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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
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
