Probability and statistical modelling
- A ppt and a YouTube video to help you understand these two concepts
Descriptive Statistics:
- used to describe the basic features of the data in a study and together with simple graphics analysis, form the basis of virtually every quantitative analysis of data.
Inferential Statistics:
- used to reach conclusions that extend beyond the immediate data alone. For e.g. use inferential statistics to try to infer (conclude) from the sample data what the population might think.
Measures of central tendency
- a summary measure that attempts to describe a whole set of data with single value representing the middle (or centre) of its distribution)
Dispersion (also called variability, scatter, or spread)
- Dispersion measures how the various elements behave with regards to some sort of central tendency, usually the mean. Measures of dispersion include range, interquartile range, variance, standard deviation and absolute deviation.
Probability and probability distributions (a table or an equation that links each outcome of a statistical experiment with its probability of occurrence)
Types of distributions:
- binomial (a success/failure experiment),
- Poisson (probability of a given number of events occurring in a fixed interval of time and/or space...)
- normal (a function that tells the probability that any real observation will fall between any two real limits...)
Confidence intervals
- In statistics, a confidence interval is a measure of the reliability of an estimate.
One and two sample hypothesis tests
- An introduction to one and two tail tests used in hypothesis testing using a standard bell curve with a population mean and sample mean. It includes introduction to the rejection region, p-value, and alpha.
Chi-square tests:
- used to investigate whether distributions of categorical variables differ from one another.
One way analysis of variance
- In statistics, one-way analysis of variance is a technique used to compare means of two or more samples. This technique can be used only for numerical data.
Linear regression
- Linear regression consists of finding the best-fitting straight line through the points. The best-fitting line is called a regression line.
Correlation:
- one of the most common and most useful statistics. A correlation is a single number that describes the degree of relationship between two variables.