Question
========
Consider the following regression results:


```

Call:
lm(formula = y ~ x, data = d)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.14867 -0.82868 -0.07472  0.66596  2.54119 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0001676  0.1254992   0.001    0.999
x           1.2492437  0.1241613  10.061 2.04e-14

Residual standard error: 0.9786 on 59 degrees of freedom
Multiple R-squared:  0.6318,	Adjusted R-squared:  0.6255 
F-statistic: 101.2 on 1 and 59 DF,  p-value: 2.043e-14
```

Describe how the response `y` depends on the regressor `x`.


Solution
========
The presented results describe a linear regression.

The mean of the response `y` increases with increasing `x`.

If `x` increases by 1 unit then a change of `y` by about 1.25 units can be expected.

Also, the effect of `x` is  significant at the 5 percent level.


Meta-information
================
extype: string
exsolution: nil
exname: regression essay
exstringtype: essay|file
exextra[essay,logical]: TRUE
exextra[essay_format,character]: editor
exextra[essay_required,logical]: FALSE
exextra[essay_fieldlines,numeric]: 5
exextra[essay_attachments,numeric]: 1
exextra[essay_attachmentsrequired,logical]: TRUE
exmaxchars: 1000, 10, 50