Quiz 4

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.

## 
## 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 gdpPercap is significant at 5% whihc you can tell by looking at the p-value which 0.0000000000000002 so making it less than 5 %

Q3 Interpret the coefficient of gdpPercap.

Hint: Discuss both its sign and magnitude.

The gdpPercap has an estimate from the graph which is 0.00076488

Q4 Interpret the Intercept.

Hint: Provide a technical interpretation.

The information from the grpah show that the life expectancy is estimated at 53.95556088 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.

## 
## 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.

From looking at the information the second model seems to be better. The first model miseses the residual errorand has the amound of years missed are 10.49 years whle the second model has 9.694 years.

Q7 Interpret the coefficient of year.

Hint: Discuss both its sign and magnitude.

The life expectancy increases by 0.23898275 years and the gdp per cap increases at 0.01397107. The data is related from every 5 years per unit in spacing that goes from 1952 to 2007

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.