[MUSIC]
In this video I will introduce the Teachings Ratings Data.
We have been working with teaching ratings data from University of Texas.
And the underlying question is, if students teaching evaluations
are influenced by the looks of individual instructors.
Or you can ask if they're teaching evaluations differ by gender,
or if good looking instructors get higher teaching evaluations.
I obtained this data from Professor Daniel Hamermesh, who has written
a paper about how beauty may impact in an instructors teaching evaluation.
In fact, he has written an amazing book called Beauty Pays,
in which he answers these questions.
Such as do you think good looking employees get higher pay or
faster promotions?
Do you think good looking instructors get higher teaching evaluations and
the data comes from University of Texas?
It's a survey.
And data obtained from 463 courses.
So the data is first referenced in the book,
Getting Started with Data Science, making sense of data with Analytics in Chapter 4.
They're variables that essentially define the attributes of instructors and
characteristics of the courses.
Some variables are continuous, others that dichotomous or categorical variables.
So the primary two variables of our interests
are beauty score, which is basically the physical appearance
of an instructor which was ranked by a panel of six students.
And I think that they have normalized the beauty
score such that it had the mean of zero and variance of one or
a standard deviation of one the same as Z transformation.
The dependent variable, the variable of interest is evaluation,
which is basically the teaching evaluation ranging between the scale of 1 to 5,
one being very unsatisfactory and
the student found the course to be excellent, then it's 5.
And then there are other dichotomous or binary or
categorical variables such as minority.
If the instructor was non Caucasian, age is a continuous variable
as the professors age or instructors age, gender being male or female.
Native stands for native English speaker.
If the instructor with a native speaker of English language10, otherwise.
If the professor was tenured, 10 otherwise.
I have produced some descriptors statistics for your reference.
For instance for the continuous variables such as age and beauty and
teaching evaluation and the number of students who are enrolled in the course
on the number of students who performed the teaching evaluations.
I produce the descriptive statistics such as the minimum, the maximum mean,
and standard deviation.
For categorical variables such as gender, female yes or no,
visible minority yes or no person being a tenured professor or otherwise.
I produced the frequency distributions and
percentage of individuals falling in one category or otherwise.
Notice that the teaching evaluation score is 3.99, with the standard deviation of
0.55 and let's see if I were to produce a histogram
of this variable teaching evaluation, how will it look like with raw data?
And if I were to use the normal distribution and feed the two parameters,
that is the mean and the standard deviation, how will the same
distribution look like using a normal distribution?
I am presenting here the distribution of the raw data on left side and
the presentation of the same data with the same parameters of the mean and
standard deviation using normal distribution.
You could see that the data not exactly following a Bell curve.
This is the raw data and it seldom does.
But then the theoretical distribution looks like this.
Essentially the same data set with a mean of 3.998 in standard deviation
is presented here and then a normal distribution drawn from these two
characters appear step, so it's much smoother.
Theoretical distributions are much smoother than the raw data.
[MUSIC]