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

Secondary Data Analysis: Sources and Elaboration

Secondary data analysis considers both qualitative and quantitative perspectives. It uses references like coding sheets in quantitative designs and identifies interviews or focus groups for qualitative data. Official statistics and public sources are used to verify field data.

Desrosieres argues that statistical indicators from official sources reflect social reality in institutions. These indicators are presented without analysis, as tables of results. This means the original data has been elaborated, which is typical of secondary sources. Secondary sources have developed, summarized, and synthesized the data. The degree of elaboration is the key difference between secondary data analysis and... Continue reading "Secondary Data Analysis: Sources, Types, and Elaboration" »

Hurwicz Criterion: Coefficient of Optimism

In this approach, a decision-maker may have an intermediate attitude. Since no probabilities are associated with each state of nature, this author suggests using a coefficient of optimism, simultaneously called C, and a coefficient of pessimism, (1 - C), where 0 ≤ C ≤ 1.

The coefficient C indicates the decision-maker's stance toward risk. When it is closer to 1, the decision-maker will be more optimistic, while closer to 0, the decision-maker is more pessimistic.

This criterion focuses only on the extreme scores of each alternative, weighing the result of maximum optimism with the coefficient C and the result of minimal value with the coefficient of pessimism (1 - C). The sum of these two products... Continue reading "Decision-Making Under Uncertainty: Criteria and Tables" »

Fundamental Data Mining Tasks and Concepts

Prediction: Forecasting Time Series Data

Prediction involves estimating the future value of a variable that is subject to random changes over time. This technique is strictly applied to time series, which are datasets whose domain is time.

Regression Analysis: Modeling Relationships

Regression is a generalization of classification (when the domain involves continuous classes) and prediction. The resulting model (classification or prediction) depends on the significance of the dependent and independent variables.

The goal is to find a mathematical or statistical model that properly relates the dependent variable with the independent variables. Geometrically, regression seeks a function that passes as close... Continue reading "Fundamental Data Mining Tasks and Algorithmic Approaches" »

The Concept of Materiality in Auditing

The auditor assesses the amount of errors or omissions that could affect the decisions of a reasonable user. This concept involves:

• Flexible guidelines.
• Both quantitative and qualitative elements.

Stages in the Application of Materiality

1. Establish a Preliminary Judgment on Materiality (Planning)

   This is the maximum amount of misstatement the auditor believes can exist in the financial statements without affecting the decisions of reasonable users. It is affected by the relative size of the company being audited.

   First, the auditor establishes a base (e.g., total assets, profits, turnover), which is multiplied by a percentage factor to determine an initial quantitative judgment. This initial judgment is then adjusted

Cutting speed depends on mining velocity. The cutter's speed must remain constant throughout its journey through the ore flow.

Sampling Methods

Sampling can be performed using manual or mechanical methods. Manual methods include cone and quartering, sampling with scoops or shovels, and using sampling tubes.

Manual Sampling Techniques

Cone and Quartering

1. Stack material into a conical pile by shoveling.
2. Form a cake or 'qeqe'.
3. Divide the cake into four equal fractions.
4. Take two opposite quarters as the sample; reject the other two.
5. If the sample size is adequate, combine the two retained quarters and reduce their size.
6. Repeat steps 1-4 as necessary until the desired sample size is achieved.

Sampling Scoop or Shovel

This procedure is used when ore is moved... Continue reading "Mining Ore Sampling Techniques and Procedures" »

Fundamental Concepts in Algebra

Numerical Language

Expressing mathematical information using only numbers.

Algebraic Language

Expressing mathematical information using both numbers and letters.

Algebraic Expression

A set of numbers and letters combined with mathematical operation signs.

Numerical Value of an Algebraic Expression

The number obtained by replacing the letters with their corresponding values and performing the indicated operations.

Monomial

A monomial consists of a number and one or more letters:

• The number (including its sign) is called the coefficient.
• The letter or letters that accompany it are called the literal part.

Degree of a Monomial

The sum of the exponents of the letters that form it.

Similar Monomials

Two or more monomials are similar... Continue reading "Fundamental Concepts of Algebra and Equations" »

Fortran 90: Implicit None and Vector Management

Implicit None

In Fortran 90, undeclared variables starting with I, J, K, L, or M are treated as integers, while others are treated as real numbers. However, it's best practice to avoid implicit declarations. Use IMPLICIT NONE right after the program instructions to disable implicit typing. This forces you to explicitly declare all variables, preventing potential errors due to character misuse.

Vector Management Forms

Ordering one-dimensional arrays (vectors) is a common operation. Here are three simple methods: selection, insertion, and bubble sort.

In all cases, we'll assume a one-dimensional array of N elements.

Selection Sort

1. Find the smallest element in the vector.
2. Swap it with the element in the

Joint Cost Allocation Methods

Joint costs are costs of a production process that yields multiple products (co-products). Here's a breakdown of common allocation methods:

Criterion: Market Value

This method allocates joint costs based on the relative sales value of each co-product. Here's how it works:

1. Calculate Total Sales Value:
   • List each co-product (e.g., A, B, C).
   • Determine the sales value per unit (e.g., per kg).
   • Determine the quantity of each co-product produced (e.g., in kg).
   • Multiply the sales value per unit by the quantity to find the total sales value for each co-product.
2. Distribute Total Joint Costs:
   • Calculate the total sales value of all co-products.
   • For each co-product, divide its total sales value by the total sales value of all co-products.

Fundamentals of Statistical Analysis

Frequency Distribution

Ungrouped Data

Ungrouped data are lists of observed values when the pooled data represent a mass of observed data.

Pooled Data

When data are grouped in a frequency distribution.

• Collect data and make a tally sheet

Determination of Range

The range is the difference between the highest observed value and the lowest value observed.

Determining the Range of Classes

The range of classes is the enactment between the midpoint of classes.

Determine the Midpoints of the Class

The midpoint of the class must include the minimum data value in that class.

Determine the Class Boundaries

The class limits are the extreme values or limits of a class and are called the upper limit and lower limit.

Identify the Frequency