# General

### Normality

The normality assumption is one of the most misunderstood in all of statistics. In multiple regression, the assumption requiring a normal distribution applies only to the disturbance term, not to the independent variables as is often believed. Perhaps the confusion about this assumption derives from difficulty understanding what this disturbance term refers to – simply

### Probability

The origin of the probability theory starts from the study of games like cards, tossing coins, dice, etc. But in modern times, probability has great importance in decision making. According to the classical theory, probability is the ratio of the favorable case to the total number of equally likely cases. Empirical or relative frequency probability

### Mathematical Expectation

Mathematical expectation, also known as the expected value, is the summation or integration of a possible values from a random variable. It is also known as the product of the probability of an event occurring, denoted P(x), and the value corresponding with the actual observed occurrence of the event. The expected value is a useful

### Hypothesis Testing

Hypothesis testing was introduced by Ronald Fisher, Jerzy Neyman, Karl Pearson and Pearson’s son, Egon Pearson. Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. Hypothesis Testing is basically an assumption that we make about the population parameter. Key terms and concepts Null hypothesis: Null hypothesis is

### Data Levels and Measurement

Overview On this page you’ll learn about the four data levels of measurement (nominal, ordinal, interval, and ratio) and why they are important. Let’s deal with the importance part first. Knowing the level of measurement of your variables is important for two reasons. Each of the levels of measurement provides a different level of detail.