This is an English summary of the Statistics 2 course at the VU Amsterdam (second year). It walks you through the content of the lectures including examples. Please note I did not summarize using the book. Good luck studying!
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, Lecture 1: Recap of Stat 1 ch. 1-4
Measurement levels of variables, descriptive statistics & probability distributions
/
What is a variable? A variable is a characteristic that can vary in value among subjects in a sample
or a population. They each have their own measurement level, which
determines the statistical method that is to be used.
Measurement levels 1. Nominal
2. Ordinal
unordered, only has the quality of identity)
has the qualities of identity and order) } categorical
3. Interval
4. Ratio
identity, order and quantity in equal units)
identity, order, quantity and an absolute zero point) } nfuetarniitative
Parametric methods = suitable for interval and ratio (quantitative) data (quantitative y )
Nonparametric met. = suitable for nominal and ordinal (categorical) data ( categorical y)
Descriptive statistics = used to summarize data with tables and figures
We can - summarize per one variable (distribution)
Data centre: Mean, Mean = the average Median = the middle nr. Mode = the most frequent nr.
median, mode
. . ..
.. . .. .
.÷÷÷÷ .
Data variability:
Range = the difference between the minimum and maximum variable
Deviation = the difference from the mean for each item
(yi -
51 Yi = item score
4- mean y =
Sum of Squares = 1. Squaring the deviation score for each item (to eliminate negative numbers)
2. Summing these all up —> shows the total deviance from the mean
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SD ( s ) = gives the average deviation from the mean (through cancelling out the square)
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5. Take the
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= 5
The empirical rule
Measures of position A boxplot devides the date up in four equal parts called quartiles
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above the 3rd Id
Probability (p) = the chance that an observation takes on a particular value
—> Each possible value of a variable has a specific probability of occurring
—> This is represented in a probability distribution = all possible values
of a variable and their probabilities
Discrete vs cont. Discrete distributions
- Each value has a probability
:- Represented in a histogram
Continuous distributions
- Infinite number or possible values, probability given to intervals
:- Represented in the area under the curve
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