Section 6: Linear Models
In this section, we cover linear models. We start by looking at categorical models, in which data gets binned into categories. We use this simple framework to introduce measures like mean, variance, and R-squared. We then turn to linear models describing what linear models do, how to read regression output (a valuable skill!) and how to fit nonlinear data with linear models. These lectures are meant to give you a "feel" for how linear models are used and perhaps to motivate you to take a course on these topics. I conclude this section by highlighting a distinction between what I call Big Coefficient thinking and New Reality thinking. The readings for this section consist of two short pieces written by me, but you can find abundant resources on the web on linear models, R-squared, regression, and evidence based thinking.
Categorical Models
Linear Models
Vocabulary learning:
life expectancy 预期寿命
independent variable
dependent variable
magnitude n. 大小；量级；[地震] 震级；重要；光度
liability n. 责任；债务；倾向；可能性；不利因素
intuition 直觉
sign 符号，迹象
magnitude 大小，量级
a bunch of stuff 很多事
quadrant 象限，四分之一
spine method
approximate 近似
nonlinear term 非线性项
placeholder 占位符
akin 类似的
short cut 快捷方式
philanthropy 博爱，慈善事业
evidence based blank
construct model, gather data, identify important variables, change those variables
gather data, find pattern, identify important variables, change those variables
obviate 排除，消除
linear model tell sign and magnitude of changes in independent variables within data range.
extrapolate 外推
plateau 高原，稳定水平
marginal change 边界效应
oat bran pretzel 燕麦麸椒盐卷饼
payroll 工资单
2013-10-27 (120min) Coursera open course: Model Thinking by Scott E. Page Start from 10.07.2013, will end on 12.16.2013 Link: https://class.coursera.org/modelthinking-005/class/index;
Section 6: Linear Models In this section, we cover linear models. We start by looking at categorical models, in which data gets binned into categories. We use this simple framework to introduce measures like mean, variance, and R-squared. We then turn to linear models describing what linear models do, how to read regression output (a valuable skill!) and how to fit nonlinear data with linear models. These lectures are meant to give you a "feel" for how linear models are used and perhaps to motivate you to take a course on these topics. I conclude this section by highlighting a distinction between what I call Big Coefficient thinking and New Reality thinking. The readings for this section consist of two short pieces written by me, but you can find abundant resources on the web on linear models, R-squared, regression, and evidence based thinking.
Categorical Models Linear Models
Vocabulary learning: life expectancy 预期寿命 independent variable dependent variable magnitude n. 大小；量级；[地震] 震级；重要；光度 liability n. 责任；债务；倾向；可能性；不利因素 intuition 直觉 sign 符号，迹象 magnitude 大小，量级 a bunch of stuff 很多事 quadrant 象限，四分之一 spine method approximate 近似 nonlinear term 非线性项 placeholder 占位符 akin 类似的 short cut 快捷方式 philanthropy 博爱，慈善事业 evidence based blank construct model, gather data, identify important variables, change those variables gather data, find pattern, identify important variables, change those variables obviate 排除，消除 linear model tell sign and magnitude of changes in independent variables within data range. extrapolate 外推 plateau 高原，稳定水平 marginal change 边界效应 oat bran pretzel 燕麦麸椒盐卷饼 payroll 工资单