Nytt BA kompendiet
1.2 Causal effect and idealized experiments
Causal effect is described as when one factor causes a change in another factor. For
example, fertilizer equals more tomatoes, and reading before a test increases test result.
To measure the causal effect, you need an experiment with a control group and a treatment
group. The control group receives no treatment while the treatment group does.
If the groups are assigned at random this is called a randomized control experiment.
1.2.1 Forecasting
This is the prediction of future value of a variable. It’s not necessary to know the causal
relationship to create a good prediction. The use of observation is therefore a good tool.
1.3 Experimental data
When you conduct an experiment and obtain data. Observational data can for example be
surveys etc.
1.3.1 Cross sectional data
Can be described as data on different entities for a certain time period.
1.3.2 Time series data
Is data for a single entity collected at different or multiple time periods.
1.3.3 Panel data
Is referred to as longitudinal data, and is data for multiple entities, observed over two or
multiple time periods.
2.1 Random variables and probability distributions.
A random variable is when an effect is random, it can be discrete or continuous
2.1.1 Discrete
Are quantitative variables which shows the certain amount, for example observation. It Can
have values like 0, 1 or 2.
2.1.2 Continuous
Is also quantitative and can be described as measurable unites, that can have every set of
values.
2.1.3 Probability distribution of a discrete random variable
Is the sum of all possible variables added up to 1.
2.1.4 Discrete random variable distribution
2.1.5 Bernoulli distribution
If the discrete random variable is binary, that is an outcome of either 0 or 1.
2.16 Continuous random variable
2.2 expected value of a random variable
Denoted E(Y), the expected value of Y Is also called the expectation Y, or the mean of Y.
Is the weighted average of the variables possible outcomes.
2.2.1 Standard deviation
Measures the difference or spread of a probability distribution.