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(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%?

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

the coefficient is significant because it is less than 5%.

Q3 Interpret the coefficient of gdpPercap.

Hint: Discuss both its sign and magnitude.

the gdp was up 49 per every capita which means its a positive sign with a magnitude that is higher than what is predicted.

Q4 Interpret the Intercept.

Hint: Provide a technical interpretation.

the -19 thousands means that their life expectance is lower than the average.

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.

Of the two models the second one is better. it is better because the standard error is lower while the adjusted R squared is higher. the second model has the better results.

Q7 Interpret the coefficient of year.

Hint: Discuss both its sign and magnitude.

the coefficient of year is 17 years so the sign is positive and its magnitude is equal to statistical significantsof 5%.

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.

The predicted life expectancy is about 76 years of life.

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.