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\[ Distance = a + b \times Speed \]
\[ \beta_0, \beta_1, \Delta \]
plot(cars)m1 <- lm(data = cars, dist ~ speed)
summary(m1)##
## Call:
## lm(formula = dist ~ speed, data = cars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.069 -9.525 -2.272 9.215 43.201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.5791 6.7584 -2.601 0.0123 *
## speed 3.9324 0.4155 9.464 1.49e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.38 on 48 degrees of freedom
## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438
## F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12install.packages("stargazer")library(stargazer)##
## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.2. https://CRAN.R-project.org/package=stargazerstargazer(m1, type = "html")| Dependent variable: | |
| dist | |
| speed | 3.932*** |
| (0.416) | |
| Constant | -17.579** |
| (6.758) | |
| Observations | 50 |
| R2 | 0.651 |
| Adjusted R2 | 0.644 |
| Residual Std. Error | 15.380 (df = 48) |
| F Statistic | 89.567*** (df = 1; 48) |
| Note: | p<0.1; p<0.05; p<0.01 |
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