MA10212: Probability & statistics 1B
Academic Year:  2019/0 
Owning Department/School:  Department of Mathematical Sciences 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Certificate (FHEQ level 4) 
Period: 

Assessment Summary:  CW 25%, EXTH 75%* 
Assessment Detail: 
*Assessment updated due to Covid19 disruptions 
Supplementary Assessment: 

Requisites:  Before taking this module you must take MA10211 
Description:  Aims: To introduce probability theory for continuous random variables. To introduce statistical modelling and parameter estimation and to discuss the role of statistical computing. Learning Outcomes: After taking this unit the students should be able to: * Solve a variety of problems and compute common quantities relating to continuous random variables. * Formulate, fit and assess some statistical models. * Use the R statistical package for simulation and data exploration. Skills: Numeracy T/F A Problem Solving T/F A Data Analysis T/F A Information Technology T/F A Written and Spoken Communication F (in tutorials). Content: Definition of continuous random variables (RVs), cumulative distribution functions (CDFs) and probability density functions (PDFs). Some common continuous distributions including uniform, exponential and normal. Transformations of RVs. Discussion of the role of simulation in statistics. Use of uniform random variables to simulate (and illustrate) some common families of discrete and continuous RVs. Results for continuous RVs analogous to the discrete RV case, including mean, variance, standard deviation, properties of expectation, joint PDFs (including dependent and independent examples), independence (including joint distribution as a product of marginals), covariance, correlation. The distribution of a sum of continuous RVs, including normal and exponential examples. Statement of the central limit theorem (CLT). Introduction to model fitting; exploratory data analysis (EDA) and model formulation. Parameter estimation via method of moments and (simple cases of) maximum likelihood. Sampling distributions, particularly of sample means. Point estimates and estimators. Estimators as random variables. Bias and precision of estimators. Graphical assessment of goodness of fit. 
Programme availability: 
MA10212 is Compulsory on the following programmes:Department of Economics

Notes:
