Finite Math (SJDC M20) v2

by Jacquelynn Schwegel

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This book covers topics from the Finite Math course at San Joaquin Delta College including an introduction to set theory and combinatorics, basic probability, linear modeling, systems of linear equations (including matrices and applications), systems of linear inequalities, linear programming and math of finance.

Description from LibreTexts.

Publisher
San Joaquin Delta College
Subjects
Mathematics
Licence
CC BY-NC-SA 4.0, as LibreTexts states it. Non-commercial use only.
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. 1: All Things Sets and Probability
  7. 1.1: Sets, Samples Spaces and Probability
  8. 1.1.1: Sets and Counting
  9. 1.1.2: Sample Spaces and Probability
  10. 1.2: Venn Diagrams and Inclusion/Exclusion
  11. 1.2.1: Introduction to Venn Diagrams
  12. 1.2.2: Inclusion Exclusion Principle
  13. 1.2.3: Venn Diagrams and Applications
  14. 1.2.4: Mutually Exclusive Events and the Addition Rule
  15. 1.3: Tree Diagrams and Mutiplication Principle
  16. 1.3.1: Tree Diagrams and the Multiplication Axiom
  17. 1.3.2: Probability Using Tree Diagrams and Combinations
  18. 1.3.3: Probability Using Tree Diagrams
  19. 1.4: Conditional Probability and Bayes' Theorem
  20. 1.4.1: Conditional Probability
  21. 1.4.2: Independent Events
  22. 1.4.3: Bayes' Formula
  23. 1.5: Combinatorics and Probability
  24. 1.5.1: Combinations
  25. 1.5.2: Permutations
  26. 1.5.3: Combinations- Involving Several Sets
  27. 1.6: Chapter 1 Practice Problems
  28. 1.6.1: Practice Sets and Counting
  29. 1.6.2: Practice Sample Spaces and Probability
  30. 1.6.3: Practice Mutually Exclusive Events and the Addition Rule
  31. 1.6.4: Practice Tree Diagrams and the Multiplication Principle
  32. 1.6.5: Practice Probability Using Tree Diagrams
  33. 1.6.6: Practice Conditional Probability
  34. 1.6.7: Practice Probability of Independent Events
  35. 1.6.8: Practice Bayes' Formula
  36. 1.6.9: Practice Combinations
  37. 1.6.10: Practice Combinations Involving Multiple Sets
  38. 1.6.11: Practice Probability Using Tree Diagrams and Combinations
  39. 1.6.12: Chapter Review Part 1
  40. 1.6.13: Chapter Review Part 2
  41. 2: All Things Linear Equation
  42. 2.1: Solving Linear Equations in One Variable
  43. 2.1.1: Solve Equations Using the Subtraction and Addition Properties of Equality
  44. 2.1.2: Solve Equations using the Division and Multiplication Properties of Equality
  45. 2.1.3: Solve Equations with Variables and Constants on Both Sides
  46. 2.1.4: Use a General Strategy to Solve Linear Equations
  47. 2.1.5: Solve Equations with Fractions or Decimals
  48. 2.2: Linear Equations in Two Variables
  49. 2.2.1: Graphing a Linear Equation
  50. 2.2.2: Slope of a Line
  51. 2.2.3: Determining the Equation of a Line
  52. 2.3: Systems of Linear Equations - Two Variables
  53. 2.4: Matrices
  54. 2.4.1: Introduction to Matrices
  55. 2.4.2: Systems of Linear Equations and the Gauss-Jordan Method
  56. 2.4.3: Systems of Linear Equations – Special Cases
  57. 2.4.4: Inverse Matrices
  58. 2.4.5: Application of Matrices in Cryptography
  59. 2.4.6: Applications – Leontief Models
  60. 2.5: Applications of Linear Systems
  61. 2.5.1: Linear Models
  62. 2.5.2: System Applications
  63. 2.5.3: More System Applications
  64. 2.6: Chapter 2 Practice Problems
  65. 2.6.1: Practice Solving Linear Equations in One Variable
  66. 2.6.2: Practice Linear Equations in Two Variables
  67. 2.6.3: Practice with Matrices
  68. 2.6.4: Practice the Gauss-Jordan Method
  69. 2.6.5: Practice Solving Systems of Linear Equations – Special Cases
  70. 2.6.6: Practice Inverse Matrix Method
  71. 3: All Things Linear Inequality
  72. 3.1: Graphing Systems of Linear Inequalities
  73. 3.2: Linear Programming - Method of Corners
  74. 3.2.1: Maximization Applications
  75. 3.2.2: Minimization Applications
  76. 3.3: Linear Programming - The Simplex Method
  77. 3.3.1: Introduction to Linear Programming Applications in Business, Finance, Medicine, and Social Science
  78. 3.3.2: Maximization By The Simplex Method
  79. 3.3.3: Minimization By The Simplex Method
  80. 3.4: Chapter 3 Practice Problems
  81. 3.4.1: Practice Graphing Inequalities
  82. 3.4.2: Practice Method of Corners (Max)
  83. 3.4.3: Practice Method of Corners (Min)
  84. 3.4.4: Practice Mixed
  85. 3.4.5: Practice (Simplex)
  86. 3.4.6: Practice Mixed Applications
  87. 4: All Things Math of Finance
  88. 4.1: Simple Interest/Linear Growth
  89. 4.2: Exponential and Logarithmic Functions
  90. 4.2.1: Prelude to Exponential and Logarithmic Functions
  91. 4.2.2: Exponential Growth and Decay Models
  92. 4.2.3: Graphs and Properties of Exponential Growth and Decay Functions
  93. 4.2.4: Logarithms and Logarithmic Functions
  94. 4.2.5: Graphs and Properties of Logarithmic Functions
  95. 4.3: Compound Interest/Exponential Growth
  96. 4.3.1: Introduction
  97. 4.3.2: Compound Interest
  98. 4.3.3: Comparing Simple and Compound Interest Summary
  99. 4.4: Annuities
  100. 4.4.1: Annuities and Sinking Funds
  101. 4.4.2: Present Value of an Annuity and Installment Payment
  102. 4.5: Amortization Schedules and More About Loans
  103. 4.6: Classification of Finance Problems
  104. 4.7: Chapter 4 Practice Problems
  105. 4.7.1: Practice Simple Interest
  106. 4.7.2: Practice with Compound Interest
  107. 4.7.3: Practice Annuities and Sinking Funds
  108. 4.7.4: Practice Present Value and Payments
  109. 4.7.5: Practice Amortization and Loans
  110. 4.7.6: Practice Classifying Math of Finance Problems
  111. 4.7.7: Chapter Review
  112. 5: Additional Content
  113. 5.1: Markov Chains
  114. 5.1.1: Introduction to Markov Chains
  115. 5.1.1.1: Introduction to Markov Chains (Exercises)
  116. 5.1.2: Applications of Markov Chains
  117. 5.1.2.1: Applications of Markov Chains (Exercises)
  118. 5.1.3: Regular Markov Chains
  119. 5.1.3.1: Regular Markov Chains (Exercises)
  120. 5.1.4: Absorbing Markov Chains
  121. 5.1.4.1: Absorbing Markov Chains (Exercises)
  122. 5.1.5: CHAPTER REVIEW
  123. 5.2: Game Theory
  124. 5.2.1: Strictly Determined Games
  125. 5.2.1.1: Strictly Determined Games (Exercises)
  126. 5.2.2: Non-Strictly Determined Games
  127. 5.2.2.1: Non-Strictly Determined Games (Exercises)
  128. 5.2.3: Reduction by Dominance
  129. 5.2.3.1: Reduction by Dominance (Exercises)
  130. 5.2.4: Chapter Review
  131. Back Matter
  132. Index
  133. Glossary
  134. Detailed Licensing
  135. Detailed Licensing