Business Quantitative Analysis [QU1] Examination Blueprint

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Jasper Alexander
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1 Business Quantitative Analysis [QU1] Examination Blueprint Purpose The Business Quantitative Analysis [QU1] examination has been constructed using an examination blueprint. The blueprint, also referred to as the test specifications, outlines the content areas covered on the examination and the weighting allotted to each content area. This document also lists the topics, the level of competence for each topic, and the related learning objectives. In addition, information is provided on the proportion of each question type presented in the examination (that is, multiple choice, quantitative problems, and so on). Use Students should use the examination blueprint to prepare for the course examination. The blueprint may not include all the topics listed in the course materials. However, students are still responsible for acquiring a broad-based knowledge of all topics not listed in the blueprint since these topics will be tested in assignment and review questions. The topics not listed in the blueprint will also provide students with a greater depth of understanding of the concepts. Examination Objectives The objective of this 3-hour, comprehensive Business Quantitative Analysis [QU1] examination is to test CGA students on the prerequisite knowledge that is required for advancement into many courses, as well as to ensure the broad-based knowledge in statistical concepts needed to function properly in upper-level education and certification courses. Examination Guidelines for Questions i) Question Type The following are guidelines on the type of questions and their approximate weightings: Question Item Percentage Weighting Multiple-choice questions 100% ii) Question Content The following table is organized according to content area and provides information on topics, learning objectives, weighting, and levels of competence. Examination sessions: March 2015; June 2015 Page 1 of 11

2 Business Quantitative Analysis [QU1] Examination Blueprint 1. Data description and presentation 4% to 6% 1.1 Introduction to statistics 1.2 Types of data 1.3 Charts for nominal-level data 1.4 Graphing techniques for interval data 1.5 Describing the relationship between two variables Distinguish between populations and samples. Distinguish between the various types of data. Explain the use of bar charts and pie charts. Arrange a set of data into classes and calculate frequencies, relative frequencies, and cumulative frequencies Construct a scatter diagram showing the relationship between two variables. Examination sessions: March 2015; June 2015 Page 2 of 11

3 2. Summary measures 6% to 10% 2.1 Summation notation 2.2 Measures of central location 2.3 The geometric mean 2.4 Measures of variability 2.5 Measures of relative standing 2.6 Measures of association Use the summation command in a mathematical formula. Calculate and interpret the arithmetic mean, median, and mode for a data set. Calculate and interpret the geometric mean Calculate and interpret the range, variance, standard deviation, and coefficient of variation for a data set. Construct and interpret a box plot. Calculate the covariance and coefficient of correlation and interpret the least squares line. Examination sessions: March 2015; June 2015 Page 3 of 11

4 3. Fundamentals of probability 8% to 12% 3.1 Introduction to probability 3.2 Probability rules 3.3 Probability distributions 3.4 Binomial distribution 3.5 Poisson distribution Explain the basic concepts of probability, and determine marginal, joint, and conditional probabilities. Determine probabilities using the complement, multiplication, and addition rules. Calculate the expected value and the variance for a discrete probability distribution. Determine binomial probabilities from the tables and calculate the mean and variance of a binomial distribution. Determine Poisson probabilities from the tables. Examination sessions: March 2015; June 2015 Page 4 of 11

5 4. Normal probability distribution and sampling 8% to 12% 4.1 Normal probability distribution 4.2 The Student t distribution 4.3 Data collection and sampling 4.4 Sampling distribution of the mean Calculate probabilities by standardizing random variables and using the normal probability table. Calculate probabilities using the t distribution tables. Describe sampling methodologies and explain the nature and consequences of sampling and nonsampling errors. Explain the concept of a sampling distribution for a sample statistic. Examination sessions: March 2015; June 2015 Page 5 of 11

6 5. Sampling distributions and estimation 10% to 18% 5.1 Sampling distribution of the proportion 5.2 Estimation 5.3 Sample size determination 5.4 Introduction to hypothesis testing Determine probabilities by using the sampling distribution of the proportion. Obtain interval estimates for and p under different assumptions. Determine the sample size based on a desired confidence level and interval width. Conduct a hypothesis test on the mean when the population standard deviation is known. Examination sessions: March 2015; June 2015 Page 6 of 11

7 6. Hypothesis testing 14% to 18% 6.1 Hypothesis tests on the mean population standard deviation unknown 6.2 Hypothesis tests on the proportion 6.3 Hypothesis tests on the difference between two means independent samples 6.4 Hypothesis tests on the difference between two means matched pairs 6.5 Hypothesis test on the difference between two proportions Conduct a hypothesis test on the mean when the population standard deviation is unknown. Conduct a hypothesis test on the population proportion. Conduct a hypothesis test for the difference between two means when the population standard deviations are known and when they are unknown. Conduct a hypothesis test for dependent samples. Construct a hypothesis test for the difference between two population proportions. Examination sessions: March 2015; June 2015 Page 7 of 11

8 7. Regression and correlation 10% to 16% 7.1 Simple linear regression 7.2 Assessing the model 7.3 Correlation 7.4 Multiple regression Estimate the parameters of a simple linear regression Conduct a hypothesis test on the slope of the sample regression line and interpret the results. Calculate the sample coefficient of correlation and conduct a hypothesis test on the coefficient of correlation. Analyze a multiple regression model using Excel s summary output results. Examination sessions: March 2015; June 2015 Page 8 of 11

9 8. Index numbers and time series 6% to 8% 8.1 Index numbers 8.2 Time series and forecasting 8.3 Trend analysis Calculate and interpret Laspeyres and Paasche index numbers Smooth a time series using moving averages. Calculate seasonal index numbers. Examination sessions: March 2015; June 2015 Page 9 of 11

10 9. Statistical decision theory 6% to 10% 9.1 Introduction to decision analysis 9.2 Decision analysis with additional information 9.3 Applying decision making under uncertainty Calculate the expected monetary value. Calculate posterior probabilities using Bayes law. Apply the appropriate techniques in decision making under uncertainty. Examination sessions: March 2015; June 2015 Page 10 of 11

11 10. Linear programming 8% to 12% 10.1 Solving linear equations and graphing linear inequalities 10.2 The linear programming model 10.3 Graphical sensitivity analysis 10.4 Using Excel Solver 10.5 Applications Graph straight-line equations and linear inequalities, and determine the intersection point of two equations. Obtain the optimal solution for a linear programming model graphically and interpret the results. Determine the ranges for the objective function coefficients over which the optimal solution remains unchanged. Use Excel Solver to solve linear programming problems, and analyze and interpret the Sensitivity report. Formulate problems as linear programming models. Examination sessions: March 2015; June 2015 Page 11 of 11

Distinguish between different types of numerical data and different data collection processes.

Level: Diploma in Business Learning Outcomes 1.1 1.3 Distinguish between different types of numerical data and different data collection processes. Introduce the course by defining statistics and explaining