The University of Kent (UKC) • Business Management

Probability, outcome, event (simple and combined) definition. Types of probability (Classical, Empirical, Subjective). Calculating probability. Venn diagrams. Probability formulas (combining events). Mutually exclusive events (A or B), Joint probability (A and B), Complimentaries (not A), Conditional probabilities (A/B). Dependent vs Independent events. Tree diagram. Includes exercises.

Random Variables. Discrete vs Continuous Random Variables. Distribution vs Probability Distribution. Types of Means/Averages (Arithmetic mean, Weighted average, Mean of an Expected Value, Variance of a Dicrete Random Variable, Expected value of a DiscreteRandom Variable, Expected Value of X squared). Bernoulli Trial. Binomial Distribution. Probability Mass Function (formula and exlanation). Cumulative Binomial Probabilities. Mean and Variance of a Binomial Distribution. Shape of a Binomial Distr...

Definiton of correlation and variables. Graphical Methods (Scatter plot). Linear relationship (positive relationship vs negative relationship) and Degree of Correlation. Non-linear relationship example. Numerical Measures and formulas (Covariance, Coefficient of Correlation, Coefficient of Determination). Least Squares Line Method, Total Sum of Squares. Deterministic vs Stochastic analysis.

Hypothesis Testing. Steps in a Statistical Test. Hypothesis (Null, Alternative). P-value and Threshold definition. Level of Significance vs Level of Confidence in a test. One-sided vs Two-sided hypothesis testing. Critical zone definition. Student distribution. Types of errors (Type I, Type II).

Link between correlation and slope. Relationships between statistical coefficients. Linear Regression Model (Deterministic vs Stochastic). Regression Model formula. Characteristics of Errors. Estimating the parameters (intercept, slope, variance of errors). Mean Square Error formula. Forecasting analysis. Methods of forecasting. Quantitive vs Qualitative forecasting models. Steps for forecasting. Analysing forecasting model power.

Continuous Variables. Continuous distribution. Probability Density Function. Normal Distribution (explanation and characteristics). Calculation continuous probabilities. Standard Normal Distribution. Non-Standard Normal Distribution. Uniform Distribution.

Definition of sampling. Types of sampling (Probability sampling, Non-probability sampling). Simple Random Sampling. Sampling Distribution vs Sample Distribution. Repetitive sampling measures of central tendency. Estimate vs Estimator. Desirable qualities of an estimator. Central Limit Theory. Example of Central Limit Theory in Quality Control.

Introduction to statistics. Definition of descriptive vs inferential statistics. Natural, Whole, Rational, Irrational, Real numbers classification. Sets of numbers. Reunion, Intersection, Complimentary, Void sets. Operations with numbers. Standard form for numbers. Vectors. Includes excercises.

Descriptive vs Inferential Statistics. Population vs Sample definitions. Tyes of data (Quantitative, Qualitative). Full explanation and formulas for Measures of central tendency (Mean, Median, Outliers, Mode). Bimodal sets. Best measure of Central Tendency. Full explanation and formulas for Measures of Spread of data (Range, Interquartile Range, Variance, Standard Deviation, Coefficient of Variation). Includes exercises.