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)
library(scales)
options(scipen = 999)Hint: Your answer must include a discussion on the p-value. Yes, the coefficient of education is statistically significant because it is less than 5%. ## Q3 Interpret the coefficient of gdpPercap. Hint: Discuss both its sign and magnitude. Every year spent on education the hourly wage will increase $0.94. ## Q4 Interpret the Intercept. Hint: Provide a technical interpretation. The wages for sexM, which is males, is statistically significant because the coefficient is less than 5%. It shows that males are making $2.33 more an hour. ## 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. The hourly wage for a woman with 15 years and 5 years of experience would be $8.16. You add the intercept (-6.5) and then multiply education (0.94) by 15 and then experience (0.11) by 5 and add those together. ## Q6 Which of the two models is better? Hint: Discuss in terms of both residual standard error and reported adjusted R squared. The intercept at 0 would be statistically impossible. People would be making negative money. ## Q7 Interpret the coefficient of year. Hint: Discuss both its sign and magnitude. The adjusted R squared went from 2.489 to 2.592 after adding union as another predictor. This means that whether or not the person is a union member is has very little statistical significance to the model. Adding the union predictor didn’t impact the residual standard error very much, it only went down a little bit meaning it didn’t help with how much data they had. ## 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.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.