Analytics
ANALYTICS
FCSC DIPLOMA PROFESSIONAL PROFILE 3:
Synopses
DATA ANALYTICS
FCSC Diploma Module 3.1
FCSC DIPLOMA
Survey methodology
• Introduction to surveys and their applications
• Planning the steps and activities of a statistical survey
• Sampling frames
• Precision assessment - estimates, confidence intervals, and precision measures
• Sampling designs in terms of estimation, precision assessment, and sample size calculations - simple random sampling, stratified sampling, cluster sampling (with equal and unequal probabilities selection), and multi-stage sampling (with equal and unequal probabilities selection)
• Use of auxiliary information in surveys - qualitative data (post-stratification), and quantitative data (ratio and regression estimation)
• Non-sampling errors - coverage errors, measurement errors, processing errors, and non-response
• Domain estimation - estimation with and without auxiliary information, and estimation of different parameters
FCSC Diploma Module 3.2
Time series analysis including seasonal adjustment
• Introduction to time series analysis - time series data, simple forecasting models, case studies, time series in R, time plots, seasonal plots, seasonal versus cyclical, lag plots and autocorrelation, and white noise
• Decomposition of a time series - time series components and their estimation, standard decomposition methods, seasonal adjustment, forecasting and decomposition, and decomposition in R
• Exponential smoothing - simple exponential smoothing, trend methods, seasonal methods, taxonomy of exponential smoothing methods, and exponential smoothing in R
• Auto-regressive integrated moving average (ARIMA) models - stationarity and differencing, non-seasonal ARIMA models, estimation and order selection, ARIMA modelling in R, forecasting, seasonal ARIMA models, and ARIMA models in R
• Visualisation of time series
FCSC Diploma Module 3.3
Data analysis
• Types of statistical variables - fundamentals of bivariate and multivariate data analysis, measures of central tendency, measures of association, and measures of dispersion and location
• Tabulations and association measures for two qualitative variables - frequency tables, association tests, and association measures
• Graphical representation of relationships between variables
• Principal component analysis (PCA) - PCA procedure, interpretation of principal components, geometry of principle components, PCA in R, and dimension reduction methods
• Factor analysis (FA) - introduction to exploratory FA, notation and terminology, model assumptions, FA methods, factor rotation, appropriate data for FA, PCA versus FA, and FA in R
• Correspondence analysis (CA) - data and independence hypothesis, chi-squared and chi-squared distances, row and column profiles, plotting rows and columns, diagnostics, supplementary points, and CA analysis in R
FCSC Diploma Module 3.4
Econometric methods
• Introduction to econometric methods
• The nature of econometrics
• Correlation versus causality - a ceteris paribus analysis
• The multiple linear regression model - the ordinary least squares (OLS) estimator, interpretation, inference (tests of significance), dummy variables, finite sample properties of the OLS, asymptotic properties of the OLS
• Relaxing the assumptions of the classical model - specification (omitted variables, structural stability, Chow test), multicolinearity, heteroscedasticity (detection using White test, estimation using generalised least squares (GLS)), and autocorrelation (detection using Durbin-Watson and Breusch-Godfrey tests, estimation using GLS, estimated GLS, and Newey-West)
• Regression models with time series
• Applications with economic data
Data Analytics
DATA ANALYTICS
PROFILE 3:
Synopses
DATA ANALYTICS
FCSC Diploma Module 3.1
Survey methodology
• Introduction to surveys and their applications
• Planning the steps and activities of a statistical survey
• Sampling frames
• Precision assessment - estimates, confidence intervals, and precision measures
• Sampling designs in terms of estimation, precision assessment, and sample size calculations - simple random sampling, stratified sampling, cluster sampling (with equal and unequal probabilities selection), and multi-stage sampling (with equal and unequal probabilities selection)
• Use of auxiliary information in surveys - qualitative data (post-stratification), and quantitative data (ratio and regression estimation)
• Non-sampling errors - coverage errors, measurement errors, processing errors, and non-response
• Domain estimation - estimation with and without auxiliary information, and estimation of different parameters
FCSC Diploma Module 3.2
Time series analysis including seasonal adjustment
• Introduction to time series analysis - time series data, simple forecasting models, case studies, time series in R, time plots, seasonal plots, seasonal versus cyclical, lag plots and autocorrelation, and white noise
• Decomposition of a time series - time series components and their estimation, standard decomposition methods, seasonal adjustment, forecasting and decomposition, and decomposition in R
• Exponential smoothing - simple exponential smoothing, trend methods, seasonal methods, taxonomy of exponential smoothing methods, and exponential smoothing in R
• Auto-regressive integrated moving average (ARIMA) models - stationarity and differencing, non-seasonal ARIMA models, estimation and order selection, ARIMA modelling in R, forecasting, seasonal ARIMA models, and ARIMA models in R
• Visualisation of time series
FCSC Diploma Module 3.3
Data analysis
• Types of statistical variables - fundamentals of bivariate and multivariate data analysis, measures of central tendency, measures of association, and measures of dispersion and location
• Tabulations and association measures for two qualitative variables - frequency tables, association tests, and association measures
• Graphical representation of relationships between variables
• Principal component analysis (PCA) - PCA procedure, interpretation of principal components, geometry of principle components, PCA in R, and dimension reduction methods
• Factor analysis (FA) - introduction to exploratory FA, notation and terminology, model assumptions, FA methods, factor rotation, appropriate data for FA, PCA versus FA, and FA in R
• Correspondence analysis (CA) - data and independence hypothesis, chi-squared and chi-squared distances, row and column profiles, plotting rows and columns, diagnostics, supplementary points, and CA analysis in R
FCSC Diploma Module 3.4
Econometric methods
• Introduction to econometric methods
• The nature of econometrics
• Correlation versus causality - a ceteris paribus analysis
• The multiple linear regression model - the ordinary least squares (OLS) estimator, interpretation, inference (tests of significance), dummy variables, finite sample properties of the OLS, asymptotic properties of the OLS
• Relaxing the assumptions of the classical model - specification (omitted variables, structural stability, Chow test), multicolinearity, heteroscedasticity (detection using White test, estimation using generalised least squares (GLS)), and autocorrelation (detection using Durbin-Watson and Breusch-Godfrey tests, estimation using GLS, estimated GLS, and Newey-West)
• Regression models with time series
• Applications with economic data
