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This summarizes key concepts and directions for performing linear regression. Most of the steps are taken from Duke University’s Linear Regression and Modeling course on coursera.


Linear Regression

1. Correlation

  • correlation is the strength of linear association

  • correlation coefficients are sensitive to outliers

\(R = cor(x,y). R^2 = (correlation)^2\)

This is the correlation code for a table (x=temp, y=sound).

cor <- cricket %>% 
  summarise(r=cor(sound, temp)) %>% 
  pull(r)
cor
[1] 0.8351438

This is the scatterplot to see the points.

ggplot(cricket, aes(x=temp, y=sound))+
  geom_point(alpha=0.5)+
  geom_smooth(method = "lm", se=F)

Version Author Date
8ee3b5d KaranSShakya 2020-05-27

2. Residuals

Residuals are the difference between observed and predicted values. To visualize this we have used the broom package to test the residuals.

\(Residuals (errors) = observed - predicted\)

lm <- lm(sound~temp, data=cricket)
lm.table <- augment(lm) #can visualize all the residuals in a table form

ggplot(lm.table, aes(x=.fitted, y=.resid))+ geom_point(alpha=0.5)

3. Least Square Lines

Best way to have a linear regression line is to minimize the sum of squared residuals.

\(Slope(b_1 = SD_y/SD_x * R)\)

lm.sd <- lm.table %>% 
  summarize(sound.sd=sd(sound), temp.sd=sd(temp), cor=cor(sound, temp)) %>% 
  mutate(slope=(sound.sd/temp.sd)*cor) #Slope = 0.211

When we look at the lm model, the slope is also 0.211.

summary(lm)

4. Conditions for Linear Regression

a. Linearity (scatterplot + residual plot - residuals needs to be random)

b. Nearly normal residuals (histogram of residuals or QQ residual plot)

c. Constant variability (residual plot)

Link for interactive regression diagnostic test.

a <- ggplot(lm.table, aes(x=.fitted, y=.resid))+
  geom_point()+
  geom_hline(yintercept = 0, linetype="dashed", color="red")+
  labs(title="Residuals vs Fitted Values", x="Fitted Values", y="Residuals")
b <- ggplot(lm.table, aes(x=.resid))+
  geom_density()+
  labs(title="Histogram of residuals", x="Residuals") #geom_density can also be added
c <- ggplot(lm.table, aes(sample=.resid))+
  stat_qq()+
  stat_qq_line()
grid.arrange(a, b, c, ncol=3)

Version Author Date
11e02b8 KaranSShakya 2020-05-27

5. Inference

  • Hypothesis testing on the slope to identify if the explanatory variable is a significant predictor.

  • Null hyp: H0 = 0 (no relationship). Alt hyp: H1 not 0 (yes relationship).

\(t-stat = (pointestimate - null value) / SE\)

summary(lm)

Call:
lm(formula = sound ~ temp, data = cricket)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.56009 -0.57930  0.03129  0.59020  1.53259 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.30914    3.10858  -0.099 0.922300    
temp         0.21192    0.03871   5.475 0.000107 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9715 on 13 degrees of freedom
Multiple R-squared:  0.6975,    Adjusted R-squared:  0.6742 
F-statistic: 29.97 on 1 and 13 DF,  p-value: 0.0001067

t value can be foudn by: (0.211 - 0) / 0.039 = 5.4

For 95% confidence interval (CI): 0.211 +- 2.06 x 0.0387 = (0.13, 0.29)

6. ANOVA

Analysis of Variance

anova(lm)
Analysis of Variance Table

Response: sound
          Df Sum Sq Mean Sq F value    Pr(>F)    
temp       1 28.287 28.2873   29.97 0.0001067 ***
Residuals 13 12.270  0.9438                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  • F-Value: F-Statistics: MeanSQ (temp) / MeanSQ (residuals)

\(R^2 = SS(reg)/SS(total) = 28.287/30.5\)

7. Multi-Variable Linear Regression

  • R-square will always increase with every inclusion.

  • For multiple variables, adjusted R-square is important.



sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gridExtra_2.3   broom_0.5.6     readxl_1.3.1    forcats_0.5.0  
 [5] stringr_1.4.0   dplyr_0.8.5     purrr_0.3.4     readr_1.3.1    
 [9] tidyr_1.0.3     tibble_3.0.1    ggplot2_3.3.0   tidyverse_1.3.0
[13] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6     lubridate_1.7.8  lattice_0.20-41  assertthat_0.2.1
 [5] rprojroot_1.3-2  digest_0.6.25    R6_2.4.1         cellranger_1.1.0
 [9] backports_1.1.6  reprex_0.3.0     evaluate_0.14    httr_1.4.1      
[13] pillar_1.4.4     rlang_0.4.6      rstudioapi_0.11  whisker_0.4     
[17] Matrix_1.2-18    rmarkdown_2.1    labeling_0.3     splines_4.0.0   
[21] munsell_0.5.0    compiler_4.0.0   httpuv_1.5.2     modelr_0.1.7    
[25] xfun_0.13        pkgconfig_2.0.3  mgcv_1.8-31      htmltools_0.4.0 
[29] tidyselect_1.1.0 fansi_0.4.1      crayon_1.3.4     dbplyr_1.4.3    
[33] withr_2.2.0      later_1.0.0      grid_4.0.0       nlme_3.1-147    
[37] jsonlite_1.6.1   gtable_0.3.0     lifecycle_0.2.0  DBI_1.1.0       
[41] git2r_0.27.1     magrittr_1.5     scales_1.1.1     cli_2.0.2       
[45] stringi_1.4.6    farver_2.0.3     fs_1.4.1         promises_1.1.0  
[49] xml2_1.3.2       ellipsis_0.3.0   generics_0.0.2   vctrs_0.3.0     
[53] tools_4.0.0      glue_1.4.1       hms_0.5.3        yaml_2.2.1      
[57] colorspace_1.4-1 rvest_0.3.5      knitr_1.28       haven_2.2.0