General info abt course
Didactic team:
Lectures:
Ginette Lafit (ginette.lafit@kuleuven.be)
Practical sessions:
For a refresher see MOOC recommendations on Ultra.
Lectures:
Web lectures (available for 2 weeks)
Q&A
Mock Exam
Any question regarding practicals can be e-mailed to dominika.wisniewska@kuleuven.be
Practical session - 25-30 students per group, 8 times throughout the semester. (Short) revision of theory & working on exercises. Material and exercises are on cudi PPW, Formulary and stat tables in Ultra. Calculator should be used from early on (and will be used in exam).
2 Q&A sessions, one halfway through and one at the end. Practical info given via Ultra.
Ultra discussion forum has some things to read prior (basically forum rules).
Mock exam on Ultra in mid-December.
If you come to practical sessions, you have to come prepared (material published on Ultra).
Mostly about understanding, rather
Intro
Study 1
Do professors think the level of students has dropped?
Ask 50 professors.
26 say that the level has dropped.
52% of the professors in the sample think the level has dropped.
26 / 50 * 100 = 52%
Study 2
Continuous variable - float variable - a third value can be found between any other two values. (real numbers)
Discrete variable - integers (natural numbers)
Continuity is a theoretical assumption.
Considered continuous when:
- the variable takes on a wide range of values
- the variable is a manifestation of an underlying continuous variable
Variables are considered discrete if they only assume a limited number of values
If the answer set of a discrete variable is of size
2 → dichotomous variable
3 → trichotomous variable
3+ → polytomous variable
Qualitative variable - something that is a label or name (enum basically). You can check if qualitative variables are equal, but you can’t, for example, subtract one from the other.
however - Ordinal variables - the values may reflect some intrinsic order or size, but are not meaningful to calculate with
Quantitative variable - something you can perform calculations with (real number)
Total frequency - f(X), where X is the value you want to know the frequency of. Returns how many samples have a value of X.
Relative frequency - p(X), where X is the value you want to know the relative frequency of.
p(X = 77) = 3 / 30 = 0.1, where 3 is the number of values of 77 in the dataset, and 30 being the total amount of samples.
Cumulative frequency - F(X) where X is the value you want to know the cum. frequency of. Function returns the total number of scores lower than or equal to that specific score.
Relative cumulative frequency - P(X) where X is the value you want to know the relative cum. frequency of. Returns the cumulative frequency divided by the total number of samples.