Make sure to include the unit of the values whenever appropriate.
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
Hint: Your answer must include a discussion on the p-value.
gpd per capital is statistically significant because the probability is less than 5%.
p-value: < 0.00000000000000022
Hint: Discuss both its sign and magnitude.
GPD Per capital has a life expectancy 0.00076488.
Hint: Provide a technical interpretation.
The Intercept is 53.95556088 and it stands for the average age in years
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
Hint: Discuss in terms of both residual standard error and reported adjusted R squared.
The residule standard error on the first model is 10.49 compared to the second model which is 9.694 and this stands for the amount of years missed throughout the model making the second model more accurate. In addition the model only represents 43.68 % of life expetency in years while the first model only represents 34.03% of the life expectency.
Hint: Discuss both its sign and magnitude.
The life expectency increases by 0.23898275 years, and increases by 0.01397107 per capita and ranges from 1952-2007 in increments of 5 years.
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.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.