Lecture notes, seminar notes (about Rstudio) and 1 chapter of the book all in one summary for the exam of Statistics I. I'm doing a major in Linguistics, but this course also had students from other majors (Communicatie en Informatiewetenschappen, Informatiekunde).
Summary Statistics I
Lecture 01 – Week 1
We use statistics to assess relationships in the data, this is called inferential statistics. We also use
statistics to summarize the data en then we talk about descriptive statistics. The requirement of the
data is that it is variable (in other words, there must be variation).
Descriptive statistics
Descriptive statistics describe data without conclusions. Describing the data can be done with
measures of central tendency and measures of spread.
Measures of central tendency:
Mean, the mean is the sum of al the values divided by the amount of values.
Median, the middle value if you would order all the values from low to high. With an even amount of
values, you take the two middle values, add them and divide then by 2.
Mode, the value with the highest frequency (for nominal data: only meaningful measure)
Measures of spread
Variance, the average squared deviation from the mean
Standard deviation, the square root of variance
Inferential statistics
Inferential statistics links findings based on sample to population. To do this, we compare 2 groups or
1 group with a value.
Statistics in R
The main purpose of R is to conduct statistical analysis with many different functions to obtain
descriptive and inferential statistics.
Book – Week 1
Chapter 2
Citation
If you use someone’s add-on package for you research, follow the example below:
> citation
To cite the R package ‘cluster’ in publications use:
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K.(2015). cluster: Cluster Analysis Basics
and Extensions. R package version 2.0.1.
R syntax
The > sign indicates the prompt line where you can type your code and pressing Enter will get them
executed. The number in square brackets [1] shows the index number of the element in the output.
This information can be useful when you have a long string of elements that does not fit in one line.
It is also possible to type several commands on one line. The commands should be separated by a
semicolon.
If you stored values in objects, you can remove all objects with the following function: rm(list = ls()).
To identify your current working directory, type in: getwd().
If you want help, but do not remember or know the name of the specific function, there is also a
function to help you. Suppose you want to learn how to measure correlation between 2 variables. In
that case, you can use the following code: help.search(“correlation”), or ??correlation. This will return
a list of all function available in your documentation, which contain the expression.
, Main types of R objects
Uni-, bi- and multivariate data can be represented in R by different objects. Single variables are most
commonly represented by vectors and factors. Vectors can be of two main types: numeric (sequence
of numbers) and character (sequences of character strings). Nominal variables are best represented
by factors.
The function cbind() combines two or more vectors or factors as columns. Its counterpart is rbind(),
which combines different rows.
Lab 01 – Week 1
dat <- read.csv(‘data.csv’, sep = ‘;’ , dec=’.’)
Import data from csv file
dat$participant <- as.factor(dat$participant)
By using this code, you can change data into categorical data
mean(dat[dat$gender == ‘M”, ]$english_grade)
Gives you the mean of English grades of men
table(dat$gender, dat$study)
Gives you a table of study people do, but divided by their gender
gendertable <- table(dat$gender)
show(gendertable)
Store the data of gender in a table and show that table
barplot(gendertable, main = “Distribution of Gender”, col=c (“pink”, “lightblue”), ylim=c(0,35), xlab =
“Gender”, ylab = “Frequency”)
This function gives you a barplot with the amount of people from that gender. Females are coloured
pink en males are coloured lightblue. The title of the barplot is “Distribution of Gender”, the y-axis is
from 0 to 35 and the title of the y-axis is Frequency and the title of the x-axis is Gender.
Lecture 02 Week 2
Why study a sample?
Studying the whole population is (almost always) practically impossible. A sample is a (selected)
subset of the population and thus more accessible. But it is very important that the sample is
representative of the population.
Variables and values and types of variables
Statistics always involves variables. The values of the variables indicate properties of the case.
We distinguish different types of variables:
Nominal (categorical) scale:
A nominal variable is a categorical variable that does
not have any intrinsic ordering or ranking. This value
cannot be quantitative.
Ordinal scale:
Ordinal variables are ranked, there is a clear ordering
of the categories. However, the distances between
the categories are unclear.
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