STA 506 2.0 Linear Regression Analysis

# STA 506 2.0 Linear Regression Analysis
## Lecture 5: Interpretation of Regression Outputs
### Dr Thiyanga S. Talagala
### 2020-09-25

---

## Recapping what we know

**True linear regression model/ Population regression equation**

$$\mu_{Y|X=x} = \beta_0 + \beta_1x $$

- how the mean of `$Y$` changes for given values of `$X$`.

- We can also write the equation in terms of the observed values of `$Y$`, rather than the mean.  Because the individual observations for any given value of `$X$` vary randomly about the mean of `$Y|X$`, we need to account for this random variation, or error, in the regression equation.  Sample regression line

`$$Y_i = \beta_0 + \beta_1x_i + \epsilon_i, \text{ i = 1, 2, 3...n}$$`

> Please  write the assumptions of `$\epsilon_i$`

**Fitted regression line**

`$$\hat{Y} = \hat{\beta}_0 + \hat{\beta}_1x$$`

---
# Revisiting the fitted regression model

## Fitted model

```r
library(alr3) # to load the dataset
```

```
Loading required package: car
```

```
Loading required package: carData
```

```r
model1 <- lm(Dheight ~ Mheight, data=heights)
model1
```

```

Call:
lm(formula = Dheight ~ Mheight, data = heights)

Coefficients:
(Intercept)      Mheight  
    29.9174       0.5417  
```

---
## Model summary

```r
summary(model1)
```

```

Call:
lm(formula = Dheight ~ Mheight, data = heights)

Residuals:
   Min     1Q Median     3Q    Max 
-7.397 -1.529  0.036  1.492  9.053

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 29.91744    1.62247   18.44   <2e-16 ***
Mheight      0.54175    0.02596   20.87   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.266 on 1373 degrees of freedom
Multiple R-squared:  0.2408,	Adjusted R-squared:  0.2402 
F-statistic: 435.5 on 1 and 1373 DF,  p-value: < 2.2e-16
```

---
# `$R^2$` = 24.08%

- Maybe you have one or more omitted variables. It is important to consider other factors that might influence the daughter's height:

- Father's height

- Physical activities performed by the daughter

- Food nutrition, etc.

- Maybe the functional form of the regression form is incorrect (so you have to add some quadratic, or cubic terms...). At the same time a transformation can be an alternative (if appropriate).

- Maybe could be the effect of a group of outlier (maybe not one...).

---

- A large `$R^2$` does not necessarily imply that the regression model will be an accurate predictor.

- `$R^2$` does not measure the appropriateness of the linear model.

- `$R^2$` will often large even though `$Y$` and `$X$` are nonlinearly related.

---
![Scatterplot of  price vs carat in diamonds (Pearson's correlation coefficient = 0.783)](regression4_files/figure-html/unnamed-chunk-3-1.png)

```
[1] 0.7834839
```
---

```

Call:
lm(formula = carat ~ price, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.91911 -0.66197 -0.07846  0.82734  2.97025

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.74996    0.23234   3.228   0.0017 ** 
price        0.18736    0.01501  12.481   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.137 on 98 degrees of freedom
Multiple R-squared:  0.6138,	Adjusted R-squared:  0.6099 
F-statistic: 155.8 on 1 and 98 DF,  p-value: < 2.2e-16
```

- `$R^2$` is large even though the linear approximation is poor.

> In general a good policy is to observe the scatterplot of your data

---
class:  duke-orange, center, middle

![](crystal.jpg)

Y - Crystal size
 
 X - Number of hours it takes crystal to grow

---
## Data set

```r
crystaldata <- data.frame(Size = c(0.08, 0.07,  1.12, 2.01, 4.43, 
                                   4.98, 4.92, 7.18, 
                                   5.57, 8.40, 8.81, 10.81, 11.61, 10.12, 13.12, 15.04, 0.075, 0.075,  1.13, 2.02, 4.45, 
                                   4.99, 4.95, 7.28, 
                                   5.67, 8.40, 8.51, 10.82, 11.62, 10.13, 13.22, 15.03),
  Time= c(2, 2, 4, 4,6, 8, 10, 12, 14, 16, 18, 20, 20, 24, 26, 28, 2, 2, 4, 4,6, 8, 10, 12, 14, 16, 18, 20, 20, 24, 26, 28))
crystaldata
```

```
     Size Time
1   0.080    2
2   0.070    2
3   1.120    4
4   2.010    4
5   4.430    6
6   4.980    8
7   4.920   10
8   7.180   12
9   5.570   14
10  8.400   16
11  8.810   18
12 10.810   20
13 11.610   20
14 10.120   24
15 13.120   26
16 15.040   28
17  0.075    2
18  0.075    2
19  1.130    4
20  2.020    4
21  4.450    6
22  4.990    8
23  4.950   10
24  7.280   12
25  5.670   14
26  8.400   16
27  8.510   18
28 10.820   20
29 11.620   20
30 10.130   24
31 13.220   26
32 15.030   28
```
---
## scatterplot

```r
ggplot(crystaldata, aes(x=Time, y = Size)) + geom_point()
```

![](regression4_files/figure-html/unnamed-chunk-6-1.png)

```r
cor(crystaldata$Time, crystaldata$Size)
```

```
[1] 0.9741803
```
---

## Fit a simple linear regression model

```r
mod1 <- lm(Size ~ Time, data=crystaldata)
mod1
```

```

Call:
lm(formula = Size ~ Time, data = crystaldata)

Coefficients:
(Intercept)         Time  
    -0.1982       0.5210  
```

---
## Visualise the model

```r
ggplot(crystaldata, aes(y=Size, x=Time))+geom_point(alpha=0.5) +
  geom_abline(intercept = -0.21, slope = 0.52, colour="forestgreen", lwd=2) +
            theme(aspect.ratio = 1)
```

![](regression4_files/figure-html/unnamed-chunk-8-1.png)

---
## Questions

1. How well does this equation fit the data?

2. Are any of the basic assumptions violated?

---

```r
summary(mod1)
```

```

Call:
lm(formula = Size ~ Time, data = crystaldata)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1855 -0.7643  0.0313  0.7405  1.5223

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.19821    0.34838  -0.569    0.574    
Time         0.52099    0.02204  23.634   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.05 on 30 degrees of freedom
Multiple R-squared:  0.949,	Adjusted R-squared:  0.9473 
F-statistic: 558.5 on 1 and 30 DF,  p-value: < 2.2e-16
```

---
## Coefficient of determination

```

Call:
lm(formula = Size ~ Time, data = crystaldata)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1855 -0.7643  0.0313  0.7405  1.5223

Residual standard error: 1.05 on 30 degrees of freedom
Multiple R-squared:  0.949,	Adjusted R-squared:  0.9473 
F-statistic: 558.5 on 1 and 30 DF,  p-value: < 2.2e-16
```

- 94.5% of the variability in size is accounted by the regression model.

- 94.5% of the variation in size  is explained by the number of hours it takes crystal to grow.

---
## Are any of the basic assumptions violated?

```r
mod1
```

```

Call:
lm(formula = Size ~ Time, data = crystaldata)

Coefficients:
(Intercept)         Time  
    -0.1982       0.5210  
```

---

## Compute Residuals and Fitted values

```r
library(broom)
mod1_fitresid <- augment(mod1)
mod1_fitresid
```

```
# A tibble: 32 x 9
    Size  Time .fitted .se.fit  .resid   .hat .sigma  .cooksd .std.resid
   <dbl> <dbl>   <dbl>   <dbl>   <dbl>  <dbl>  <dbl>    <dbl>      <dbl>
 1  0.08     2   0.844   0.312 -0.764  0.0883   1.06 0.0281      -0.762 
 2  0.07     2   0.844   0.312 -0.774  0.0883   1.06 0.0289      -0.772 
 3  1.12     4   1.89    0.278 -0.766  0.0700   1.06 0.0215      -0.756 
 4  2.01     4   1.89    0.278  0.124  0.0700   1.07 0.000567     0.123 
 5  4.43     6   2.93    0.247  1.50   0.0552   1.03 0.0634       1.47  
 6  4.98     8   3.97    0.220  1.01   0.0440   1.05 0.0223       0.984 
 7  4.92    10   5.01    0.200 -0.0917 0.0363   1.07 0.000149    -0.0890
 8  7.18    12   6.05    0.188  1.13   0.0321   1.05 0.0197       1.09  
 9  5.57    14   7.10    0.186 -1.53   0.0314   1.03 0.0354      -1.48  
10  8.4     16   8.14    0.194  0.262  0.0343   1.07 0.00115      0.254 
# … with 22 more rows
```

---

### 1) The relationship between the response `$Y$` and the regressors is linear, at least approximately.

Plot of residuals vs fitted values and Plot of residuals vs predictor

Residuals vs Fitted

![](regression4_files/figure-html/unnamed-chunk-13-1.png)
]

Residuals vs Time

![](regression4_files/figure-html/unnamed-chunk-14-1.png)
]

---

## 2) The error term `$\epsilon$` has zero mean.

## 3) The error term `$\epsilon$` has constant variance `$\sigma^2$`.

Residuals vs Fitted

![](regression4_files/figure-html/unnamed-chunk-15-1.png)

---

## 4) The error are uncorrelated.

---
## 5) The errors are normally distributed.

```r
ggplot(mod1_fitresid, 
       aes(x=.resid))+
  geom_histogram(colour="white")+ggtitle("Distribution of Residuals")
```

![](regression4_files/figure-html/unnamed-chunk-16-1.png)
]

```r
ggplot(mod1_fitresid, 
       aes(sample=.resid))+
  stat_qq() + stat_qq_line()+labs(x="Theoretical Quantiles", y="Sample Quantiles")
```

![](regression4_files/figure-html/unnamed-chunk-17-1.png)

]

```r
shapiro.test(mod1_fitresid$.resid)
```

```

Shapiro-Wilk normality test

data:  mod1_fitresid$.resid
W = 0.95397, p-value = 0.1867
```

---
## Interpretations

$$\mu_{Y|X=x} = \beta_0 + \beta_1x $$

**Slope:**

Increase in the mean response for every one unit increase in the explanatory variable.

**Intercept:**

Mean response when the explanatory variable equals 0

```r
mod1
```

```

Call:
lm(formula = Size ~ Time, data = crystaldata)

Coefficients:
(Intercept)         Time  
    -0.1982       0.5210  
```

---
## Nonsensical intercept interpretation

- Sometimes it doesn't make sense to interpret the intercept: when?

- `$X$` can never realistically take the value of zero, then the intercept is meaningless.

- The intercept is negative even though the response can take only positive values.

- The intercept helps the line fit the data as closely as possible.

- It is fine to have a regression model with nonsensical intercept if it helps the model give better overall prediction accuracy.

---

## Hypothesis testing on the slope and intercept
---

## Hypothesis testing - Intercept

`$H_0: \beta_1=0$`

`$H_1: \beta_1 \neq 0$`

```r
summary(mod1)
```

```

Call:
lm(formula = Size ~ Time, data = crystaldata)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1855 -0.7643  0.0313  0.7405  1.5223

Residual standard error: 1.05 on 30 degrees of freedom
Multiple R-squared:  0.949,	Adjusted R-squared:  0.9473 
F-statistic: 558.5 on 1 and 30 DF,  p-value: < 2.2e-16
```

---
## Situations where `$H_0: \beta_1=0$` is not rejected.

In-class

---
## Situations where `$H_0: \beta_1=0$` is rejected.

In-class

---
## Confidence Interval on `$\beta_0$` and `$\beta_1$`

- Confidence intervals `$\beta_0$`

`$$[\hat{\beta_0} - t_{\alpha/2, n-2}se(\hat{\beta_0}), \hat{\beta_0} + t_{\alpha/2, n-2}se(\hat{\beta_0})]$$`

- Confidence intervals `$\beta_1$`

`$$[\hat{\beta_1} - t_{\alpha/2, n-2}se(\hat{\beta_1}), \hat{\beta_1} + t_{\alpha/2, n-2}se(\hat{\beta_1})]$$`

---

## t-distribution

`$t_{0.025, 30}$`

```r
qt(0.975, df=30)
```

```
[1] 2.042272
```

![](regression4_files/figure-html/unnamed-chunk-22-1.png)

```r
qt(0.025, df=30)
```

```
[1] -2.042272
```

---

```r
summary(mod1)
```

```

Call:
lm(formula = Size ~ Time, data = crystaldata)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1855 -0.7643  0.0313  0.7405  1.5223

Residual standard error: 1.05 on 30 degrees of freedom
Multiple R-squared:  0.949,	Adjusted R-squared:  0.9473 
F-statistic: 558.5 on 1 and 30 DF,  p-value: < 2.2e-16
```

---
## Confidence Interval on `$\beta_0$` and `$\beta_1$` - Interpretation

- Confidence intervals `$\beta_0$`

`$$[\hat{\beta_0} - t_{\alpha/2, n-2}se(\hat{\beta_0}), \hat{\beta_0} + t_{\alpha/2, n-2}se(\hat{\beta_0})]$$`

- Confidence intervals `$\beta_1$`

`$$[\hat{\beta_1} - t_{\alpha/2, n-2}se(\hat{\beta_1}), \hat{\beta_1} + t_{\alpha/2, n-2}se(\hat{\beta_1})]$$`

Frequency interpretation:

If we were to take repeated samples of the same size at the same `$X$` levels and construct, for example, `$95\%$` confidence intervals on the slope for each sample, then `$95\%$` of those intervals will contain the true value `$\beta_1$`.

We therefore have considerable *confidence* that the observed interval, here (04792, 0.5608), covers the true `$\beta_1$`. The measure of confidence is .95. Here, .95 is called the confidence coefficient.

In general, the larger the confidence coefficient `$(1-\alpha)$` is, the wider the confidence interval.

## Analysis of variance - ANOVA

- We may also use ANOVA to test the significance of regression.

- The idea behind the ANOVA  is that the variation in the response variable (referred to as the total variation - **SST**) is partitioned into two parts: 
 
> i. how much of the variation is explained by the regression model (referred to as the variable name in the R output) - **SSM (Model sum of squares)**
 
> ii. how much of the variation is left unexplained by the regression model (called the “Residual” or “Error”) - **SSE (Residual or error sum of squares)**.

`$$\sum_{i=1}^n(y_i - \bar{y})^2=\sum_{i=1}^n(\hat{Y}_i - \bar{y})^2 + \sum_{i=1}^n(y_i - \hat{Y_i})^2$$`

`$$SST = SSM + SSE$$`

Degrees of component

`$$df_T = df_M + df_E$$`
`$$n-1 = 1 + (n-2)$$`
---

## ANOVA  - F test

`$$F = \frac{SSM/df_M}{SSE/df_E} = \frac{MS_M}{MS_E}$$`

`$$E(MS_E) = \sigma^2$$`
---

## ANOVA - Hypothesis

`$$H_0: \beta_1=0$$`
`$$H_1: \beta_1 \neq 0$$`

- In simple linear regression t-test is equivalent to the F test.

- The real usefulness of the analysis of variance is in multiple regression models.

```r
anova(mod1)
```

```
Analysis of Variance Table

Response: Size
          Df Sum Sq Mean Sq F value    Pr(>F)    
Time       1 615.46  615.46  558.55 < 2.2e-16 ***
Residuals 30  33.06    1.10                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```
---

```r
summary(mod1)
```

```

Call:
lm(formula = Size ~ Time, data = crystaldata)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1855 -0.7643  0.0313  0.7405  1.5223

Residual standard error: 1.05 on 30 degrees of freedom
Multiple R-squared:  0.949,	Adjusted R-squared:  0.9473 
F-statistic: 558.5 on 1 and 30 DF,  p-value: < 2.2e-16
```

---
### Regression through the origin (No intercept)

-  Appropriate in analysing data from chemical and other manufacturing processes.

- Do not misuse the no-intercept model: where the data lie in a region of `$x$`-space remote from the origin.

- Scatterplot sometimes helps in deciding whether or not to fit the no-intercept model.

How to select the model: Fit both models (with intercept and without intercept) and choose between them based on the quality of the fit.

```r
mod2 <- lm(Size ~ Time -1, data=crystaldata)
mod2
```

```

Call:
lm(formula = Size ~ Time - 1, data = crystaldata)

Coefficients:
  Time  
0.5104  
```

---

```r
summary(mod2)
```

```

Call:
lm(formula = Size ~ Time - 1, data = crystaldata)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.12895 -0.92631 -0.04059  0.78642  1.41254

Coefficients:
     Estimate Std. Error t value Pr(>|t|)    
Time  0.51037    0.01161   43.95   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.038 on 31 degrees of freedom
Multiple R-squared:  0.9842,	Adjusted R-squared:  0.9837 
F-statistic:  1931 on 1 and 31 DF,  p-value: < 2.2e-16
```
---

With intercept

```r
ggplot(crystaldata, aes(y=Size, x=Time))+geom_point(alpha=0.5) +
  geom_abline(intercept = -0.21, slope = 0.52, colour="forestgreen", lwd=2) +
            theme(aspect.ratio = 1)
```

![](regression4_files/figure-html/unnamed-chunk-29-1.png)

]

```r
ggplot(crystaldata, aes(y=Size, x=Time))+geom_point(alpha=0.5) +
  geom_abline(intercept = 0, slope = 0.51, colour="red", lwd=2) +
            theme(aspect.ratio = 1)
```

![](regression4_files/figure-html/unnamed-chunk-30-1.png)

]

Perform the residual analysis on the new model before using it.
---

## Prediction of new observations

---

```r
test <- data.frame(Time = c(2, 2, 6, 6, 8, 20, 26))
test
```

```
  Time
1    2
2    2
3    6
4    6
5    8
6   20
7   26
```

```r
predict(mod2, newdata=test, interval="predict") 
```

```
        fit        lwr       upr
1  1.020746 -1.0972070  3.138699
2  1.020746 -1.0972070  3.138699
3  3.062237  0.9400507  5.184424
4  3.062237  0.9400507  5.184424
5  4.082983  1.9570988  6.208867
6 10.207457  8.0376929 12.377222
7 13.269695 11.0645387 15.474851
```

---
## Next Lecture

> Multiple linear regression
---
class: center, middle

Acknowledgement

Introduction to Linear Regression Analysis, Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

[Dr. Thiyanga S. Talagala](https://thiyanga.netlify.app/)