I'm analyzing some data about computer proficiency (dependent variable, value between 100-400) having the following independent variables from a questionnaire:

- country

- age

- gender

- likes learning new things (score 1-5)

- parent education level (1-3)

Am wondering if I can just put this into a multiple linear regression model as per the example in R below:

Code:

`lm(computerProficiency ~ AGE + GENDER + PARENTS_EDUCATION + COUNTRY + ENJOY_LEARNING, data = dataSet)`

Code:

```
Residuals:
Min 1Q Median 3Q Max
-207.37 -24.30 1.21 25.25 177.07
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 246.7276 1.7096 144.32 < 2e-16 ***
AGE -0.5577 0.0182 -30.70 < 2e-16 ***
GENDER_Male 4.8323 0.4301 11.24 < 2e-16 ***
PARENTS_ED 18.4795 0.3721 49.67 < 2e-16 ***
ENJ_LEARNING 7.3347 0.2734 26.83 < 2e-16 ***
CNTRY_UK -9.2826 0.7207 -12.88 < 2e-16 ***
CNTRY_JPN 3.9170 0.8987 4.36 1.3e-05 ***
CNTRY_POL -56.4754 0.8575 -65.86 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 37 on 30115 degrees of freedom
(15909 observations deleted due to missingness)
Multiple R-squared: 0.308, Adjusted R-squared: 0.308
F-statistic: 1.34e+03 on 10 and 30115 DF, p-value: <2e-16
```