PHYSICS 102 Lecture Notes - Lecture 12: Hypothesis, Standard Deviation, Descriptive Statistics
Document Summary
Simple and multiple linear regression analysis: regression model: Question: the relation between variables, the influence of 1 or more independent variables on one dependence variable. Equation: 1 numerical dependent variable to be predicted/explained, one or more numerical independent variables (used to explain the variances in the dependent variable: linear regression model: relations between variables is a linear function. Population y-intercept (b0): where the regression line cuts the vertical axis y(x) Population slope (b1): is the regression coefficient, the estimation of the population slope. Random error (e): most of the time, we are unable to explain 100% of the variance of the dependent variable y, so there are errors left. The part of the variance that is unexplained by this model. This way of estimating tries to minimize all the errors parts of all the observed scored related to the estimated plane.
