ALL THE NOTES YOU WILL NEED TO PASS THE SIMPLE LINEAR, MULTIPLE LINEAR REGRESSION & LOGISTIC REGRESSION SECTION IN THE EXAM.
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Second-year second-semester applied statistical science notes.
Notes made from the lecture slides, textbook and l...
statistical interference: attempt to reach conclusion concerning a complete set of observations
(population), but only using a subset thereof (sample)
hypothesis testing
Quantifying linear relationship between variables
Correlation analysis
> Objective: more descriptive
> Data: bivariate
> Describes:
direction (positive or negative) &
magnitude (strong or weak)
of the linear relationship between two variables.
> Note: correlation ≠ causation
Regression analysis
> Objective: emphasis on prediction
> Data: bivariate (simple) or
multivariate (multiple)
> Describes:
relationship between a dependent variable and one or more independent
the goal is to create a mathematical equation (regression equation) that predicts the value of the dependent variable
based on the values of the independent variables.
Basically regression analysis helps us understand how changes in the independent variables are related to changes in
the dependent variable.
> Uses: Independent variable(s) to estimate value of dependent variable
,Correlation analysis
> Sample correlation coefficient: estimate of 𝝆, used to measure strength of linear relationship in sampled observations
> Population correlation coefficient: measures strength of association between two variables
Properties of correlation coefficient:
> Unit free
> Bounded between -1 and 1
Perform correlation analysis in R
BACK TO EXAMPLE
, Significance of correlation coefficient
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