\[ y = \beta_0 + \beta_1 \mbox{gender} + \beta_2 \mbox{age} \]

##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)    4.484      0.125  35.792    0.000
## gendermale     0.191      0.052   3.632    0.000
## age           -0.009      0.003  -3.280    0.001

\[ y = \beta_0 + \beta_1 \mbox{gender} + \beta_2 \mbox{age} + \beta_3 \mbox{gender}\times\mbox{age} \]

##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)       4.883      0.205  23.795    0.000
## gendermale       -0.446      0.265  -1.681    0.094
## age              -0.018      0.004  -3.919    0.000
## gendermale:age    0.014      0.006   2.446    0.015

Question: The original paper describes how these data were gathered by taking a sample of professors from the University of Texas at Austin and including all courses that they have taught. Considering that each row represents a course, could this new information have an impact on any of the conditions of linear regression?

Question: Based on your final model, describe the characteristics of a professor and course at University of Texas at Austin that would be associated with a high evaluation score.

##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)              3.772      0.232  16.255    0.000
## ethnicitynot minority    0.168      0.075   2.230    0.026
## gendermale               0.207      0.050   4.131    0.000
## languagenon-english     -0.206      0.104  -1.989    0.047
## age                     -0.006      0.003  -2.315    0.021
## cls_perc_eval            0.005      0.001   3.244    0.001
## cls_creditsone credit    0.505      0.104   4.853    0.000
## bty_avg                  0.051      0.017   3.016    0.003
## pic_colorcolor          -0.191      0.067  -2.830    0.005

Question: Based on your final model, describe the characteristics of a professor and course at University of Texas at Austin that would be associated with a high evaluation score.

Answer:

• non-minority
• male
• english-speaking
• younger
• with a high proportion of students responding
• teaching a one-credit class
• with a high beauty rating
• a non-black/white picture.

Question: Would you be comfortable generalizing your conclusions to apply to professors generally (at any university)? Why or why not?

Question: Would you be comfortable generalizing your conclusions to apply to professors generally (at any university)? Why or why not?

Answer: No, the student body may be very different at the University of Texas, Austin as opposed to, say, Reed College.