Introduction to Statistics using R exam with complete solution
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Course
Introduction to Statistics
Institution
Introduction To Statistics
What is inferential statistics?
sample > measure values > make inference about whole population
What is causal inference?
prediction of intervention, missing data imputation
In causality, what are all paths that start with an arrow pointing to the independent variable and end wi...
Introduction to Statistics using R exam
with complete solution
What is inferential statistics?
sample > measure values > make inference about whole population
What is causal inference?
prediction of intervention, missing data imputation
In causality, what are all paths that start with an arrow pointing to the independent
variable and end with an arrow pointing to the dependent variable (to be controlled)
called?
back-door paths
How can I block back-door paths?
include 3rd variables in regression models, matching based on confounding variables
Example: We want to investigate how personality (agreeableness) of a manager is
related to his/her employee well-being. -> we are expecting a positive relationship. What
would be H0?
The agreeableness of a manager does not affect his/her employees wellbeing
(experiment)
H1 represents...
what you want to observe
You either __ / __ your hypothesis, but you do not proof it __
confirm / disconfirm; true / false
The manipulated variable is the___
independent one
Examples for nominal skala
gender
Examples for ordinal skala
grades
Example for interval Skala
Likert Skala
Examples for Ratio skala
, Kelvin, Time
What is probability?
o 1. view: long term frequencies, probability of the same event happening many times
o 2. view: personal belief = Bayesian statistics, subjective Probability/personal belief
Axioms of Probability
a set of rules that probabilities defined on a sample space must follow
Die cumulative discribution function does what compared to the probability mass
function?
Go up
We cannot compute the probability of each number up to x either because there are just
too many numbers in between. Solution
using integral to create a probability density function.
4: We showed the results to our supervisor and the supervisor advised that we add
gender as another predictor. We did so but results showed that our explained variance
in dependent variable (R2) did not change. Based on this, which of the following is
correct?
Adding gender does not improve our model fit.
Law of large numbers
increased sample size > mean gets closer to expected mean
Central Limit Theorem (CLT)
with the addition of new random variables a normal distribution is created
What is the aim of the Exploratory Factor Analysis (EFA)
reproduce the observed relationships among a group of indicators with a smaller set of
latent variables.
The higher the common variance, the ___ the possibility that your factors belong
together as they highly correlate with each other
higher
What do I do it the commonality of an item is low?
reconsider / change
What does PCA stand for?
principal component analysis
PCA vs Max. Likelihood
PCA: does not differentiate between common / unique variance
Max. Likelihood: does differentiate
What does it mean if Max likelihood differentiates between unique variance?
how uniqueliy q item is correllated to the construct
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