Pima Community College R Programming 101 Questions

Q1:The linear regression results below display the effect of six indices that might affect happiness across different regions in the world: economic production, social support, life expectancy, freedom, absence of corruption, and generosity. The data question is:

What affects happiness?

Based on the output below, how can we answer the question above?

## ## Call:## lm(formula = ladder_score ~ logged_gdp_per_capita + social_support + ## healthy_life_expectancy + freedom_to_make_life_choices + ## generosity + perceptions_of_corruption, data = .)## ## Residuals:## Min 1Q Median 3Q Max ## -1.85049 -0.30026 0.05735 0.33368 1.04878 ## ## Coefficients:## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -2.23722 0.63049 -3.548 0.000526 ***## logged_gdp_per_capita 0.27953 0.08684 3.219 0.001595 ** ## social_support 2.47621 0.66822 3.706 0.000301 ***## healthy_life_expectancy 0.03031 0.01333 2.274 0.024494 * ## freedom_to_make_life_choices 2.01046 0.49480 4.063 7.98e-05 ***## generosity 0.36438 0.32121 1.134 0.258541 ## perceptions_of_corruption -0.60509 0.29051 -2.083 0.039058 * ## —## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1## ## Residual standard error: 0.5417 on 142 degrees of freedom## Multiple R-squared: 0.7558, Adjusted R-squared: 0.7455 ## F-statistic: 73.27 on 6 and 142 DF, p-value: < 2.2e-16 question2 see attached file Match the scatterplot of a numeric variable on happiness (which is represented by ladder score on the y axis) with the linear regression results for that numeric variable. ladder_score 1. Social Support estimate/slope: 2.47621 0.25 0.50 0.75 2. Perceptions of Corruption estimate/slope: -0.60509