Statistics with R Part01Week01

Coursera 2018.01

Designing Studies

1. Introduction

• populations and samples

2. Data Basics

• data matrix

  • observation(case)

  • variable

  

• types of variables

• relationships between variables

  • Two variables that show some connection with one another are called associated (dependent).

  • Association can be further described as positive or negative.

  • If two variables are not associated, they are said to be independent.

3. observational studies & experiments

• define observational studies and experiments

  

  最主要的区别是experiments利用了random assignment
  observational study就是只是观察，得到observation

• confounding variables

  extraneous variables that affect both the explanatory and the response variable,
  and that make it seem like there is a relationship between them

• correlation does not imply causation

  两个事件或变量相关并意味着，他们之间一定存在着因果（causation）关系。

4. sampling & sources of bias

• Why sampling？

  我们为什么要采样呢？

  考虑人口普查。第一很难，非法移民之类；第二人口总数（总体）并不是stand still。

• a few sources of sampling bias

  • Convenience sample

    Individuals who are easily accessible are more likely to be included in the sample

  • Non-response

    If only a (non-random) fraction of the randomly sampled people respond to a survey such that the sample is no longer representative of the population

    随机采样对的样本中一部分不给于回应。

  • Voluntary response

    Occurs when the sample consists of people who volunteer to respond because they have strong opinions on the issue

  这些都导致样本并不具有代表性，并不能代表总的样本

• sampling methods

  • simple random sample (SRS)

    

  • stratified sample

    分层采样

    例如把受采样的人群分为男\女分别采样。

    

  • cluster sample

    heterogeneous

    The clusters, unlike strata and stratified sampling, are heterogeneous within themselves, and each cluster is similar to another, such that we can get away with just sampling from a few of the clusters.

    

  • multistage sample

    基于cluster sample的基础之上，每个cluster中随机选择一些样本。

    例如：将城市划分为多个区域，在每个区域中随机选择一些样本。

    

5. experimental design

• principles of experimental design

  

• blocking vs. explanatory variables

  • explanatory variables (factors) - conditions we can impose on experimental units

  • blocking variables - characteristics that the experimental units come with, that we would like to control for

  

• experimental terminology

  

6. random sampling vs. assignment