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Lecture notes PS5801 (2022-23): Advanced Research Methods PG (PS5801) $9.04   Add to cart
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Lecture notes PS5801 (2022-23): Advanced Research Methods PG (PS5801)

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  • Course
  • PS5801 : Advanced Research Methods PG (PS5801)
  • Institution
  • The University Of Aberdeen (Aberd)

I have made easy to understand notes on statistics. They are comprehensive lecture notes on the PS5801 course but would be helpful to anyone trying to understand statistical models such as MLMs, Logistic Regression, (non)parametric tests and a basic overview of t tests. I found it difficult to unde...

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Document information

  • July 19, 2023
  • 6
  • 2022/2023
  • Class notes
  • Dr konopka
  • All classes

Subjects

  • statistics
  • psychology
  • research methods
  • postgraduate psychology
  • mres psychology
  • easy statistics
  • statistical analysis
  • ttest
  • logistic regression
  • mixed linear models
  • non parametric tests
  • parametric tests

Written for

  • The University of Aberdeen (Aberd)
  • Unknown
  • PS5801 : Advanced Research Methods PG (PS5801)
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Summary: So what
exactly is it?
Logistic regression could be considered a mechanism for
calculating probability
In simple terms it is a way to estimate the probability
of an event happening when considering the role of your
data
It can be used to predict the dependent variable. This is
provided the variables are dichotomous (there are two
of them like true/false)


How to calculate
it? LOGISTIC
It is fairly straightforward to
calculate logistic regression in R.
You can use the glm() function.
REGRESSI
In SPSS go to analyze >
regression > binary regression and
then put in your variables

, So what is the difference
between logistic and linear
regression?
The main difference between the two is that in
linear regression is used when the dependent
variable is continuous
Logistic regression is used when the variables are
binary
Linear regression find the best fitting line
Logsitic regression fits line values in the sigmond
curve



Akaike Information
Criterion (AIC) LOGISTIC
The AIC is a popular model used in LG
It shows how well a model fits the data it
is describing
REGRESSIO
The AIC is effective because it penalises
dat with more IVs. Every time you add a
variable to a model the R-square
CONT. 1
increases, so this eliminates this issue.

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