MAT 211

ASU

Course Overview

Lessons & Practice

I. Welcome

1. Review: Systems of Linear Equations [4.1]

2hr

1.1.1. Solving Graphically7 min

1.1.2. Example2 min

1.1.3. Practice Level 12 min

1.1.4. Practice Level 22 min

1.1.5. Practice Level 22 min

1.2.1. Solving by Substitution12 min

1.2.2. Example5 min

1.2.3. Practice Level 12 min

1.2.4. Practice Level 25 min

1.2.5. Practice Level 37 min

1.3.1. Solving by Simple Elimination12 min

1.3.2. Example3 min

1.3.3. Practice Level 13 min

1.3.4. Practice Level 22 min

1.3.5. Practice Level 24 min

1.4.1. Solving by Multiplication before Elimination8 min

1.4.2. Example4 min

1.4.3. Example4 min

1.4.4. Practice Level 14 min

1.4.5. Practice Level 24 min

1.4.6. Practice5 min

1.5.1. No Solution or Many Solutions5 min

1.5.2. Example 1 7 min

1.5.3. Example 2 4 min

1.5.4. Practice4 min

2. Linear Programming [6.1, 6.2]

1.2hr

2.1.1. Solving Linear Inequalities in Two Variables5 min

2.1.2. Example5 min

2.1.3. Practice Level 13 min

2.1.4. Practice Level 36 min

2.2.1. Systems of Linear Inequalities4 min

2.2.2. Example9 min

2.2.3. Practice3 min

2.2.4. Practice5 min

2.3.1. Optimizing a Function 3 min

2.3.2. Example: Setting Up LPP4 min

2.3.3. Example: Solving a Linear Programming Problem6 min

2.3.4. Practice: Solving a Linear Programming Problem2 min

2.3.5. Isoprofit Lines3 min

2.3.6. Example: LPP (with Isoprofit Lines)12 min

2.3.7. Practice: LPP (Use Isoprofit Lines)5 min

3. Functions of Several Variables, Partial Derivatives, Max & Min [15.1, 15.2, 15.3, 15.4]

31min

3.1.1. Functions of Several Variables5 min

3.1.2. Practice3 min

3.2.1. Partial derivatives6 min

3.2.2. Practice: First Partial derivatives2 min

3.2.3. Practice: Second Partial Derivatives3 min

3.3.1. Local Max & Min4 min

3.3.2. Absolute Max & Min5 min

3.3.3. Lagrange Multipliers3 min

3.3.4. Practice: Lagrange Multipliers

3.3.5. Practice: Lagrange Multipliers

3.4.1. Multivariable Functions Quiz7 Qns

4. Matrices [5.1, 5.2]

37min

4.1.1. Basics of Matrices3 min

4.1.2. Matrix Arithmetic3 min

4.1.3. Example1 min

4.1.4. Practice2 min

4.2.1. Matrix Multiplication6 min

4.2.2. Example1 min

4.2.3. Example4 min

4.2.4. Practice3 min

4.2.5. Practice2 min

4.2.6. Practice

4.3.1. Matrix Transpose3 min

4.3.2. Symmetric Matrices3 min

4.3.3. Example3 min

4.3.4. Practice60 sec

4.3.5. Practice2 min

7. Sets & Counting [7.1, 7.2, 7.3, 7.4]

47min

7.1.1. Basics of Sets7 min

7.1.2. Example: Basics of Sets5 min

7.1.3. Example: Elements in a Set5 min

7.1.4. Practice: Elements in a Set5 min

7.2.1. Fundamental Counting Principle6 min

7.2.2. Permutations and Combinations6 min

7.2.3. Practice1 min

7.2.4. Practice54 sec

7.2.5. More Combination Rules3 min

7.2.6. Example4 min

7.2.7. Practice3 min

7.2.8. Practice

8. Probability [8.1, 8.2, 8.3, 8.4, 8.5, 8.6]

1.9hr

8.1.1. Events & Sample Spaces6 min

8.1.2. Types of Probability6 min

8.1.3. Probability Language & Notation4 min

8.1.4. Example3 min

8.1.5. Independent vs. Mutually Exclusive Events4 min

8.1.6. Example2 min

8.1.7. Practice

8.2.1. Venn Diagrams3 min

8.2.2. Tree Diagrams4 min

8.2.3. Two-way Table5 min

8.3.1. Interpretation of Probability2 min

8.3.2. Complement Rule3 min

8.3.3. Practice1 min

8.3.4. The Addition Rule4 min

8.4.1. Probability of Independent Events4 min

8.4.2. Example3 min

8.4.3. Practice2 min

8.5.1. Probability of Dependent Events (Conditional Probability)7 min

8.5.2. Conditional Probability: Tree Diagram5 min

8.5.3. Bayes’ Theorem4 min

8.5.4. Example4 min

8.5.5. Practice3 min

8.6.1. Example6 min

8.6.2. Example7 min

8.6.3. Example7 min

8.6.4. Practice4 min

8.6.5. Practice5 min

8.7.1. Counting and Probability3 min

8.7.3. Practice1 min

9. Measures of Central Tendency & Dispersion [9.3, 9.4]

49min

9.1.1. Measures of Central Tendency7 min

9.1.2. Mean and Median Example2 min

9.1.3. Practice

9.1.4. Practice2 min

9.1.5. Practice3 min

9.2.1. Skewness and Symmetry 6 min

9.2.2. Practice

9.3.1. Percentiles4 min

9.3.2. Example2 min

9.3.3. Practice

9.4.1. Range and Interquartile Range11 min

9.4.2. Variance and Standard Deviation6 min

9.4.3. Practice3 min

9.4.4. Practice3 min

9.4.5. Practice

10. Discrete Random Variables & Binomial Distribution [9.1, 9.2]

2.5hr

10.1.1. Discrete Random Variables8 min

10.1.2. Practice

10.1.3. Probability Distribution Function4 min

10.1.4. Expected Value, Variance, and Standard Deviation of Discrete Random Variables6 min

10.1.5. Example5 min

10.1.6. Example4 min

10.1.7. Practice6 min

10.1.8. Practice4 min

10.1.9. Practice

10.2.1. Transforming Data: Shifting & Scaling11 min

10.2.2. Practice2 min

10.2.3. Linear Transformation3 min

10.2.4. Example4 min

10.2.5. Practice2 min

10.3.1. Cumulative Distribution Function (CDF)3 min

10.3.2. Example6 min

10.3.3. Practice2 min

10.4.1. Combining Random Variables7 min

10.4.2. Example2 min

10.4.3. Practice2 min

10.4.4. Example3 min

10.4.5. Practice

10.4.6. Functions of Two Independent Random Variables X and Y5 min

10.4.7. Example3 min

10.4.8. Practice

10.4.9. Practice2 min

10.5.1. Conditional Probability & Joint Distribution7 min

10.5.2. Example5 min

10.5.3. Practice

10.5.4. Random Variables & Joint Distribution7 min

10.5.5. Example3 min

10.5.6. Practice

10.5.7. Example

10.5.8. Practice

10.6.1. Bernoulli Trials4 min

10.6.2. Practice4 min

10.6.3. Binomial Experiment4 min

10.6.4. Binomial Distribution8 min

10.6.5. Example10 min

10.6.6. Example1 min

10.6.7. Practice6 min

10.6.8. Practice

11. Continuous Probability Distributions [9.5]

1.3hr

11.1.1. Continuous Probability Distribution Theory10 min

11.2.1. Normal Distribution11 min

11.2.2. Practice5 min

11.3.1. Standard Normal Distribution (Z-Scores)11 min

11.3.2. Standardizing a Normal Random Variable4 min

11.3.3. Example10 min

11.3.4. Practice6 min

11.3.5. Practice3 min

11.4.1. Normal Approximation to Binomial2 min

11.4.2. Practice5 min

11.4.3. Practice4 min

11.4.4. Practice4 min

11.5.1. Tchebysheff’s Theorem (Chebyshev's Theorem)3 min

11.5.2. Practice2 min

I Welcome

Free Activity

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