We load some data to play with

library(MASS)
data(birthwt)
head(birthwt)
##    low age lwt race smoke ptl ht ui ftv  bwt
## 85   0  19 182    2     0   0  0  1   0 2523
## 86   0  33 155    3     0   0  0  0   3 2551
## 87   0  20 105    1     1   0  0  0   1 2557
## 88   0  21 108    1     1   0  0  1   2 2594
## 89   0  18 107    1     1   0  0  1   0 2600
## 91   0  21 124    3     0   0  0  0   0 2622

A Linear Model

lm1 <- lm(bwt ~ lwt, data = birthwt)
summary(lm1)
## 
## Call:
## lm(formula = bwt ~ lwt, data = birthwt)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2192.12  -497.97    -3.84   508.32  2075.60 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2369.624    228.493  10.371   <2e-16 ***
## lwt            4.429      1.713   2.585   0.0105 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 718.4 on 187 degrees of freedom
## Multiple R-squared:  0.0345, Adjusted R-squared:  0.02933 
## F-statistic: 6.681 on 1 and 187 DF,  p-value: 0.0105

A second one

lm2 <- lm(bwt ~ lwt + age + smoke + ui, data = birthwt)
summary(lm2)
## 
## Call:
## lm(formula = bwt ~ lwt + age + smoke + ui, data = birthwt)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1974.55  -440.18    23.59   499.46  1773.25 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2578.216    297.603   8.663 2.31e-15 ***
## lwt            3.169      1.684   1.882 0.061380 .  
## age            5.528      9.631   0.574 0.566698    
## smoke       -249.200    102.711  -2.426 0.016222 *  
## ui          -512.250    142.731  -3.589 0.000426 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 687 on 184 degrees of freedom
## Multiple R-squared:  0.1312, Adjusted R-squared:  0.1123 
## F-statistic: 6.947 on 4 and 184 DF,  p-value: 3.139e-05

A beautiful Table

library(texreg)
htmlreg(list(lm1, lm2), ci.force = T, caption = "Our results")
Our results
Model 1 Model 2
(Intercept) 2369.62* 2578.22*
[1921.79; 2817.46] [1994.92; 3161.51]
lwt 4.43* 3.17
[1.07; 7.79] [-0.13; 6.47]
age 5.53
[-13.35; 24.40]
smoke -249.20*
[-450.51; -47.89]
ui -512.25*
[-792.00; -232.50]
R2 0.03 0.13
Adj. R2 0.03 0.11
Num. obs. 189 189
RMSE 718.44 687.04
* 0 outside the confidence interval

Another one

library(stargazer)
stargazer(list(lm1,lm2), type = "html", title = "",  ci = T, digits = 1)
Dependent variable:
bwt
(1) (2)
lwt 4.4** 3.2*
(1.1, 7.8) (-0.1, 6.5)
age 5.5
(-13.3, 24.4)
smoke -249.2**
(-450.5, -47.9)
ui -512.3***
(-792.0, -232.5)
Constant 2,369.6*** 2,578.2***
(1,921.8, 2,817.5) (1,994.9, 3,161.5)
Observations 189 189
R2 0.03 0.1
Adjusted R2 0.03 0.1
Residual Std. Error 718.4 (df = 187) 687.0 (df = 184)
F Statistic 6.7** (df = 1; 187) 6.9*** (df = 4; 184)
Note: p<0.1; p<0.05; p<0.01