04-Linear-Regression.Rmd

# (PART) 線性迴歸 Linear Regression {-}

# 簡單線性迴歸 Simple Linear Regression {#lm}


> Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong questions, which can always be made precise.
> ~ John Tukey


```{block2, note-thankJenny, type='rmdnote'}
The Linear Regression lectures were orgainised and taught by Professor [Jennifer Nicholas](https://www.lshtm.ac.uk/aboutus/people/nicholas.jennifer).
```


```{r lm-Session01, child = ('04-Linear-Regression/Session01.Rmd')}

```

## LM practical 01 {#exeChol}

```{r lm-Practical01, child = ('04-Linear-Regression/Practical01.Rmd')}

```


# 最小二乘估計的性質和推斷 Ordinary Least Squares Estimators and Inference {#OLS}

```{r lm-Session02, child = ('04-Linear-Regression/Session02.Rmd')}

```


## LM practical 02

```{r lm-Practical02, child = ('04-Linear-Regression/Practical02.Rmd')}

```




# 方差分析 Introduction to Analysis of Variance {#ANOVA}

```{r lm-Session03, child = ('04-Linear-Regression/Session03.Rmd')}

```

## LM practical 03

```{r lm-Practical03, child = ('04-Linear-Regression/Practical03.Rmd')}

```


# 多元模型分析 Multivariable Models {#multivariable-models}


```{r lm-Session04, child = ('04-Linear-Regression/Session04.Rmd')}

```


## LM practical 04

```{r lm-Practical04, child = ('04-Linear-Regression/Practical04.Rmd')}

```


# 多元模型分析：矩陣標記與其意義 {#matrix-multivariable}


```{r lm-Session05, child = ('04-Linear-Regression/Session05.Rmd')}

```


## LM practical 05

```{r lm-Practical05, child = ('04-Linear-Regression/Practical05.Rmd')}

```


# 線性迴歸的模型診斷 {#lm-diag}


```{r lm-Session06, child = ('04-Linear-Regression/Session06.Rmd')}

```


## LM practical 06

```{r lm-Practical06, child = ('04-Linear-Regression/Practical06.Rmd')}

```



# 交互作用 Interactions {#interaction}


```{r lm-Session07, child = ('04-Linear-Regression/Session07.Rmd')}

```


## LM practical 07

```{r lm-Practical07, child = ('04-Linear-Regression/Practical07.Rmd')}

```