knitr::opts_chunk$set(echo = TRUE)

Introduction

Add text here

Initially Proposed Model

Total expenditures: Add more text here

mod_totexp <- lm(totexp/1e6 ~ initcirc + law + phdfld + msda + fac + irpgg, 
                 data = arl_totexp)
summary(mod_totexp)
## 
## Call:
## lm(formula = totexp/1e+06 ~ initcirc + law + phdfld + msda + 
##     fac + irpgg, data = arl_totexp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -28.7537  -4.2685   0.0145   3.6981  25.0966 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -3.662e-01  2.128e+00  -0.172   0.8637    
## initcirc     4.593e-05  7.641e-06   6.011 3.77e-08 ***
## law1         4.869e+00  1.927e+00   2.526   0.0133 *  
## phdfld       8.014e-02  3.496e-02   2.292   0.0242 *  
## msda         1.323e-03  7.249e-04   1.825   0.0713 .  
## fac          2.224e-03  1.047e-03   2.125   0.0363 *  
## irpgg        1.157e-08  1.161e-09   9.973 2.90e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.942 on 91 degrees of freedom
## Multiple R-squared:  0.8179, Adjusted R-squared:  0.8059 
## F-statistic: 68.13 on 6 and 91 DF,  p-value: < 2.2e-16
pred_totexp <- predict(mod_totexp, newdata = uva_totexp)
compare(pred_totexp, uva_totexp$totexp/1e6)
## Predicted: $35.16 
## Actual:    $40.03
# also try msda in place of gradstu

Materials Expenditures: add more text here

mod_explm <- lm(explm/1e6 ~ initcirc + law + phdfld + msda + fac + irpgg, 
                 data = arl_explm)
summary(mod_explm)
## 
## Call:
## lm(formula = explm/1e+06 ~ initcirc + law + phdfld + msda + fac + 
##     irpgg, data = arl_explm)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.7320  -2.1568  -0.4108   1.8806  11.9938 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.687e+00  1.037e+00   1.626 0.107339    
## initcirc    1.338e-05  3.726e-06   3.591 0.000533 ***
## law1        2.401e+00  9.398e-01   2.555 0.012276 *  
## phdfld      4.522e-02  1.705e-02   2.652 0.009429 ** 
## msda        5.627e-04  3.535e-04   1.592 0.114887    
## fac         3.255e-04  5.104e-04   0.638 0.525186    
## irpgg       5.368e-09  5.659e-10   9.485 3.04e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.873 on 91 degrees of freedom
## Multiple R-squared:  0.7618, Adjusted R-squared:  0.7461 
## F-statistic:  48.5 on 6 and 91 DF,  p-value: < 2.2e-16
pred_explm <- predict(mod_explm, newdata = uva_explm)
compare(pred_explm, uva_explm$explm/1e6)
## Predicted: $16.07 
## Actual:    $14.03
# also try msda in place of gradstu