regression intercept.
k = Number of Predictors.
n = sample size.
SEβ0 = Standard Error.
α = Percentage of Confidence Interval.
t = t-value.
Problem Statement:
Compute the Regression Intercept Confidence Interval of following data. Total number of predictors (k) are 1, regression intercept β0 as 5, sample size (n) as 10 and standard error SEβ0 as 0.15.
Solution:
Let us consider the case of 99% Confidence Interval.
Step 1: Compute t-value where α=0.99.
=t(1−α2,n−k−1)=t(1−0.992,10−1−1)=t(0.005,8)=3.3554
Step 2: ≥Regression intercept:
=β0+t(1−α2,n−k−1)×SEβ0=5−(3.3554×0.15)=5−0.50331=4.49669
Step 3: ≤Regression intercept:
=β0−t(1−α2,n−k−1)×SEβ0=5+(3.3554×0.15)=5+0.50331=5.50331
As a result, Regression Intercept Confidence Interval is 4.49669 or 5.50331 for 99% Confidence Interval.