# POL101Y1 Lecture Notes - Conditional Expectation, Population Model, Interval Estimation

## Document Summary

Modeling the relationship between two or more variables. Descriptive inference: point estimation, interval estimation, hypothesis testing. Statistical inference: causal inference, associational inference. Modeling the relationship between variables: bivariate regression model. Estimation example: economic voting in u. s: multivariate regression. Cannot afford to study everyone in the population, therefore we use a random sample: therefore the population dist of y = probability dist of y, population dist of continuous variable we use density. Again, cannot afford to study everything, therefore we use a random sample of x, y: population dist of x & y = joint probability of x & y. Population: joint prob dist of x & y, p(x,y) the height represents density. Conditional expectation of y given the value of x. The value of y we expect, given the certain value of x. The causal relationship we expect in social science is almost always probabilistic.