Notes, summaries, assignments, exams, and problems for Mathematics

This step-by-step analysis covers Problem Sets 6-9, emphasizing key concepts from Problem Sets 7 and 8, essential for your final exam.

Problem Set 6: Product Differentiation & Merger Impacts

1. Why Bertrand Does Not Equal Marginal Cost in Reality

• Firms may experience:

  • Capacity constraints

  • Brand loyalty (differentiated products)

  • Reputational concerns or switching costs

2. Bertrand Competition with Differentiated Products

• Demand:

  • Q_M = 1000 - 200P_M + 100P_B

  • Q_B = 1000 - 200P_B + 100P_M

• Steps:

  1. Plug in rival's price to derive inverse demand.

  2. Derive Marginal Revenue (MR); set MR = Marginal Cost (MC) = 4.

  3. Solve for the best response price.

  4. Set both best responses equal to solve for the Nash Equilibrium (NE).

  5. Calculate quantity, profit, and price-cost margin.

Chapter 2: Predetermined Overhead Rate

Predetermined Overhead Rate = Estimated Total Manufacturing Overhead (MOH) / Estimated Total MOH Driver (e.g., Direct Labor hours, Direct Labor costs, Machine Hours)

Prime Cost = Direct Materials + Direct Manufacturing Labor

Conversion Cost = Direct Manufacturing Labor + Indirect Manufacturing Overhead

Cost Accumulation: Data is collected in an organized way (also known as cost pools).

Cost Assignment: Systematically links an actual cost pool to a distinct cost object (e.g., Tires, engine, labor assigned to car cost).

Activity Base: Examples include kilometers driven in a car, units produced, units sold, machine hours.

Product Cost: Costs tied to creating a product (Direct Materials, Direct Labor, Manufacturing... Continue reading "Cost Accounting Essentials: Key Concepts and Calculations" »

Statistical Measures and Causal Inference Concepts

Measures of Dispersion and Relationship

Variance

Variance: Estimates how far a set of numbers (random) are spread out from their mean value.

Covariance

Covariance: The relationship between two variables.

• Cov = 0: Unsure of the relationship.
• Cov > 0: Suggests Y will be above average when X is above average.
• Cov < 0: Suggests Y will be below average when X is above average.

The formula for variance is often expressed as: $\mathbb{E}[X^2] - (\mathbb{E}[X])^2$ (where $\mathbb{E}$ is the Expected Value).

The formula for covariance between two variables $X$ and $Y$ is: $\mathbb{E}[(X - \mathbb{E}[X])(Y - \mathbb{E}[Y])]$

Pearson's Correlation Coefficient

Standardizes covariance between -1 and 1:

Pearson’s

Data Science, Machine Learning, and Artificial Intelligence

AdaBoost: Adaptive Boosting Algorithm Explained

AdaBoost (Adaptive Boosting) is a classic and widely used boosting algorithm that focuses on correcting the errors of preceding weak learners (typically decision trees). It works by iteratively adjusting the weights of the training data points.

How AdaBoost Works

1. Initial Weights: AdaBoost starts by assigning equal weights to all the training data points.
2. Train a Weak Learner: A "weak" learner (a model that performs slightly better than random chance, like a decision stump) is trained on the dataset using the current weights.
3. Calculate Error and Performance: The error rate of the weak learner is calculated based on the instances it misclassified. A measure of the weak learner's performance (often called

1. Understand the Problem

The first step to solving a two-step algebraic equation is to clearly write down the problem. This helps you visualize the solution process. For our example, we will work with the equation: -4x + 7 = 15.

2. Isolate the Variable Term Using Addition or Subtraction

The next step is to isolate the variable term (e.g., "-4x") on one side of the equation and the constants (whole numbers) on the other. To achieve this, you'll use the Additive Inverse. Find the opposite of the constant term on the same side as the variable. In our example, the constant is +7, so its additive inverse is -7.

Subtract 7 from both sides of the equation to cancel out the "+7" on the variable's side. Write "-7" below the 7 on the left side and below... Continue reading "Mastering Two-Step Algebraic Equations" »

Sampling Methods

• Simple Random Sampling: equal probability of selection —> good representation but may have non-response bias.
• Systematic Sampling: apply a selection interval k from a random starting point; equal probability of selection —> simple to implement but may give poor representation if there is a pattern in how subjects are ordered.
• Stratified Sampling: divide the sampling frame into strata; each stratum can have a different size; apply simple random sampling within each stratum; equal probability of selection —> good representation but requires information about the sampling frame and strata.
• Cluster Sampling: divide the sampling frame into clusters; select a fixed number of clusters using simple random sampling; equal

Essential Statistical & Numerical Concepts

This document covers fundamental questions and answers across various topics in statistics, numerical methods, and data analysis. Each section provides a concise explanation of key concepts.

1. What is Interpolation?

Interpolation is a mathematical technique used to estimate unknown values that fall between known data points. It involves creating a function or model based on the given data and using it to predict values within the range of the data, rather than extrapolating outside it. This method is commonly applied in fields like data analysis, engineering, and computer graphics for filling in missing data or smoothing curves.

2. Regula Falsi Method Formula for Finding Roots

The formula for finding... Continue reading "Core Concepts in Statistics and Numerical Analysis" »

Understanding the distribution of sample means is crucial in statistics. Let's analyze two distinct scenarios.

MLB Player Salaries in 2012

In 2012, there were 855 major league baseball players. The mean salary was \(\mu = 3.44\) million dollars, with a standard deviation of \(\sigma = 4.70\) million dollars. We will examine random samples of size \(n = 50\) players to understand the distribution of their mean salaries.

Distribution of Sample Means

To describe the shape, center, and spread of the distribution of sample means, we apply the Central Limit Theorem (CLT). The CLT states that for sufficiently large sample sizes (typically \(n \ge 30\)), the sampling distribution of the sample mean will be approximately normal, irrespective of the population... Continue reading "MLB Player Salaries & Dark Chocolate's Vascular Health Impact" »

Matrices (Question 6a)

Verify that A · (\text{adj } A) = (\text{adj } A) · A = |A| · I_3 for
A = \begin{bmatrix} 2 & 3 & 4 \\ 3 & 0 & 1 \\ 2 & 1 & 5 \end{bmatrix}.

Tasks:

• Find the determinant |A|:
• Find the Adjoint (\text{adj } A): This involves finding the cofactor of each element and then transposing the resulting matrix.
• Cofactors: C₁₁ = -1, C₁₂ = -13, C₁₃ = 3, C₂₁ = -11, C₂₂ = 2, C₂₃ = 4, C₃₁ = 3, C₃₂ = 10, C₃₃ = -9
• Multiply A · (\text{adj } A)

4. Financial Arithmetic (Question 2g)

Find the compound interest on Rs. 8,000 for 1 1/2 years at 10% per annum, compounded annually.

• Amount for the first year:
• Interest for the next half year: Use simple interest on the new principal.
• Total Compound Interest:

Answers use standard... Continue reading "Matrix Determinant and Adjoint Verification with AP/GP and CI" »