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Trial and Standard Error: Learn Simple Regression

Reverse Regression
Confidence Interval

Residuals i Residuals are the differences between observed values and the values predicted by a regression model. They are used to assess the fit of the model, with smaller residuals indicating a better fit.

Intercept i The intercept is the value at which the regression line crosses the y-axis in a linear regression model. It represents the predicted value of the dependent variable when all independent variables are zero. Slope i The slope is a measure in a linear regression that represents the expected change in the dependent variable for a one-unit change in the independent variable. It indicates the strength and direction of the relationship between variables.
Std. Error: i The standard error measures the precision with which a sample mean estimates the true population mean. It indicates how much the sample mean is expected to vary from sample to sample.
t-Statistic: i The t-statistic is a ratio that compares the difference between the sample mean and the population mean relative to the variability in the sample data. It is used in a t-test to determine whether to reject the null hypothesis.

© 2023 Luke M. Froeb, Keyuan Jiang & Gray Curtis