statistics

• Mode is a measure of central tendency that represents the most frequently occurring value in a dataset.
• Median is a measure of central tendency that divides a data set into two equal parts, with half of the values being below it and half above it.
• Statistics is a branch of Mathematics that deals with the collection, analysis and interpretation of data.
• Descriptive statistics are used to describe the characteristics or features of a dataset and look at distribution, central tendency and variability.
• Types of Descriptive Statistics include Distribution, which is the frequency of different outcomes in a population or sample.
• Central tendency in descriptive statistics is the mean, median and mode.
• Variability in descriptive statistics is measured by the standard deviation, minimum and maximum values, range, kurtosis and skewness.
• The standard deviation in descriptive statistics shows the amount of variation or dispersion.
• The minimum and maximum value in a dataset are the highest and lowest value.
• The range in descriptive statistics is the size of the distribution value.
• Kurtosis in descriptive statistics shows whether the tail contains extreme values.
• Skewness in descriptive statistics measures the dataset's symmetry.
• Inferential statistics help draw conclusions and make predictions based on a dataset and the results are always in the form of probability.
• Types of Inferential Statistics include Regression Analysis, which shows the difference between dependent and independent variables.
• Hypothesis Tests in inferential statistics compare an entire population or assess relationships between variables using samples.
• Sample Space is the set of possible outcomes that can occur in a trial, for example, when tossing a coin, the set of possible outcomes is (heads, tails).
• Continuous random variables represent measured data, such as heights, weights, and temperatures.
• Confidence Intervals in inferential statistics observe the variability in a statistic to draw an interval estimate for a parameter.
• Variable is a characteristics that is observable or measurable in every unit or universe.
• Random Variable is a numerical description of the outcome of a random event.
• In Probability, the actual outcome is determined by chance.
• Discrete random variables represent count data, such as the number of defective chairs produced in a factory.
• Qualitative/Categorical Variables cannot be measured, usually descriptive or textual, and are either ordinal or nominal data.
• Quantitative results from counting or measuring something and can be ratio or interval data.
• Event is one single outcome as the result of a trail or experiment, for example, getting a three when rolling a die, or getting an eight of clubs when choosing a card out of a deck.
• Experiment is when the outcomes are always uncertain in a series of actions, for example, selecting a card from a deck, tossing a coin or rolling a die.
• In Probability, the outcome of a random event cannot be determined before it occurs, but it may be any one of several possible outcomes.
• Outcome is a possible result you can get from doing a trial or experiment, for example, you could get heads when tossing a coin.
• Probability/ Probability theory is a branch of Mathematics concerned with the analysis of random phenomena

• Random variable is a function that associates a real number to each element in the sample space
• Sample space is a set notation containing all possible outcomes