stargazer, ols regression

[alua]

hi, i'm just starting to study r studio, could any of you help in interpreting results after function stargazer.What do asterisks and f test mean?

[technocrat]

Hi, and welcome!

For future reference, see FAQ: What's a reproducible example (`reprex`) and how do I create one? It's often essential to getting good answers.

Let's use one from the help(lm) page

ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D90 <- lm(weight ~ group - 1) # omitting intercept
summary(lm.D90)
#> 
#> Call:
#> lm(formula = weight ~ group - 1)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -1.0710 -0.4938  0.0685  0.2462  1.3690 
#> 
#> Coefficients:
#>          Estimate Std. Error t value Pr(>|t|)    
#> groupCtl   5.0320     0.2202   22.85 9.55e-15 ***
#> groupTrt   4.6610     0.2202   21.16 3.62e-14 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.6964 on 18 degrees of freedom
#> Multiple R-squared:  0.9818, Adjusted R-squared:  0.9798 
#> F-statistic: 485.1 on 2 and 18 DF,  p-value: < 2.2e-16

^{Created on 2020-01-26 by the reprex package (v0.3.0)}

Before running a regression, you should decide on \alpha, of the level of statistical significance. \alpha = 0.05 is conventional, and it means that the result has a 1 in 20 chance of being due to random variation.

The first thing to look at is the overall model results is the F statistic, it's p-value should be less than \alpha. In this case it is. If it isn't, the model

The stars are the p-values of the coefficients

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

But that's not the end of it. There are also diagnostic plots

For a longer discussion see my short introduction to OLS