10 Matching Annotations
  1. Feb 2020
    1. assume that the average class in a district has 252525 students

      Assume that average student-to-teacher ratio in a district is 25 students. In regression model our dependent variable is student to teacher ratio not the size of the class. That's why, when we want to make a prediction we should take correct value for our explanatory variable.

      P.S. what is written would be correct if every class has one and only one teacher.

    1. all

      First of all,

    2. as

      "as" is not necessary here. This is very minor mistake but since you are doing excellent job I am going to point out any mistake I find to contribute the project towards perfection.

    1. data is collected

      data that is collected, or data collected

    2. and applications to forecasting and estimation of dynamic causal effects.

      and its applications to forecasting and estimation of dynamic causal effects.

      The word "its" is necessary since it refers to the noun econometric techniques.

  2. Jul 2019
    1. linear regression function

      This is linear regression model. Function is the deterministic (systematic) part of it without the error term.

    2. In a simple linear regression model, we model the relationship between both variables by a straight line, formally

      It would be better to start in the following way: "To build simple linear regression model, we hypothesize that the relationship between dependent and independent variable is linear, formally:"

      By saying that, In a simple linear regression model, we model the relationship between both variables by straight line is strictly speaking wrong. Strictly speaking, by straight line we model the relationship between regressors the expected value of the dependent variable given the value of the regressors.

      Then later, you point out that the relationship is not exact because not all points fall on the straight line and because of that you come up with an error terms and you continue in this way util you arrive to the final formulation of the Simple Linear Regression Model. (Which you do well later).