options(scipen = 999)

Make sure to include the unit of the values whenever appropriate.

Q1 Build a regression model to predict life expectancy using gdp per capita.

Hint: The variables are available in the gapminder data set from the gapminder package. Note that the data set and package both have the same name, gapminder.

data(gapminder, package="gapminder")
houses_lm <- lm(lifeExp ~ gdpPercap, 
                data = gapminder)

# View summary of model 1
summary(houses_lm)
## 
## Call:
## lm(formula = lifeExp ~ gdpPercap, data = gapminder)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -82.754  -7.758   2.176   8.225  18.426 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 53.95556088  0.31499494  171.29 <0.0000000000000002 ***
## gdpPercap    0.00076488  0.00002579   29.66 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.49 on 1702 degrees of freedom
## Multiple R-squared:  0.3407, Adjusted R-squared:  0.3403 
## F-statistic: 879.6 on 1 and 1702 DF,  p-value: < 0.00000000000000022

Q2 Is the coefficient of gdpPercap statistically significant at 5%?

Yes, the coefficient of gdpPercap is statistically significant at 5% because the p-value is actually much smaller than even 1%. This meqans that the data has over 99% credibility. ## Q3 Interpret the coefficient of gdpPercap. With the coefficient of gdpPercap being .00076488, this means that as gdpPercap increases by $1, the life expectancy of the individual increases by .00076488 years.

Q4 Interpret the Intercept.

With the intercept value being 53.955, this means that if you’re born with a $0 gdpPercap, your life expectancy at birth is 53.95 years.

Q5 Build another model that predicts life expectancy using gdpPercap, but also controls for another important variable, year.

Hint: This is a model with two explanatory variables. Insert another code chunk below.

data(gapminder, package="gapminder")
houses_lm <- lm(lifeExp ~ year, gdpPercap,
                data = gapminder)

# View summary of model 1
summary(houses_lm)
## 
## Call:
## lm(formula = lifeExp ~ year, data = gapminder, subset = gdpPercap)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.221  -9.436   1.517  11.201  21.581 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) -573.69800   56.15343  -10.22 <0.0000000000000002 ***
## year           0.31998    0.02837   11.28 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.86 on 580 degrees of freedom
##   (1122 observations deleted due to missingness)
## Multiple R-squared:  0.1799, Adjusted R-squared:  0.1784 
## F-statistic: 127.2 on 1 and 580 DF,  p-value: < 0.00000000000000022

Q6 Which of the two models is better?

In the first model, the residual standard error is 10.49, while in the second model, the residual standard error is 11.86. This means that the first model misses 10.49 people, while the second model misses 11.86 people. The R-squared value for the first model is .3403, while the second models is .1784. These values mean that the first models data points are going to be further to the line of regression than the second models. With these numbers, I’d say the first model is better because the model misses less people, althoughg the second models data points will be closer to the line of regression.

Q7 Interpret the coefficient of year.

With the coefficient of year being .31998, this means that for every year you’re born after 1952, your life expectancy increases by .31998 years.

Q7.a Based on the second model, what is the predicted life expectancy for a country with gdpPercap of $40,000 a year in 1997.

Hint: We had this discussion in class while watching the video at DataCamp, Correlation and Regression in R. The video is titled as “Interpretation of Regression” in Chapter 4: Interpreting Regression Models. Based on the second model, the predicted life expectancy for a country with a gdpPercap of $40,000 in 1997 is 76.49 years.

Q8 Hide the messages, but display the code and its results on the webpage.

Done

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.