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.

library(tidyverse)
options(scipen = 999)

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

# View summary of model 1
summary(houses_lm)
## 
## Call:
## lm(formula = year ~ gdpPercap, data = gapminder)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -67.779 -14.901  -0.191  14.182  30.262 
## 
## Coefficients:
##                  Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept) 1976.62723521    0.50495752 3914.44 <0.0000000000000002 ***
## gdpPercap      0.00039815    0.00004134    9.63 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.82 on 1702 degrees of freedom
## Multiple R-squared:  0.05167,    Adjusted R-squared:  0.05112 
## F-statistic: 92.74 on 1 and 1702 DF,  p-value: < 0.00000000000000022

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

Hint: Your answer must include a discussion on the p-value.

The gdp per capital is statistically significant because the probablity is less than 5%.

Q3 Interpret the coefficient of gdpPercap.

Hint: Discuss both its sign and magnitude.

The gdapPercap has an expected life of 29 years

Q4 Interpret the Intercept.

Hint: Provide a technical interpretation.

The intercept is 53.95 whch would be the average life expectancy

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.

library(tidyverse)
options(scipen = 999)

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

# View summary of model 1
summary(houses_lm)
## 
## Call:
## lm(formula = lifeExp ~ gdpPercap + year, data = gapminder)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -67.262  -6.954   1.219   7.759  19.553 
## 
## Coefficients:
##                  Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept) -418.42425945   27.61713769  -15.15 <0.0000000000000002 ***
## gdpPercap      0.00066973    0.00002447   27.37 <0.0000000000000002 ***
## year           0.23898275    0.01397107   17.11 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.694 on 1701 degrees of freedom
## Multiple R-squared:  0.4375, Adjusted R-squared:  0.4368 
## F-statistic: 661.4 on 2 and 1701 DF,  p-value: < 0.00000000000000022

Q6 Which of the two models is better?

Hint: Discuss in terms of both residual standard error and reported adjusted R squared.

I would say the 2nd one is more accurate because the first one missed 10.49 data in point years compared to the second one which is 9.694 and this is for the amount of years missed during the model.

Q7 Interpret the coefficient of year.

Hint: Discuss both its sign and magnitude.

When you look at the model it shows the life expectancy in years 1952-2007 in small incriments of 5 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.

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

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

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

Q10 Use the correct slug.