Many business problems cannot be solved without the use of mathematical methods. All businesses collect masses of data during the course of their daily activities. This data must be organised and analysed in such a way that all of the key decision-making staff can make sense of it.
Data collection requires techniques which are often sophisticated. Collected data must then be analysed; the business must have some idea of the meaning of their data. The data can be used to diagnose the problems faced by the business.
When business problems have been diagnosed, then a whole range of techniques (many of them mathematical) can be used to help provide solutions to problems. This is why this course is inevitably concerned with the techniques used for problem solving.
But as you will see from the above, the concern does not just end at the use of the technique, it extends back to the collection of the data required by the technique, and the inevitable questions raised about whether the correct methods are being used.
The importance of the above is to prevent you from making the mistake of thinking that QM will merely consist of a set of techniques. Techniques are important, but the reasons why we use them, and the degree of success we can expect from them are much more important.
Overview
Analysis and organisation of data acquired during the course of a business's daily activities, using mathematical methods, so that all key decision-making staff can make sense of it and apply it to problem-solving.
On completion of your course, you will receive two certificates:
Quantitative Methods Diploma issued by Stonebridge Associated Colleges, to view a sample of the college’s award, please click here.
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
On completion of this course you will be eligible to join the following Professional Associations(s):
Anyone who is studying for or has completed a course endorsed by an Ofqual approved awarding body is eligible to join Qualifications Register. The Qualifications Register (QR) is an independent register that aims to serve both members of the public and those professionals who hold recognised qualifications.
QR's primary aim is to provide transparency to members of the public and employers. The QR is a searchable database that allows them to find a practitioner and view the relevant qualification and endorsements the practitioner holds. The member of the public is then able to make an informed decision in relation to the suitability of the practitioner. QR also tracks whether practitioners have insurance, again something that is useful for members of the public. QR can also help professionals gain insurance or find professional indemnity cover. Click here to find out more.
Members of the public can check an independent source with complete confidence that the practitioner has the qualification(s) they claim to have and that any additional CPD the practitioner has, is verified and quality assured to make sure that the CPD is being done on a regular basis. QR tracks a practitioners endorsed CPD (in unison with our CPD accreditation partner Lifetime Awarding) and other key signals to help members of the public and employers to come to a more informed decision about the professional they intend to use. This is an enormously valuable signal that's increasingly important as business becomes more deregulated and privatised.
For practitioners QR helps you market your skills, endorsements and feedback transparently to a wider audience. It also gives you access to a community to help make you more able to progress and succeed in your field of expertise. Click here to find out more.
UNIT ZERO - BASIC MATHS
Unit Objectives
Section 1. Numbers
1. Our Number System
2. The Number Line
3. Counting Numbers
4. Parts of Counting Numbers
5. Rounding
6. Comparisons of Numbers
7. Percentages
8. Summary
Section 2 Algebra
1. Letters for Numbers
2. Indices, Powers and Exponents
3. Basic Rules for Products Involving Indices
4. Sequence of Operations
5. Directed (or Signed) Numbers
6. Manipulating Arithmetic Fractions
7. Multiplication and Division in Algebra
8. Use of Brackets
9. Summary
Section 3 Equations and Coeffecicients
1. Simple Equations
2. Changing the Subject of a Formula
3. Simultaneous Equations
4. Expanding Brackets
5. Coefficients
6. Inequalities
7. Summary
Section 4 Using a Calculator
1. Basic Calculations
2. The Square Root Function
3. The Square Function
4. The General Power Function
5. The Factorial Function
6. The Scientific Notation
7. Use of the Memory Facility
8.Combination of Functions
9. Summary
UNIT ONE - BUSINESS MATHS
Section 1 Financial Analysis
1. Interest Rates
2. Number Patterns
2.1 Pattern 1. Arithmetric Progressions
2.2 Pattern 2. Geometric Progressions
2.3 Pattern 3. Summing the Terms of a Geometric Progression
3. Calculation of Interest Earned
4. Notation
5. Compound Interest
6. A More General Approach to Interest
7. Applying the General Result to Particular Cases
8. Comparison of Interest Rates
9. Present Value of a Future Sum
10. Financially Equivalent Sums
11.Summary
12. Repaying a Loan
12.1 Calculating the Amount of a Loan
12.2 Calculating the Repayments
12.3 Loan Repayment Schedules
12.4 Mortgages
12.5 Annuities
13. Investment Decisions
14. Regular Savings Seach Schemes
14.1 Calculating the Value of a Scheme
14.2 Calculating the Sum to Achieve a Given Valuation
15. Summary
Section 2 Graphs
1. The Grid System
2. Straight Line Graphs
2.1 Straight Lines From an Equation
2.2 Slope of a Line
2.3 The Equation of a Straight Line
2.4 Detailed Inspection of the Equation
2.5 Other Forms of the Equation
2.6 Comparative Slopes
2.7 Summary
3. Other Graphs
4. Summary
UNIT TWO - DATA PRESENTATION
Section 1 Data, Variables and Measurement
1. What is Statistics
2. Measurement
3. Elements, Variables and Data
4. Summary
Section 2 The Frequency Distribution
1. Data, Frequencies Classes and Distributions
2. Frequency Distributions and Variability
3. How to Construct a Frequency Distribution
4. Frequency Distributions and Diagrams
5. Note for Computer Users
6. Types of Distribution
7. Summary
Section 3 Measures of Central Tendency
1. Introduction: Summary Statistics
2. What Are Measures of Central Tendency?
3. The Arithmetic Mean
4. The Mean of a Grouped Frequency Distribution
5. The Median
6. Calculating The Median for Grouped Data
7. The Mode
8. Summary
Section 4 Understanding Averages
1. What Kind of Variable are we Dealing With?
2. How Representative is the Measure of Central Tendency?
3. How are the Data Distributed?
4. Quick Estimate or Thorough One?
5. Do We Plan to Carry Out Further Analysis?
6. Summary
Section 5 Measures of Dispersion or Variation
1. Why Measure Variability?
2. How Can We Measure Variability?
3. Calculating s2 and s For Frequency Data
4. Summary
Section 6 Interpreting the Variance and Standard Deviation
1. Why Do we need the Standard Deviation?
2. Comparing Two or More Sets of Data
3. Chebyshev's Theorem
4. Symmetrical or Bell-Shaped Distributions
5. Z-Scores (Standard Scores)
6. Summary
References
Glossary
UNIT THREE - LINEAR PROGRAMMING
Unit Objectives
Section 1: A Case Study
1. Introduction: A Linear Programming Problem
2. What Does Linear Programming Do?
3. Applications of Linear Programming
4. Jarvis & Partners: Developing a Model
5. Summary
Section 2: Developing a Linear Programming Model
1. Introduction
2. Linear Equations and Linear Inequalities
3. More Case Studies
4. Summary
Section 3: Solving Linear Programming Problems Using Graphical Methods
1. Introduction
2. Jarvis Builders Revisited
3. Summary
Section 4: Practising the Graphical Solution Technique
1. Introduction
2. Superb Audio plc: a Graphical Solution
3. The Small Mine: a Graphical Solution
4. Rest Easy Lounge Suites: a Graphical Solution
5. The Doll's Workshop: a Graphical Solution
6. Summary
Section 5: Shadow Prices and Sensitivity Analysis
1.Introduction
2. Scarce Resources and Shadow Prices
3. Calculating Shadow prices
4. Shadow Prices and Sensitivity
5. Summary
Section 6: The Role of Computer Software
1. Introduction
2. The Simplex Algorithm
3. Simplex Linear Programming - Sample Output
4. More Than Two Decision Variables
5. Limitations of Linear Programming
6. Summary
References
Glossary
UNIT FOUR - REGRESSION AND CORRELATION
Unit Objectives
Section 1. Relationships Between Variables
1. What is Correlation and Regression?
2. Relationships Between Variables
3. Summary
Section 2. The Coefficient of Correlation
1. Correlation Between Variables
2.The Coefficient of Correlation
3.Interpretation of the Coefficient of Correlation
4. Summary
Section 3. Interpreting the Coefficient of Correlation
1. Linearity
2. Causality
3. Trends
4. Sample Size and Accuracy
5. Non-Ratio Scale Data
6. Summary
Section 4. Regression Analysis
1. What is Regression Analysis
2. The Purpose of Regression
3. Finding the Form of the Regression Equation
4. Best Fit - The Least Squares Method
5. Statistical and Spreadsheet Packages
6. Dependent and Independent Variables
7. Summary
Section 5. How Effective is Regression Analysis?
1. What We Have Accomplished So Far
2. Interpretation of the Coefficient of Determination
3. Summary
Section 6. What Next?
1 Where Do We Go From Here?
2. Correlation, Regression and Non-Ratio Scale Data
3. Dealing With a Time Series
4. What Can We Do About Non-Linearity?
5. Must We Always Be Limited to Two Variables?
6. Summary
References
Glossary
UNIT FIVE - PROBABILITY
Unit Objectives
Section 1. The Problem of Measurement
1. Introduction
2. What is Probability
3. Percentages and Odds
4. Summary
Section 2. Events, contingency Tables and Venn
Diagrams
1. Simple and Complex Events
2. Probability Notation and Venn Diagrams
3. Summary
Section 3. Rules for Calculating Probabilities
1. Introduction
2. Conditional Probabilities
3. The Intersection of Events
4. The Union of Events
5. Complementary Events
6. Summary
Section 4. Putting the Rules of Probability to Work
1. Introduction
2. Summary
Section 5. Probability Trees
1. Introduction
2. Probability Trees in Use
3. Summary
Section 6. Counting Techniques
1. Introduction
2. The 'MN' Rule
3. The Permutations Rule
4. The Combinations Rule
5. Summary
References
Glossary
UNIT SIX - PROBABILITY DISTRIBUTION
Unit Objectives
Section 1. Random Variables
1. Introduction
2. Discrete and Continuous Variables
3. Probability and Random Variables
4. Probability Distributions
5. What Do Probability Distributions Look Like?
6. Conclusion
7. Summary
Section 2. Mathematical Functions of Probability Distributions
1. Introduction
2. Functions and Discrete Data
3. Functions and Continuous Data
4. Conclusion
5. Summary
Section 3. Summing Up Probability Distributions
1. Introduction
2. Expected Value For a Discrete Variable
3. The Variance of a Discrete Random Variable
4. Expected Value and Variance: Continuous Variables
5. Interpretation of Expected Value and Variance
6. Summary
Section 4. Binomial Distribution
1. Introduction: A Matter of Insurance
2. The General Form of the Binomial Distribution
3. Expected Value and Variance of a Binomial Distribution
4. Summary
Section 5. Normal Distribution
1. Introduction
2. Characteristics of the Normal Distribution
3. Mean and Standard Deviation of the Normal Distribution
4. Probability and Areas Under the Normal Curve
5. The Standard Normal Distribution
6. Summary
Section 6. Using the Normal Distribution
1. Non-Standard Normal Distributions
2. The Normal Curve and Binomial Probabilities
3. Summary
4. Unit Summary and Conclusions
References
Glossary
Tables of Areas Under the Standard Normal Curve
UNIT SEVEN - INTRODUCTION TO SAMPLING
Unit Objectives
Section 1 Sample and Population
1. Introduction
2. Random Sampling
3. Selecting a Random Sample
4. Can We Trust Sample Estimates?
5. Summary
Section 2 The Behaviour of Sample Estimates
1. Terminology
2. The Central Limit Theorem
3. Summary
Section 3 Point Estimates and Interval Estimates
1. Problems in Estimation
2. Calculation of Interval Estimates
3. Interpretation of Confidence Intervals
4. How Precise Are Interval Estimates?
5. How Precise Are Sample Estimates?
6. Summary
Section 4 Proportions and Percentages
1. Introduction
2. Sampling Distribution of Proportions
3. Confidence Intervals for Proportions
4. Summary
Section 5 Sample Size Problems
1. Introduction
2. Deciding Sample Size: The Method
3. Proportions and Sample Size Problems
4. Summary
Section 6 Significance Testing
1. Introduction
2. Construction of Statistical Tests
3. Possible Mistakes in Statistical Tests
4. Notes Concerning the Use of Sampling Theory
5. Summary
References
Glossary
Table of Areas Under the Standard Normal Curve
UNIT EIGHT - INDEX NUMBERS
Unit Objectives
Section 1 The Measurement of Change
1. Why Measure Change?
2. Simple Index Numbers
3. More Complex Index Numbers
4. The Price Relative Method
5. Summary
Section 2 Weighted Indices and Aggregative Methods of Calculation
1. Weighted Index Numbers
2. Selecting Suitable Weights
3. The Aggregative Method of Calculation
4. Summary
Section 3 Quantity and Value Indices
1. Value and Volume Indices
2. Using a Value Index as a Deflater
3. The UK Retail Prices Index
4. Summary
Section 4 Time Series
1. Introduction
2. A Sales Data Time Series
3. Breaking Down a Time Series
4. Models of Time Series Data
5. Summary
Section 5 Moving Averages
1. Introduction
2. Moving Averages
3. Seasonal Variation
4. Summary
Section 6 Forecasting a Time Series
1. Using Regression Analysis on a Time Series
2. The Multiplicative Model
3. Summary
References
Glossary
What's Included
Many business problems cannot be solved without the use of mathematical methods. All businesses collect masses of data during the course of their daily activities. This data must be organised and analysed in such a way that all of the key decision-making staff can make sense of it.
Data collection requires techniques which are often sophisticated. Collected data must then be analysed; the business must have some idea of the meaning of their data. The data can be used to diagnose the problems faced by the business.
When business problems have been diagnosed, then a whole range of techniques (many of them mathematical) can be used to help provide solutions to problems. This is why this course is inevitably concerned with the techniques used for problem solving.
But as you will see from the above, the concern does not just end at the use of the technique, it extends back to the collection of the data required by the technique, and the inevitable questions raised about whether the correct methods are being used.
The importance of the above is to prevent you from making the mistake of thinking that QM will merely consist of a set of techniques. Techniques are important, but the reasons why we use them, and the degree of success we can expect from them are much more important.
Overview
Analysis and organisation of data acquired during the course of a business's daily activities, using mathematical methods, so that all key decision-making staff can make sense of it and apply it to problem-solving.
On completion of your course, you will receive two certificates:
Quantitative Methods Diploma issued by Stonebridge Associated Colleges, to view a sample of the college’s award, please click here.
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
On completion of this course you will be eligible to join the following Professional Associations(s):
Anyone who is studying for or has completed a course endorsed by an Ofqual approved awarding body is eligible to join Qualifications Register. The Qualifications Register (QR) is an independent register that aims to serve both members of the public and those professionals who hold recognised qualifications.
QR's primary aim is to provide transparency to members of the public and employers. The QR is a searchable database that allows them to find a practitioner and view the relevant qualification and endorsements the practitioner holds. The member of the public is then able to make an informed decision in relation to the suitability of the practitioner. QR also tracks whether practitioners have insurance, again something that is useful for members of the public. QR can also help professionals gain insurance or find professional indemnity cover. Click here to find out more.
Members of the public can check an independent source with complete confidence that the practitioner has the qualification(s) they claim to have and that any additional CPD the practitioner has, is verified and quality assured to make sure that the CPD is being done on a regular basis. QR tracks a practitioners endorsed CPD (in unison with our CPD accreditation partner Lifetime Awarding) and other key signals to help members of the public and employers to come to a more informed decision about the professional they intend to use. This is an enormously valuable signal that's increasingly important as business becomes more deregulated and privatised.
For practitioners QR helps you market your skills, endorsements and feedback transparently to a wider audience. It also gives you access to a community to help make you more able to progress and succeed in your field of expertise. Click here to find out more.
UNIT ZERO - BASIC MATHS
Unit Objectives
Section 1. Numbers
1. Our Number System
2. The Number Line
3. Counting Numbers
4. Parts of Counting Numbers
5. Rounding
6. Comparisons of Numbers
7. Percentages
8. Summary
Section 2 Algebra
1. Letters for Numbers
2. Indices, Powers and Exponents
3. Basic Rules for Products Involving Indices
4. Sequence of Operations
5. Directed (or Signed) Numbers
6. Manipulating Arithmetic Fractions
7. Multiplication and Division in Algebra
8. Use of Brackets
9. Summary
Section 3 Equations and Coeffecicients
1. Simple Equations
2. Changing the Subject of a Formula
3. Simultaneous Equations
4. Expanding Brackets
5. Coefficients
6. Inequalities
7. Summary
Section 4 Using a Calculator
1. Basic Calculations
2. The Square Root Function
3. The Square Function
4. The General Power Function
5. The Factorial Function
6. The Scientific Notation
7. Use of the Memory Facility
8.Combination of Functions
9. Summary
UNIT ONE - BUSINESS MATHS
Section 1 Financial Analysis
1. Interest Rates
2. Number Patterns
2.1 Pattern 1. Arithmetric Progressions
2.2 Pattern 2. Geometric Progressions
2.3 Pattern 3. Summing the Terms of a Geometric Progression
3. Calculation of Interest Earned
4. Notation
5. Compound Interest
6. A More General Approach to Interest
7. Applying the General Result to Particular Cases
8. Comparison of Interest Rates
9. Present Value of a Future Sum
10. Financially Equivalent Sums
11.Summary
12. Repaying a Loan
12.1 Calculating the Amount of a Loan
12.2 Calculating the Repayments
12.3 Loan Repayment Schedules
12.4 Mortgages
12.5 Annuities
13. Investment Decisions
14. Regular Savings Seach Schemes
14.1 Calculating the Value of a Scheme
14.2 Calculating the Sum to Achieve a Given Valuation
15. Summary
Section 2 Graphs
1. The Grid System
2. Straight Line Graphs
2.1 Straight Lines From an Equation
2.2 Slope of a Line
2.3 The Equation of a Straight Line
2.4 Detailed Inspection of the Equation
2.5 Other Forms of the Equation
2.6 Comparative Slopes
2.7 Summary
3. Other Graphs
4. Summary
UNIT TWO - DATA PRESENTATION
Section 1 Data, Variables and Measurement
1. What is Statistics
2. Measurement
3. Elements, Variables and Data
4. Summary
Section 2 The Frequency Distribution
1. Data, Frequencies Classes and Distributions
2. Frequency Distributions and Variability
3. How to Construct a Frequency Distribution
4. Frequency Distributions and Diagrams
5. Note for Computer Users
6. Types of Distribution
7. Summary
Section 3 Measures of Central Tendency
1. Introduction: Summary Statistics
2. What Are Measures of Central Tendency?
3. The Arithmetic Mean
4. The Mean of a Grouped Frequency Distribution
5. The Median
6. Calculating The Median for Grouped Data
7. The Mode
8. Summary
Section 4 Understanding Averages
1. What Kind of Variable are we Dealing With?
2. How Representative is the Measure of Central Tendency?
3. How are the Data Distributed?
4. Quick Estimate or Thorough One?
5. Do We Plan to Carry Out Further Analysis?
6. Summary
Section 5 Measures of Dispersion or Variation
1. Why Measure Variability?
2. How Can We Measure Variability?
3. Calculating s2 and s For Frequency Data
4. Summary
Section 6 Interpreting the Variance and Standard Deviation
1. Why Do we need the Standard Deviation?
2. Comparing Two or More Sets of Data
3. Chebyshev's Theorem
4. Symmetrical or Bell-Shaped Distributions
5. Z-Scores (Standard Scores)
6. Summary
References
Glossary
UNIT THREE - LINEAR PROGRAMMING
Unit Objectives
Section 1: A Case Study
1. Introduction: A Linear Programming Problem
2. What Does Linear Programming Do?
3. Applications of Linear Programming
4. Jarvis & Partners: Developing a Model
5. Summary
Section 2: Developing a Linear Programming Model
1. Introduction
2. Linear Equations and Linear Inequalities
3. More Case Studies
4. Summary
Section 3: Solving Linear Programming Problems Using Graphical Methods
1. Introduction
2. Jarvis Builders Revisited
3. Summary
Section 4: Practising the Graphical Solution Technique
1. Introduction
2. Superb Audio plc: a Graphical Solution
3. The Small Mine: a Graphical Solution
4. Rest Easy Lounge Suites: a Graphical Solution
5. The Doll's Workshop: a Graphical Solution
6. Summary
Section 5: Shadow Prices and Sensitivity Analysis
1.Introduction
2. Scarce Resources and Shadow Prices
3. Calculating Shadow prices
4. Shadow Prices and Sensitivity
5. Summary
Section 6: The Role of Computer Software
1. Introduction
2. The Simplex Algorithm
3. Simplex Linear Programming - Sample Output
4. More Than Two Decision Variables
5. Limitations of Linear Programming
6. Summary
References
Glossary
UNIT FOUR - REGRESSION AND CORRELATION
Unit Objectives
Section 1. Relationships Between Variables
1. What is Correlation and Regression?
2. Relationships Between Variables
3. Summary
Section 2. The Coefficient of Correlation
1. Correlation Between Variables
2.The Coefficient of Correlation
3.Interpretation of the Coefficient of Correlation
4. Summary
Section 3. Interpreting the Coefficient of Correlation
1. Linearity
2. Causality
3. Trends
4. Sample Size and Accuracy
5. Non-Ratio Scale Data
6. Summary
Section 4. Regression Analysis
1. What is Regression Analysis
2. The Purpose of Regression
3. Finding the Form of the Regression Equation
4. Best Fit - The Least Squares Method
5. Statistical and Spreadsheet Packages
6. Dependent and Independent Variables
7. Summary
Section 5. How Effective is Regression Analysis?
1. What We Have Accomplished So Far
2. Interpretation of the Coefficient of Determination
3. Summary
Section 6. What Next?
1 Where Do We Go From Here?
2. Correlation, Regression and Non-Ratio Scale Data
3. Dealing With a Time Series
4. What Can We Do About Non-Linearity?
5. Must We Always Be Limited to Two Variables?
6. Summary
References
Glossary
UNIT FIVE - PROBABILITY
Unit Objectives
Section 1. The Problem of Measurement
1. Introduction
2. What is Probability
3. Percentages and Odds
4. Summary
Section 2. Events, contingency Tables and Venn
Diagrams
1. Simple and Complex Events
2. Probability Notation and Venn Diagrams
3. Summary
Section 3. Rules for Calculating Probabilities
1. Introduction
2. Conditional Probabilities
3. The Intersection of Events
4. The Union of Events
5. Complementary Events
6. Summary
Section 4. Putting the Rules of Probability to Work
1. Introduction
2. Summary
Section 5. Probability Trees
1. Introduction
2. Probability Trees in Use
3. Summary
Section 6. Counting Techniques
1. Introduction
2. The 'MN' Rule
3. The Permutations Rule
4. The Combinations Rule
5. Summary
References
Glossary
UNIT SIX - PROBABILITY DISTRIBUTION
Unit Objectives
Section 1. Random Variables
1. Introduction
2. Discrete and Continuous Variables
3. Probability and Random Variables
4. Probability Distributions
5. What Do Probability Distributions Look Like?
6. Conclusion
7. Summary
Section 2. Mathematical Functions of Probability Distributions
1. Introduction
2. Functions and Discrete Data
3. Functions and Continuous Data
4. Conclusion
5. Summary
Section 3. Summing Up Probability Distributions
1. Introduction
2. Expected Value For a Discrete Variable
3. The Variance of a Discrete Random Variable
4. Expected Value and Variance: Continuous Variables
5. Interpretation of Expected Value and Variance
6. Summary
Section 4. Binomial Distribution
1. Introduction: A Matter of Insurance
2. The General Form of the Binomial Distribution
3. Expected Value and Variance of a Binomial Distribution
4. Summary
Section 5. Normal Distribution
1. Introduction
2. Characteristics of the Normal Distribution
3. Mean and Standard Deviation of the Normal Distribution
4. Probability and Areas Under the Normal Curve
5. The Standard Normal Distribution
6. Summary
Section 6. Using the Normal Distribution
1. Non-Standard Normal Distributions
2. The Normal Curve and Binomial Probabilities
3. Summary
4. Unit Summary and Conclusions
References
Glossary
Tables of Areas Under the Standard Normal Curve
UNIT SEVEN - INTRODUCTION TO SAMPLING
Unit Objectives
Section 1 Sample and Population
1. Introduction
2. Random Sampling
3. Selecting a Random Sample
4. Can We Trust Sample Estimates?
5. Summary
Section 2 The Behaviour of Sample Estimates
1. Terminology
2. The Central Limit Theorem
3. Summary
Section 3 Point Estimates and Interval Estimates
1. Problems in Estimation
2. Calculation of Interval Estimates
3. Interpretation of Confidence Intervals
4. How Precise Are Interval Estimates?
5. How Precise Are Sample Estimates?
6. Summary
Section 4 Proportions and Percentages
1. Introduction
2. Sampling Distribution of Proportions
3. Confidence Intervals for Proportions
4. Summary
Section 5 Sample Size Problems
1. Introduction
2. Deciding Sample Size: The Method
3. Proportions and Sample Size Problems
4. Summary
Section 6 Significance Testing
1. Introduction
2. Construction of Statistical Tests
3. Possible Mistakes in Statistical Tests
4. Notes Concerning the Use of Sampling Theory
5. Summary
References
Glossary
Table of Areas Under the Standard Normal Curve
UNIT EIGHT - INDEX NUMBERS
Unit Objectives
Section 1 The Measurement of Change
1. Why Measure Change?
2. Simple Index Numbers
3. More Complex Index Numbers
4. The Price Relative Method
5. Summary
Section 2 Weighted Indices and Aggregative Methods of Calculation
1. Weighted Index Numbers
2. Selecting Suitable Weights
3. The Aggregative Method of Calculation
4. Summary
Section 3 Quantity and Value Indices
1. Value and Volume Indices
2. Using a Value Index as a Deflater
3. The UK Retail Prices Index
4. Summary
Section 4 Time Series
1. Introduction
2. A Sales Data Time Series
3. Breaking Down a Time Series
4. Models of Time Series Data
5. Summary
Section 5 Moving Averages
1. Introduction
2. Moving Averages
3. Seasonal Variation
4. Summary
Section 6 Forecasting a Time Series
1. Using Regression Analysis on a Time Series
2. The Multiplicative Model
3. Summary
References
Glossary
What's Included
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