Greek alphabets for equation typing.
\[ gpa_i = \beta_0 + \beta_1 hours\_studied_i + \epsilon_i \]
remove(list=ls())
hours_studied <- c(2.5 , 4 , 2 ,0 ,3 ,2.5 , 0.5 , 6 , 0 , 2 , 1, 2 )
gpa <- c(3.7 , 3.9, 3.8, 4,3.7,3.6 , 1.2 , 4 , 0 , 4 , 2, 3.7)
cov(x = hours_studied, y = gpa)[1] 1.331818cor(x = hours_studied, y = gpa)[1] 0.5842855library(ggplot2)
ggplot2::ggplot(mapping = aes(x = hours_studied, y = gpa)) + geom_point() +geom_abline()
reg1 <-
lm(formula = gpa ~ hours_studied)
round(sum(reg1$residuals), 15)[1] 0sum(reg1$residuals^2)[1] 12.71557sum(reg1$residuals)^2 # 1.232595e-32[1] 1.232595e-32ggplot2::ggplot(mapping = aes(x = hours_studied, y = reg1$fitted.values)) + geom_point()+geom_line()
summary(reg1)
Call:
lm(formula = gpa ~ hours_studied)
Residuals:
Min 1Q Median 3Q Max
-2.1773 -0.6896 0.2355 0.6479 1.8227
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1773 0.5313 4.098 0.00215 **
hours_studied 0.4499 0.1976 2.277 0.04604 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.128 on 10 degrees of freedom
Multiple R-squared: 0.3414, Adjusted R-squared: 0.2755
F-statistic: 5.183 on 1 and 10 DF, p-value: 0.04604var(x = hours_studied)[1] 2.960227cov(y = gpa, x = hours_studied)[1] 1.331818beta1 <- cov(y = gpa, x = hours_studied) / var(x = hours_studied)
beta1[1] 0.449904\[ \bar y = \beta_0 + \beta_1 \bar x \]
x_bar <- mean(x = hours_studied)
y_bar <- mean(x = gpa)
beta0 <- y_bar - beta1 * x_bar
beta0[1] 2.177287
length(hours_studied)[1] 12time_wasted_class <- c(2,10,5,8,3,1,6,1,10,9,.3,4)
time_wasted_AS <- c(3, 5, 8,2, 4,6,
4, 5, 7, 1, 2,4)
time_wasted_R <- rnorm(n = 12,
mean = 3,
sd = 1
)
reg2 <-
lm(formula = gpa ~ hours_studied + time_wasted_class)
reg3 <-
lm(formula = gpa ~ hours_studied + time_wasted_AS)
reg4 <-
lm(formula = gpa ~ hours_studied + time_wasted_R)
library(stargazer)
Please cite as: Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.3. https://CRAN.R-project.org/package=stargazer stargazer(reg1, reg2, reg3,reg4, type = "text")
=================================================================================================
Dependent variable:
-----------------------------------------------------------------------------
gpa
(1) (2) (3) (4)
-------------------------------------------------------------------------------------------------
hours_studied 0.450** 0.449* 0.488** 0.416**
(0.198) (0.223) (0.194) (0.181)
time_wasted_class -0.002
(0.106)
time_wasted_AS -0.203
(0.159)
time_wasted_R -0.481
(0.275)
Constant 2.177*** 2.188** 2.958*** 3.279***
(0.531) (0.889) (0.802) (0.795)
-------------------------------------------------------------------------------------------------
Observations 12 12 12 12
R2 0.341 0.341 0.442 0.508
Adjusted R2 0.276 0.195 0.318 0.399
Residual Std. Error 1.128 (df = 10) 1.189 (df = 9) 1.094 (df = 9) 1.027 (df = 9)
F Statistic 5.183** (df = 1; 10) 2.333 (df = 2; 9) 3.562* (df = 2; 9) 4.646** (df = 2; 9)
=================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01ln_gpa <- log(gpa+.00000001)
sqrt_gpa <- gpa^.5
reg5 <-
lm(formula = ln_gpa ~ hours_studied)
reg6 <-
lm(formula = sqrt_gpa ~ hours_studied)
library(stargazer)
stargazer(reg1, reg5,reg6, type = "text")
===========================================================
Dependent variable:
-----------------------------
gpa ln_gpa sqrt_gpa
(1) (2) (3)
-----------------------------------------------------------
hours_studied 0.450** 1.371 0.191*
(0.198) (0.947) (0.092)
Constant 2.177*** -3.371 1.270***
(0.531) (2.547) (0.247)
-----------------------------------------------------------
Observations 12 12 12
R2 0.341 0.173 0.302
Adjusted R2 0.276 0.091 0.232
Residual Std. Error (df = 10) 1.128 5.406 0.524
F Statistic (df = 1; 10) 5.183** 2.096 4.326*
===========================================================
Note: *p<0.1; **p<0.05; ***p<0.01plot(reg1)
