Overview

Provide students with the tools to collect, analyse and interpret their own data to answer research questions within the context of business and finance.

Indicative Content:

• Sampling Methods and The Central Limit Theorem • Point Estimates and Confidence Intervals about Two Populations • Hypothesis Testing about Two Populations • Correlation and Linear Regression Analysis • Multiple Regression and Correlation • Multiple Standard Error of Estimate • The ANOVA Table • Nonparametric Methods (Chi-square) • Time Series and Forecasting Decision Theory

Assessment Strategy

-threshold -D- to D+ (40-49%): No major omissions or inaccuracies in the deployment of information/skills. Some grasp of theoretical/conceptual/practical elements. Integration of theory/practice/information present intermittently in pursuit of the assessed work's objectives.

-good -B- to B+ (60-69%): Very good performance Most of the relevant information accurately deployed. Good grasp of theoretical/conceptual/practical elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Evidence of the use of creative and reflective skills.

-excellent -A- to A+ (70%+): Outstanding performance. The relevant information accurately deployed. Excellent grasp of theoretical/conceptual/practice elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Strong evidence of the use of creative and reflective skills.

-another level-C- to C+ (50-59%): Much of the relevant information and skills mostly accurately deployed. Adequate grasp of theoretical/conceptual/practical elements. Fair integration of theory/practice/information in the pursuit of the assessed work's objectives. Some evidence of the use of creative and reflective skills.

Learning Outcomes

• Apply probability and non-probability sampling techniques and statistical inference to analyze data.

• Investigate a business problem and apply the relevant statistical techniques and tools to find answers and/or make interpretations.

• Perform hypothesis tests for nominal level data, including chi – square test for comparing frequencies and independence on a contingency table.

• Use forecasting tools, such as linear single and multiple regression, correlation analysis and time series to form the basis of predicting future values and forecasting.

• Use point and interval estimation techniques and hypothesis testing techniques for statistical inference about two populations and explain their importance to management decision making.