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.
At 5%, it shows that gdpPercap is statistically singnificant. We can tell this because the P value is less than 5%. That means that we are 95% confident gdpPercap is significant to the data set.
Hint: Discuss both its sign and magnitude.
The coefficient of gdpPercap tells us how much the coefficient changes. At 0.00076488 we can tell that its positive so it is positivly effected.
Hint: Provide a technical interpretation.
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 second model is better because the first model has an amout of years missed in the model at 10.49, and the second model is lower at 9.64. The second model is also better in regards to R squared because it shows .4368 in regaurds to .3403 of life expetancy.
Hint: Discuss both its sign and magnitude.
The life expectancy increases by 0.23898275 years. This is correlated with 0.01397107 increase in gdpPercap. the data is every five years, from 1952 to 2007.
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.