Review of Regression Analysis

# Review of Regression Analysis
## PSYC 575
### Mark Lai
### University of Southern California
### 2020/08/04 (updated: 2021-08-29)

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# Statistical Model

![](02_review_regression_files/figure-html/stat-model-1.png)

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## A set of statistical assumptions describing how data are generated

- Deterministic/fixed component

`$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \ldots$$`

- Stochastic/random component

`$$Y_i = \beta_0 + \beta_1 X_{1i} + \beta_2 X_{2i} + \ldots + \color{red}{e_i}$$`
`$$\color{red}{e_i \sim N(0, \sigma)}$$`

]

???

- It's only a review so I won't go deep. 
- You may check out the sections in the book by Gelman et al. 
- Model in OpenBoard
- Statistical notation
    * Notation for normal distribution
    * Important for MLM

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# Why Regression?

### MLM is an extension of multiple regression to deal with data from multiple levels

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# Learning Objectives

### Refresh your memory on regression

- Describe the statistical model

- Write out the model equations

- Simulate data based on a regression model

- Plot interactions

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# R Demonstration

???

Transition to RStudio

- Data Import
- Explain the variables

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# Salary Data

From [Cohen, Cohen, West & Aiken (2003)](https://doi.org/10.4324/9780203774441)

Examine factors related to annual salary of faculty in a university department

- time = years after receiving degree
- pub = # of publications
- sex = gender (0 = male, 1 = female)
- citation = # of citations
- salary = annual salary

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# Data Exploration

]

???

Explain what the x axis, y axis, diagonals are

Citation vs salary as an example

- How does the distribution of `salary` look?

- Are there more males or females in the data?

- How would you describe the relationship between number of publications and salary?

]

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# Simple Linear Regression

### Sample regression line

### Confidence intervals

### Centering

???

- Regression line is only a sample estimate; there is uncertainty
- Uncertainty measured by standard errors and confidence intervals
    * Show animations on the varying regression slopes
    * A function of sample size
- Centering: Draw picture on changing the x-axis
- Interpretations: unit increase in `$x$` associated with `$\beta$` unit increase in `$y$`

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# Simulation

See lecture and R code

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# Categorical Predictors

### Dummy Coding

With `$k$` categories, one needs `$k – 1$` dummy variables

The coefficients are differences relative to the reference group

]

![](02_review_regression_files/figure-html/p2-1.png)

]

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# Categorical Predictors

### Dummy Coding

With `$k$` categories, one needs `$k – 1$` dummy variables

The coefficients are differences relative to the reference group

Male = 0

`$$y = \beta_0 + \beta_1 (0) = \beta_0$$`

]

![](02_review_regression_files/figure-html/p2-1-1.png)

]

---

# Categorical Predictors

### Dummy Coding

With `$k$` categories, one needs `$k – 1$` dummy variables

The coefficients are differences relative to the reference group

Male = 0

`$$y = \beta_0 + \beta_1 (0) = \beta_0$$`

Female = 1

`$$y = \beta_0 + \beta_1 (1) = \beta_0 + \beta_1$$`

]

![](02_review_regression_files/figure-html/p2-2-1.png)

]

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# Multiple Regression

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# Partial Effects

`$$\text{salary}_i = \beta_0 + \beta_1 \text{pub}^c_i + \beta_2 \text{time}_i + e_i$$`

![](https://github.com/marklhc/marklai-pages/raw/master/static/img/regression_venn.png)

]

???

Transition to R

# Interpretations

### Every unit increase in `$X$` is associated with `$\beta_1$` unit increase in `$Y$`, **when all other predictors are constant**

]

---

# Interactions

### Regression slope of a predictor depends on another predictor

`\begin{align}
  \widehat{\text{salary}} & = 54238 + 105 \times \text{pub}^c + 964 \times \text{time}^c \\
  & \quad + 15 (\text{pub}^c)(\text{time}^c)
\end{align}`

`time` = 7 `$\Rightarrow$` `time_c` = 0.21

`\begin{align}
  \widehat{\text{salary}} & = 54238 + 105 \times \text{pub}^c + 964 (0.21) \\
  & \quad + 15 (\text{pub}^c)(0.21) \\
  & = 54440 + 120 \times \text{pub}^c
\end{align}`

`time` = 15 `$\Rightarrow$` `time_c` = 8.21

`\begin{align}
  \widehat{\text{salary}} & = 54238 + 105 \times \text{pub}^c + 964 (8.21) \\
  & \quad + 15 (\text{pub}^c)(8.21) \\
  & = 62152 + 228 \times \text{pub}^c
\end{align}`

---

# Interactions

### Regression slope of a predictor depends on another predictor

`\begin{align}
  \widehat{\text{salary}} & = 54238 + 105 \times \text{pub}^c + 964 \times \text{time}^c \\
  & \quad + 15 (\text{pub}^c)(\text{time}^c)
\end{align}`

`time` = 7 `$\Rightarrow$` `time_c` = 0.21

`\begin{align}
  \widehat{\text{salary}} & = 54238 + 105 \times \text{pub}^c + 964 (0.21) \\
  & \quad + 15 (\text{pub}^c)(0.21) \\
  & = 54440 + 120 \times \text{pub}^c
\end{align}`

`time` = 15 `$\Rightarrow$` `time_c` = 8.21

`\begin{align}
  \widehat{\text{salary}} & = 54238 + 105 \times \text{pub}^c + 964 (8.21) \\
  & \quad + 15 (\text{pub}^c)(8.21) \\
  & = 62152 + 228 \times \text{pub}^c
\end{align}`

]

]

---

# `modelsummary::msummary()`

```r
library(modelsummary)
msummary(list("M3 + Interaction" = m4),
         fmt = "%.1f")  # keep one digit
```

<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;"> M3 + Interaction </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> (Intercept) </td>
   <td style="text-align:center;"> 54238.1 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:center;"> (1183.0) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> pub_c </td>
   <td style="text-align:center;"> 104.7 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:center;"> (98.4) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> time_c </td>
   <td style="text-align:center;"> 964.2 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:center;"> (339.7) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> pub_c × time_c </td>
   <td style="text-align:center;"> 15.1 </td>
  </tr>
  <tr>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:center;box-shadow: 0px 1px"> (17.3) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Num.Obs. </td>
   <td style="text-align:center;"> 62 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 </td>
   <td style="text-align:center;"> 0.399 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 Adj. </td>
   <td style="text-align:center;"> 0.368 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> AIC </td>
   <td style="text-align:center;"> 1291.8 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> BIC </td>
   <td style="text-align:center;"> 1302.4 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Log.Lik. </td>
   <td style="text-align:center;"> −640.895 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> F </td>
   <td style="text-align:center;"> 12.817 </td>
  </tr>
</tbody>
</table>

]

---

# Summary

### Concepts

- What is a statistical model

- Linear/Multiple Regression

* Centering 
    
    * Categorical predictor
    
    * Interpretations
    
    * Interactions

### HW 2

### Try replicating the examples in the Rmd file