A range of values likely to contain the true population parameter.

The single best estimate of a population parameter, like the sample slope.

The 'buffer zone' around the point estimate, calculated using the t-score and standard error.

The differences between observed y-values and y-values predicted by the regression line.

Measures the spread of data points around the regression line; also known as the standard error of the estimate.

T-distribution: Used when population standard deviation is unknown, heavier tails | Normal distribution: Used when population standard deviation is known, lighter tails.

Point estimate: Single value estimate of a parameter | Confidence interval: Range of plausible values for a parameter.

Standard deviation: Measures the spread of data around the mean | Standard error of the slope: Measures the variability of the sample slope.

90% CI: Narrower, less confidence | 99% CI: Wider, more confidence.

Confidence Interval: Provides a range for the true parameter. | Confidence Level: Indicates the success rate of the method if repeated many times.

$SE(b) = \frac{s}{\sqrt{\sum(x_i - \bar{x})^2}}$, where s is the standard deviation of the residuals.

Margin of Error = t-score * Standard Error of the slope.

Point Estimate ± Margin of Error

df = n - 2, where n is the sample size.

Sample Slope ± (t-critical value * Standard Error of the slope)