An overview of all exam materials and key points you have to know before subtest 1. Made for several premaster programs at the UTwente. Based on all assignments and lectures by Henk van der Kolk. An addition to the R-Guide that is provided in the course. (Overview of key points and important inform...
Inferential Statistics test 1 - Unit 501, 502, 503, 520, 521, 522, 530, 531, 540,
541 & 551
Cicely Bullee
Libraries used in R: tidyverse, janitor, ggplot, stats, broom, foreign (words), haven (numbers).
, 1
Unit 501
Key terms:
(standard) normal distribution
empirical rule
statistic
parameter
population proportion
sample distribution
sampling distribution
standard error
confidence interval (for the proportion)
Margin of error (of proportions)
• differentiate between statistics and parameters;
• explain what a sampling distribution is;
• explain what a sampling distribution of the proportion is;
• know that the shape of the sampling distribution of a proportion can be approximated by
the normal distribution;
• Know that another word for the standard deviation of the sampling distribution is
'standard error'
• explain how the shape of the sampling distribution of the proportion is related to the
population distribution and the sample size (central limit theorem, standard error
decrease);
• work with the online tool for normal distributions and the 68%-95%-99.7% rule.
Inference: saying something about the population on the basis of a single sample.
Percentage: between 0 - 100%
Fraction: between 0 - 1
Proportion: between 0 – 1
Population proportion: π (mean of all proportions)
Sample proportion: p
Sampling distribution of a proportion: in a large number of samples from a population with π, of size n,
many will have slightly different p’s (the expected p = π).
- Similar to a normal distribution.
- Not all proportions are possible (n=100, 50.5% is impossible).
- Proportion can’t be <0 or > 1
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