Finite Mathematics Videos

You can print these Notes and then use them to take notes from the videos. This will save you some writing!

The section numbers below are for the Mathematics for Business & Social Sciences book by Barnett, Ziegler, & Byleen. If your class is using a different book, just look at the titles instead of the section numbers.

Book Section Topic Video Title (ID Number, Presenter, Length)
3.1 Simple Interest Simple Interest (20161001, Jennifer Travis, 14:36)
Simple Interest (21251003, Andrea Spalding, 34:17)
3.2 Compound Interest Compound Interest (20161003, Jennifer Travis, 24:24)
Compound Interest (21251004, Andrea Spalding, 53:31)
Effective Rate (Annual Percentage Yield) (20161004, Jennifer Travis, 9:55)
3.3 Future Value of an Annuity and Sinking Funds Future Value of an Annuity and Sinking Funds (20161005, Jennifer Travis, 26:37)
Future Value of an Annuity and Sinking Funds (21251005, Andrea Spalding, 1:03:49)
3.4 Present Value of an Annuity and Amortization Present Value of an Annuity and Amortization (20161006, Jennifer Travis, 24:13)
Present Value of an Annuity and Amortization (21251006, Andrea Spalding, 1:04:31)
4.1 Systems of Linear Equations Systems of Linear Equations (21251007, Andrea Spalding, 40:46)
Systems of Linear Equations (20161007, Jennifer Travis, 18:23)
Applications of Linear Systems (20161008, Jennifer Travis, 16:03)
4.2 & 4.3 Matrices: The Gauss-Jordan Method Introduction to Matrices; Solving Systems Using Augmented Matrix Methods (21251008, Andrea Spalding, 1:02:09)
Augmented Matrices and Back Substitution (20161009, Jennifer Travis, 27:38)
Gaussian Elimination and Back Substitution: 3×3 Systems (20161010, Jennifer Travis, 21:45)
Gauss-Jordan Elimination (21251009, Andrea Spalding, 1:08:13)
The Gauss-Jordan Method (20161011, Jennifer Travis, 17:36)
More on Gauss-Jordan, Including Inconsistent Systems and Infinitely Many Solutions (20161012, Jennifer Travis, 19:17)
Solving Applied Problems using Augmented Matrix Methods (21251010, Andrea Spalding, 1:00:49)
Solutions to Augmented Matrices: Extra Examples (21251026, Andrea Spalding, 27:30)
4.4 Matrices: Addition, Subtraction, and Multiplication Addition, Subtraction, and Scalar Multiplication (20161013, Jennifer Travis, 7:23)
Basic Matrix Operations (21251011, Andrea Spalding, 52:42)
Matrix Multiplication (20161014, Jennifer Travis, 27:56)
4.5 The Identity Matrix and the Inverse of a Square Matrix The Identity Matrix and the Inverse of a Square Matrix (20161015, Jennifer Travis, 32:28)
Inverse of a Square matrix (21251012, Andrea Spalding, 31:33)
Inverses: Shortcut for 2×2 Matrices (20161016, Jennifer Travis, 7:17)
4.6 Matrix Equations Matrix Equations and Systems of Linear Equations (21251013, Andrea Spalding, 1:08:07)
Matrix Equations (20161017, Jennifer Travis, 29:18)
5.1 Linear Inequalities Linear Inequalities: Part 1 (20161065, Jennifer Travis, 7:19)
Linear Inequalities: Part 2 (20161066, Jennifer Travis, 17:06)
Linear Inequalities in Two Variables (21251014, Andrea Spalding, 18:16)
Graphing a Region in MyLab Math (21161017, Jennifer Travis, 9:12)
5.2 Systems of Linear Inequalities: Feasible Regions and Corner Points Systems of Linear Inequalities: Feasible Regions and Corner Points (20161019, Jennifer Travis, 23:14)
Systems of Linear Inequalities in Two Variables (21251015, Andrea Spalding, 58:55)
5.3 Linear Programming: The Graphical Method Linear Programming: The Graphical Method (20161067, Jennifer Travis, 40:43)
Applications of Linear Programming: Part 1 (20161022, Jennifer Travis, 16:13)
Applications of Linear Programming: Part 2 (20161023, Jennifer Travis, 24:36)
Applications of Linear Programming: Part 3 (20161024, Jennifer Travis, 14:31)

Linear Programming-Geometric Approach: Example 1 (21251016, Andrea Spalding, 14:58)
Example 1: Finding Intersections of Lines (21251017, Andrea Spalding, 4:19)
Example 1: Why it Works (21251018, Andrea Spalding, 3:51)
Linear Programming-Geometric Approach: Example 2 (21251019, Andrea Spalding, 8:20)
Example 2: Finding Intersections of Lines (21251020, Andrea Spalding, 2:20)

Example 2: Why it Works (21251021, Andrea Spalding, 3:14)
Linear Programming-Geometric Approach: Example 3 (21251022, Andrea Spalding, 10:22)
Example 3: Why it Works (21251023, Andrea Spalding, 1:52)
Linear Programming-Geometric Approach: Example 4 (21251024, Andrea Spalding, 12:43)
Linear Programming-Geometric Approach: Example 5 (21251025, Andrea Spalding, 14:55)

6.1 An Introduction to the Simplex Method Linear Programming: The Simplex Method – Part 1 (20161025, Jennifer Travis, 32:28)
6.2: The Simplex Method: Standard Maximization Problems Linear Programming: The Simplex Method – Part 2 (20161026, Jennifer Travis, 14:41)
Linear Programming: The Simplex Method – Part 3 (20161027, Jennifer Travis, 22:21)
Applications of the Simplex Method: Example 1 (20161028, Jennifer Travis, 16:18)
Applications of the Simplex Method: Example 2 (20161029, Jennifer Travis, 20:04)
Applications of the Simplex Method: Example 3 (20161030, Jennifer Travis, 16:14)
6.3 The Dual Method: Standard Minimization Problems Linear Programming: The Dual Method – Part 1 (20161031, Jennifer Travis, 19:02)
Linear Programming: The Dual Method – Part 2 (20161032, Jennifer Travis, 17:36)
Linear Programming: The Dual Method – Part 3 (2016033, Jennifer Travis, 24:09)
7.2 Sets and Venn Diagrams Introduction to Sets (20161034, Jennifer Travis, 11:34)
Set Operations (20161035, Jennifer Travis, 19:17)
Venn Diagrams and the Addition Principle (20161036, Jennifer Travis, 27:26)
Multiplication Principle (20161037, Jennifer Travis, 13:24)
7.3 Permutations and Combinations Permutations (20161038, Jennifer Travis, 7:32)
Combinations (20161039, Jennifer Travis, 9:42)
More on Permutations and Combinations (20161041, Jennifer Travis, 19:57)
8.1 Introduction to Probability Sample Spaces (20161042, Jennifer Travis, 19:07)
Introduction to Probability (20161043, Jennifer Travis, 18:44)
Probability Assignments and Empirical Probability (20161044, Jennifer Travis, 9:15)
More on Probability (20161045, Jennifer Travis, 18:55)
Probability involving Permutations and Combinations (20161046, Jennifer Travis, 28:58)
8.2 Probabilities of Unions, Intersections, and Complements Probabilities of Unions, Intersections, and Complements (20161047, Jennifer Travis, 12:41)
8.2 Odds Odds (20161048, Jennifer Travis, 13:39)
8.3 Conditional Probability Conditional Probability (20161049, Jennifer Travis, 13:36)
Probability Trees (20161050, Jennifer Travis, 25:58)
Independence of Events (20161051, Jennifer Travis, 24:40)
8.4 Bayes’ Formula Bayes’ Formula (20161052, Jennifer Travis, 12:07)
8.5 Expected Value Expected Value (20161053, Jennifer Travis, 22:41)
10.1 Frequency Distributions Frequency Distributions (20161054, Jennifer Travis, 19:27)
10.2 Measures of Central Tendency Measures of Central Tendency (20161055, Jennifer Travis, 20:35)
Median of Grouped Data (20161056, Jennifer Travis, 14:38)
10.3 Measures of Dispersion Measures of Dispersion (20161057, Jennifer Travis, 25:54)
10.3 Calculating Statistics on a TI Graphing Calculator Calculating Descriptive Statistics on a TI Graphing Calculator (20161058, Jennifer Travis, 11:14)
Grouped Data on a TI Graphing Calculator (20161059, Jennifer Travis, 12:59)
10.4 Binomial Probability Binomial Probability (20161060, Jennifer Travis, 27:59)
The Binomial Distribution (20161061, Jennifer Travis, 18:47)
10.5 The Normal Distribution The Normal Distribution (20161062, Jennifer Travis, 24:40)
More on the Normal Distribution (20161063, Jennifer Travis, 30:37)
The Normal Approximation to the Binomial (20161064, Jennifer Travis, 29:43)