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Welcome to this lecture
on statistical analysis,
providing an overview of
an introduction to the
foundational concepts of
statistical analysis
beginning with
data types and their influence
on analysis methods.
We will explore key
summary statistics,
measures of central tendency
and variability and
the principles of probability
that support
inferential statistics.
The lecture will also cover
hypothesis testing,
confidence intervals,
and methods for
analyzing relationships
between variables such as
correlation and regression.
Equipped with these tools,
you will be prepared
to interpret data,
make informed decisions,
and draw sound
research conclusions.
Statistical analysis
is a cornerstone
of modern research
and decision making.
Every analysis begins by
understanding your data.
At the base level we distinguish
between categorical
and numerical data.
Categorical data like
gender or region
signal classifications
without inherent order.
Numerical data
describe counts or
measurements such as
height or test scores.
Recognizing these types informs
your choices in graphs,
statistical techniques,
and interpretation
ensuring valid conclusions
and proper application
of methods.
One of the first
steps in summarizing
a dataset is to
explore measures of
central tendency and variability
which provide a concise view
of the data's distribution.
Central tendency describes
the typical value,
the mean or average,
the median, the middle
point when ordered,
and the mode, the
most common value.
