Notes de cours
Statistics for CSAI 1
- Cours
- Établissement
- Book
Lecture notes study book Learning Statistics with R of Navarro, Daniel - ISBN: 9781326189723
[Montrer plus]
Livre connecté
Titre de l’ouvrage:
Auteur(s):
- Édition:
- ISBN:
- Édition:
Plus de résumés pour
-
MATH 225N Week 5 Lab Assignment
Vendeur
S'abonner
melisanver
Avis reçus
Aperçu du contenu
Lecture 1 is an informative session about the course structureChapter 1- Book
Why do we learn statistics?
- we don’t trust humans // Biases
- when people are presented with a strong argument that contradicts our pre-existing
beliefs, we find it pretty hard to even perceive it to be a strong argument
- Simpson’s Paradox
Chapter 2 - Book
A brief introduction to research design
- Data collection can be thought of as a kind of measurement.
- operationalisation is the process by which we take a meaningful but somewhat vague
concept, and turn it into a precise measurement.
- A theoretical construct. This is the thing that you’re trying to take a measurement of,
like “age”, “gender” or an “opinion”. A theoretical construct can’t be directly
observed, and often they’re actually a bit vague.
- A measure. The measure refers to the method or the tool that you use to make your
observations.
- An operationalisation. It refers to the logical connection between the measure and
the theoretical construct, or to the process by which we try to derive a measure from a
theoretical construct.
- A variable. It is what we end up with when we apply our measure to something in the
world. That is, variables are the actual “data” that we end up with in our data sets.
Scales of measurement
- A nominal scale variable (categorical variable) is one in which there is no particular
relationship between the different possibilities: for these kinds of variables it doesn’t
make any sense to say that one of them is “bigger’ or “better” than any other one, and
it absolutely doesn’t make any sense
- An ordinal scale variable is one in which there is a natural, meaningful way to order
the different possibilities, but you can’t do anything else.e to average them.
- In the case of interval scale variables, the differences between the numbers are
interpretable, but the variable doesn’t have a “natural” zero value.
- ratio scale you can do real calculations with it
- A continuous variable is one in which, for any two values that you can think of, it’s
always logically possible to have another value in between.
- A discrete variable is, in effect, a variable that isn’t continuous. For a discrete
variable, it’s sometimes the case that there’s nothing in the middle.
, - Likert Scale- we can’t say an exact thing about it-- quasi-interval scale.
- reliability (repeatability or consistency of your measurement) of a measure tells you
how precisely you are measuring something, whereas the validity (correctness of a
measurement) of a measure tells you how accurate the measure is
- Test-retest reliability. This relates to consistency over time: if we repeat the
measurement at a later date, do we get the same answer?
- Inter-rater reliability. This relates to consistency across people: if someone
else repeats the measurement will they produce the same answer?
- Parallel forms reliability. This relates to consistency across
theoretically-equivalent measurements: if I use a different set of bathroom
scales to measure my weight, does it give the same answer?
- Internal consistency reliability. If a measurement is constructed from lots of
different parts that perform similar functions (e.g., a personality questionnaire
result is added up across several questions) do the individual parts tend to give
similar answers.
- Internal validity: extent to which you are able draw the correct conclusions
about the causal relationships between variables.
- External validity: generalisability of the study
- Construct validity: a question of whether you’re measuring what you want to
be mea- suring. A measurement has good construct validity if it is actually
measuring the correct theoretical construct,
- Face validity: whether or not a measure “looks like” it’s doing what it’s
supposed to.
- Ecological validity: for EV the entire set up of the study should closely
approximate the real world scenario that is being investigated
Threats to validity
- Confound: A confound is an additional, often unmeasured variable that turns out to be
related to both the predictors and the outcomes. The existence of confounds threatens
the internal validity of the study because you can’t tell whether the predictor causes
the outcome, or if the confounding variable causes it, etc.
- Artifact: A result is said to be “artifactual” if it only holds in the special situation that
you happened to test in your study. The possibility that your result is an artifact
describes a threat to your external validity, because it raises the possibility that you
can’t generalise your results to the actual population that you care about.
, - History effects---specific events may occur during the study itself that might influence
the outcomes.
- repeated testing effects
- Maturation effects-- how people change on their own over time: we get older, we get
tired, we get bored, etc.
- selection bias
- differential attrition, a kind of selection bias, that is caused by the study itself.
- regression to mean
- experimenter bias--solution double blind studies
- pygmalion effect-the expectations become a self-fulfilling prophecy.
- Hawthorne effect: people alter their performance because of the attention that the
study focuses on them.
Lecture 3
Descriptive Statistics
- There is two types of statistics: Inferential and descriptive
- DS is a way to characterize some data we have collected (our sample) without
attempting to go beyond that data (to understand a population)
- Only what the data show
- Includes:
- Measures of central tendency (where is most of the data concentrated,
middle?)
- Measures of dispersion (how spread out are the data?)
- Features of the distribution (what is the shape of the distribution?)
- In statistics we fit models to our data
- we use a statistical model to represent what is happening in the real world
- The mean is a hypothetical value
- it doesn’t have to be a value that actually exists in the data set
- As such, the mean is simple statistical model
- outcome i = (model) + error i
Les avantages d'acheter des résumés chez Stuvia:
Qualité garantie par les avis des clients
Les clients de Stuvia ont évalués plus de 700 000 résumés. C'est comme ça que vous savez que vous achetez les meilleurs documents.
L’achat facile et rapide
Vous pouvez payer rapidement avec iDeal, carte de crédit ou Stuvia-crédit pour les résumés. Il n'y a pas d'adhésion nécessaire.
Focus sur l’essentiel
Vos camarades écrivent eux-mêmes les notes d’étude, c’est pourquoi les documents sont toujours fiables et à jour. Cela garantit que vous arrivez rapidement au coeur du matériel.
Foire aux questions
Qu'est-ce que j'obtiens en achetant ce document ?
Vous obtenez un PDF, disponible immédiatement après votre achat. Le document acheté est accessible à tout moment, n'importe où et indéfiniment via votre profil.
Garantie de remboursement : comment ça marche ?
Notre garantie de satisfaction garantit que vous trouverez toujours un document d'étude qui vous convient. Vous remplissez un formulaire et notre équipe du service client s'occupe du reste.
Auprès de qui est-ce que j'achète ce résumé ?
Stuvia est une place de marché. Alors, vous n'achetez donc pas ce document chez nous, mais auprès du vendeur melisanver. Stuvia facilite les paiements au vendeur.
Est-ce que j'aurai un abonnement?
Non, vous n'achetez ce résumé que pour $8.36. Vous n'êtes lié à rien après votre achat.
Peut-on faire confiance à Stuvia ?
4.6 étoiles sur Google & Trustpilot (+1000 avis)
65040 résumés ont été vendus ces 30 derniers jours
Fondée en 2010, la référence pour acheter des résumés depuis déjà 15 ans
Récemment vu par vous
Notes de cours ·
Comprehensive AI notes
Cas ·
Ondernemingsplan
Examen ·
LML4801 EXAM PACK 2022
Examen ·
CCRNEXAMCram_PDF
Examen ·
Technisch ontwerp
Notes de cours ·
Intro to Ecology
Pack ·
AHIP Final Exam 2023
Notes de cours ·
consumption data
