Why Everyone, Yes Everyone, Should Learn Statistics

Why Everyone, Yes Everyone, Should Learn Statistics

We're Not so Good at Statistics

• Votes in the U.S. House of Representatives in favor of passing the Civil Rights Act of 1964:

We're Not so Good at Statistics

• Votes in the U.S. House of Representatives in favor of passing the Civil Rights Act of 1964:

• Simple enough: "on average, Republicans tended to vote for passage more than Democrats"

We're Not so Good at Statistics

• Broken down further by Northern vs. Southern states:

• What's going on?

We're Not so Good at Statistics

• Broken down further by Northern vs. Southern states:

• What's going on?

• A far greater proportion of Democrats (94/248, 38%) than Republicans (10/172, 6%) were from the South

We're Not so Good at Statistics

• Broken down further by Northern vs. Southern states:

• What's going on?

• A far greater proportion of Democrats (94/248, 38%) than Republicans (10/172, 6%) were from the South

• The 7% of southern Democrats voting for the Act dragged down the Democrats' overall percentage more than the 0% of southern Republicans

Simpson's Paradox

Simpson's Paradox

Economic Theory and Economic Models

• Economic theorizing often involves building a formal model to relate economic phenomena and build intuitions

Economic Theory and Economic Models

• Economic theorizing often involves building a formal model to relate economic phenomena and build intuitions

• We can at least predict the "sign" of each relationship between $y$ and each $x_i$ (then estimate the quantitative impact)

Example Code

ggplot(data = gapminder, aes(x = gdpPercap, 
    y = lifeExp, color = continent, fill= continent))+
    geom_point()+geom_smooth(method = "lm") +
     scale_x_log10()+ylab("Life Expectancy (Years)")+
     xlab("Log GDP/Capita")

This Class Is

This Class Is

Example

Example II

## 
## Call:
## lm(formula = hwy ~ displ, data = mpg)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1039 -2.1646 -0.2242  2.0589 15.0105 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  35.6977     0.7204   49.55   <2e-16 ***
## displ        -3.5306     0.1945  -18.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.836 on 232 degrees of freedom
## Multiple R-squared:  0.5868,    Adjusted R-squared:  0.585 
## F-statistic: 329.5 on 1 and 232 DF,  p-value: < 2.2e-16

Example III

## 
## Call:
## lm(formula = hwy ~ displ, data = mpg)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1039 -2.1646 -0.2242  2.0589 15.0105 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  35.6977     0.7204   49.55   <2e-16 ***
## displ        -3.5306     0.1945  -18.15   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.836 on 232 degrees of freedom
## Multiple R-squared:  0.5868,    Adjusted R-squared:  0.585 
## F-statistic: 329.5 on 1 and 232 DF,  p-value: < 2.2e-16

DAGs