We need Ti 30XIIS, 30XIIB, 30XS MULTIVIEW, 30XB MultiView
Introdcution to the practice of statistics, Moore.
Keep track of ”Ultra”
total of 8 practical sessions (These are 2h in smaller groups)
Revision of the theory at the end
2 Q&A sessions
Try the intro-exercises (at Cudi PPW)
Formulary and stat tables in Ultra.
Use calculator a lot, you use it in exam.
Concept teach structure
Always an intro class,
then the basics
and then into advanced
(Everything in Lutra)
Discussion forum will be good, find at Ultra.
Questions are sorted by topic, make sure to post it in the right space
Exam will be Wednesday 15:th January, written, closed book exam with both open questions and multiple choice
You can bring calculatorm forumlary and stats tables.
Online mock exam to do at home
Feedback some time in February
Should spend about 134 hours studying statistics if I want to succeed. (We’ll see about that lål)
Distribution of Data (1 Variable)
Variables are generally represented by capital letters in Italics
”Random variables are realisations of a random process”
eg. Xij, ie represents the element and the variable together, ie. Picks out a place in a table
- Independent variable and dependent variable.
input/Independent/explanatory/predictors: stands on its own feet. Symbolised using X
dependent/output/response/criteria: are a response to some other factor. Symbolised using Y
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Continous and discrete variables
In continous you can always find infinite numbers between the two numbers
In discrete values, this is not the case, eg. natural numbers
Continuity is a theoretical assumption when
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The variable takes on a wide range of values
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The variable is a manifestation of an underlying continous variable.
Discontinuity is a theoretical assumption when
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There is only a limited number of values
Discreet variables that
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Only assume two values → dichotomous varaible
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Only assumes three values → trichotomous variables
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Assumes three or more values → polytomous variables
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Qualitative and Quantitative variables
Qualitative only to refers to equalities and inequalities between research elements
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Number is a name or label, calculating is not meaningful
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Eg. what language a set of of elements speak
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Levels of variables is common, like assigning a number to a country
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Can also be ordinal variables, ie. They can be compared by size, but still not calculable, like language knowledge, eg. c1 English proficieny.
Quantitative assigned such that differences betweeen numbers correspond with distances between research elements
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Number is a value and calculating it is meaningful
”hierarchy”: qualitative – ordinal – quantiative
f(x)freq p(x) rel freq F(x)cum freq P(x) cum rel freq
Key Statistics
Percentile:
is a score on X under which at least a specfifc % of scores are situated
Notation eg. ”P10”
Percentage occurs in rel cum freq table
percentage does not occur on this table.