Mathematics and Statistics for the Quantitative Sciences
portes grátis
Mathematics and Statistics for the Quantitative Sciences
Betti, Matthew
Taylor & Francis Ltd
12/2022
454
Dura
Inglês
9781032208145
15 a 20 dias
Descrição não disponível.
Section I. Applied Mathematics. The Plot (so you don't lose it). 1. Functions. 1.1. Anatomy of a Function. 1.2. Modeling With Mathematics. 1.3. Constants and Linear Functions. 1.4. Polynomials. 1.5. Exponentials And Logarithms. 1.6. Functions in Higher Dimensions. 1.7. Contour Diagrams. 1.8. Models In Two Dimensions. 1.9. Variables vs Parameters. 2. Derivatives. 2.1. The Tangent Line. 2.2. Approximating Derivatives of Functions. 2.3. Limits. 2.4. Limits And Derivates. 2.5. Derivative Formulas. 2.6. The Product Rule. 2.7. The Chain Rule. 2.8. Mixing Rules. 2.9. Critical Values. 2.10. Constrained Optimization. 2.11. Elasticity. 2.12. Partial Derivatives. 3. Linear Algebra. 3.1. Vectors. 3.2. Matrices. 3.3. Multiplication: Numbers and Matrices. 3.4. Multiplication: Matrix and Vectors. 3.5. Multiplication: Matrix and Matrix. 3.6. Leslie Matrices. 3.7. The Determinant. 3.8. Eigenvalues & Eigenvectors. 4. Derivatives in Multiple Dimensions. 4.1. Applications. 4.2. Distribution Fitting, Probability and Likelihood. 5. Differential Equations. 5.1. Solving Basic Differential Equations; With an Example. 5.2 Equilibria and Stability. 5.3 Equilibria and Linear Stability in Higher Dimensions. 5.4. The Jacobian. 6. Integration. 6.1. Accumulated Change. 6.2. The Fundamental Theorem of Calculus. 6.3. The Anti-Derivative. 6.4. Fundamental Theorem of Calculus Revisited. 6.5. Properties Of Integrals. 6.6. Integration by Parts. 6.7. Substitution. Section II. Applied Stats & Data Science. Some Context to Anchor Us. Math vs. the World. 7. Data and Summary Statistics. 7.1. What Is Data? 7.2. Data In Python. 7.3. Summary Statistics. 7.4. Ethical and Moral Considerations: Part 1. 7.5. Mean vs. Median vs. Mode. 7.6. Variance & Standard Deviation. 7.7. Ethical And Moral Considerations: Episode 2. 7.8. An Example. 7.9. The Empirical Rule. 8. Visualizing Data. 8.1. Plotting In Python. 8.2. Scatter Plots. 8.3. Outliers. 8.4. Correlation. 8.6. The Anatomy of a Technical Document. 8.7. Bad Plots and Why They're Bad. 9. Probability. 9.1. Ethical and Moral Considerations: A Very Special Episode. 9.2. Counting. 9.3. Permutations. 9.4. Combinations. 9.5. Combinations With Replacement. 9.6. Probability. 9.7. Properties of Probabilities. 9.8. More Notation. 9.9. Conditional Probability. 9.10. Bayes' Theorem. 9.11. The Prosecutor's Fallacy. 9.12. The Law of Total Probability. 10. Probability Distributions. 10.1. Discrete Probability Distributions. 10.2. The Binomial Distribution. 10.3. Trinomial Distribution. 10.4. Cumulative Probability Distributions. 10.5. Continuous Probability. 10.6. Continuous vs Discrete Probability Distributions. 10.7. Probability Density Functions. 10.8. The Normal Distribution. 10.9. Other Useful Distributions. 10.10. Mean, Median, Mode, Variance. 10.11. Summing To Infinity. 10.12. Probability and Python. 10.13. Practice Problems. 11. Fitting Data. 11.1. Defining Relationships. 11.2. Data and Lines. 11.3. Distribution Fitting & Likelihood. 11.4. Dummy Variables. 11.5. Logistic Regression. 11.6. Logistic Regression in Python. 11.7. Iterated Logistic Regression. 11.8. Random Forest Classification. 11.9. Bootstrapping & Confidence Intervals. 11.10. T-Statistics. 11.11. The Dichotomous Nature of P-Values. A. A Crash Course in Python.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Applied Mathematics;Modeling With Mathematics;Functions;Variables;Parameters;Determinant;Discrete Probability Distributions;Logistic Regression;Python;Quantitative Sciences;everyday mathematics;statistics;data science;introduction to mathematics;Calculus;Linear Algebra;trantion to advanced mathematics;Data Set;Wo;Mathematical Expression;DNA Match;Leslie Matrix;Sample Space;Vice Versa;Gamma Distribution;Follow;Honey Bees;Venn Diagram;Conditional Probability;Modelling Drug Concentration;Numerical Discrete Data;Algebra;Dummy Variables;Shannon Diversity Index;Scatter Plot;Contour Plot;Bar Plot
Section I. Applied Mathematics. The Plot (so you don't lose it). 1. Functions. 1.1. Anatomy of a Function. 1.2. Modeling With Mathematics. 1.3. Constants and Linear Functions. 1.4. Polynomials. 1.5. Exponentials And Logarithms. 1.6. Functions in Higher Dimensions. 1.7. Contour Diagrams. 1.8. Models In Two Dimensions. 1.9. Variables vs Parameters. 2. Derivatives. 2.1. The Tangent Line. 2.2. Approximating Derivatives of Functions. 2.3. Limits. 2.4. Limits And Derivates. 2.5. Derivative Formulas. 2.6. The Product Rule. 2.7. The Chain Rule. 2.8. Mixing Rules. 2.9. Critical Values. 2.10. Constrained Optimization. 2.11. Elasticity. 2.12. Partial Derivatives. 3. Linear Algebra. 3.1. Vectors. 3.2. Matrices. 3.3. Multiplication: Numbers and Matrices. 3.4. Multiplication: Matrix and Vectors. 3.5. Multiplication: Matrix and Matrix. 3.6. Leslie Matrices. 3.7. The Determinant. 3.8. Eigenvalues & Eigenvectors. 4. Derivatives in Multiple Dimensions. 4.1. Applications. 4.2. Distribution Fitting, Probability and Likelihood. 5. Differential Equations. 5.1. Solving Basic Differential Equations; With an Example. 5.2 Equilibria and Stability. 5.3 Equilibria and Linear Stability in Higher Dimensions. 5.4. The Jacobian. 6. Integration. 6.1. Accumulated Change. 6.2. The Fundamental Theorem of Calculus. 6.3. The Anti-Derivative. 6.4. Fundamental Theorem of Calculus Revisited. 6.5. Properties Of Integrals. 6.6. Integration by Parts. 6.7. Substitution. Section II. Applied Stats & Data Science. Some Context to Anchor Us. Math vs. the World. 7. Data and Summary Statistics. 7.1. What Is Data? 7.2. Data In Python. 7.3. Summary Statistics. 7.4. Ethical and Moral Considerations: Part 1. 7.5. Mean vs. Median vs. Mode. 7.6. Variance & Standard Deviation. 7.7. Ethical And Moral Considerations: Episode 2. 7.8. An Example. 7.9. The Empirical Rule. 8. Visualizing Data. 8.1. Plotting In Python. 8.2. Scatter Plots. 8.3. Outliers. 8.4. Correlation. 8.6. The Anatomy of a Technical Document. 8.7. Bad Plots and Why They're Bad. 9. Probability. 9.1. Ethical and Moral Considerations: A Very Special Episode. 9.2. Counting. 9.3. Permutations. 9.4. Combinations. 9.5. Combinations With Replacement. 9.6. Probability. 9.7. Properties of Probabilities. 9.8. More Notation. 9.9. Conditional Probability. 9.10. Bayes' Theorem. 9.11. The Prosecutor's Fallacy. 9.12. The Law of Total Probability. 10. Probability Distributions. 10.1. Discrete Probability Distributions. 10.2. The Binomial Distribution. 10.3. Trinomial Distribution. 10.4. Cumulative Probability Distributions. 10.5. Continuous Probability. 10.6. Continuous vs Discrete Probability Distributions. 10.7. Probability Density Functions. 10.8. The Normal Distribution. 10.9. Other Useful Distributions. 10.10. Mean, Median, Mode, Variance. 10.11. Summing To Infinity. 10.12. Probability and Python. 10.13. Practice Problems. 11. Fitting Data. 11.1. Defining Relationships. 11.2. Data and Lines. 11.3. Distribution Fitting & Likelihood. 11.4. Dummy Variables. 11.5. Logistic Regression. 11.6. Logistic Regression in Python. 11.7. Iterated Logistic Regression. 11.8. Random Forest Classification. 11.9. Bootstrapping & Confidence Intervals. 11.10. T-Statistics. 11.11. The Dichotomous Nature of P-Values. A. A Crash Course in Python.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Applied Mathematics;Modeling With Mathematics;Functions;Variables;Parameters;Determinant;Discrete Probability Distributions;Logistic Regression;Python;Quantitative Sciences;everyday mathematics;statistics;data science;introduction to mathematics;Calculus;Linear Algebra;trantion to advanced mathematics;Data Set;Wo;Mathematical Expression;DNA Match;Leslie Matrix;Sample Space;Vice Versa;Gamma Distribution;Follow;Honey Bees;Venn Diagram;Conditional Probability;Modelling Drug Concentration;Numerical Discrete Data;Algebra;Dummy Variables;Shannon Diversity Index;Scatter Plot;Contour Plot;Bar Plot
