Introduction to Algebra (2021)

This post is about an Introduction to Algebra.

The basics of algebra include numbers, variables, constants, expressions, equations, linear equations, and quadratic equations. Further, it involves the basic arithmetic operations of addition, subtraction, multiplication, and division within the algebraic expressions.

Introduction to Algebra

We work with numbers in arithmetic, while in algebra we use numbers as well as Alphabets such as $A, B, C, a, b$, and $c$ for any numerical values we choose. We can say that algebra is an extension of arithmetic. For example, the arithmetic sum of two numbers $5+3=8$ means that the sum of numbers 5 and 3 is 8. In algebra, two numbers can be summed by the expression $x+y=z$ which is the general form that can be used to add any two numbers. For example, if $x=5$ and $y=3$ then $x+y$ will be equivalent to the left-hand side ($5+3$) and the summation of these numbers will be equivalent to the right-hand side $z$ which is 8.

In algebra all arithmetic operators such as $+, -, \times, =$ and $\div$, etc., can be used used.

For example, $x-y=z$ means that the difference between two numbers is equal to the number represented by the letter z. In algebra, many other notations used are the same as in arithmetic. For example,

$c=a\times b$ means that the product of two numbers represented by $a$ and $b$ is equal to the number $c$.

$x \times x \times x$ can be written as $a^4$.

Introduction to Algebra

From the above discussion, note that letters of the alphabet represent variables, and arithmetic operators (+, -, etc) represent the mathematical operations on a variable. The combination of numbers and letters of the alphabet is called an algebraic expression. For example, $8x + 7y$, $x+y$, and $7x^2+2xy-5y^2$ etc. are examples of expression.

Some important points to remember:

  • Algebra is like a toolbox for solving mathematics problems with unknowns. Instead of using specific numbers, we use letters like $x$, $y$, and $z$ to represent unknown values. These letters are called variables.
  • A variable is a quantity (usually denoted by letters of the alphabet) in algebraic expressions and equations, that changes from place to place, person, to person, and/or time to time. The variable can have any one of a range of possible values.
  • A factor that multiplies with a variable. For example, in $2x^3+3x=0$, $x$ is a variable, 2 is the coefficient of $x^3$, and 3 is the coefficient of $x$.

We learning algebra the following concepts are very important to understand the concepts used in algebra:

  • Variables are the building blocks, representing unknown numbers.
  • Expressions are combinations of variables, numbers, and mathematical operations (such as +, -, *, /) that do not necessarily have an equal sign (=).
  • Equations are statements (or expressions) with an equal sign that shows two expressions are equivalent. The equations are solved to find the value of the variable.
  • Inequalities are statements (or expressions) having “greater than” (>), “less than” (<), or “not equal to” (≠) symbols for making comparisons between expressions.

Real Life Applications of Algebra

  • Finances and Budgeting: Algebra helps you create formulas to track income, expenses, savings goals, and loan payments. You can set up equations to see how much extra money you’d have if you cut certain expenses or how long it’ll take to save for a down payment on a house.
  • Mixing and Ratios: Whether you’re baking a cake or mixing paint colors, algebra helps you determine the correct ratios of ingredients to achieve the desired outcome. You can set up proportions to find out how much water to add to a paint concentrate or how much flour you need to double a recipe.
  • Motion and Physics: From calculating travel time based on speed and distance to understanding the trajectory of a thrown ball, algebra forms the foundation for many physics concepts. You can use formulas to figure out how long it’ll take to drive somewhere at a certain speed or the angle needed to throw a basketball into the hoop.
  • DIY Projects and Home Improvement: From measuring lumber for a bookshelf to calculating the amount of paint needed for a room, algebra helps with planning and executing home improvement tasks. You can use formulas to find the area of a wall to determine how much paint to buy or calculate the volume of wood needed for a project.
  • Scientific Research and Data Analysis: Algebra is the backbone of many scientific formulas and equations used in research. It helps analyze data, identify trends, and make predictions.
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Levels of Measurement (2021): A Comprehensive Tutorial

Levels of Measurement (Scale of Measure)

The levels of measurement (scale of measures) have been classified into four categories. It is important to understand these measurement levels since they play an important part in determining the arithmetic and different possible statistical tests carried on the data. The scale of measure is a classification that describes the nature of the information within the number assigned to a variable. In simple words, the level of measurement determines how data should be summarized and presented.

It also indicates the type of statistical analysis that can be performed. The four-level of measurements are described below:

Nominal Level of Measurement (Nominal Scale)

At the nominal level of measurement, the numbers are used to classify the data (unordered group) into mutually exclusive categories. In other words, for the nominal level of measurement, observations of a qualitative variable are measured and recorded as labels or names.

Ordinal Level of Measurement (Ordinal Scale)

In the ordinal level of measurement, the numbers are used to classify the data (ordered group) into mutually exclusive categories. However, it does not allow for a relative degree of difference between them. In other words, for the ordinal level of measurement, observations of a qualitative variable are either ranked or rated on a relative scale and recorded as labels or names.

Interval Level of Measurement (Interval Scale)

For data recorded at the interval level of measurement, the interval or the distance between values is meaningful. The interval scale is based on a scale with a known unit of measurement.

Ratio Level of Measurement (Ratio Scale)

Data recorded at the ratio level of measurement are based on a scale with a known unit of measurement and a meaningful interpretation of zero on the scale. Almost all quantitative variables are recorded on the ratio level of measurement.

Levels of Measurement

Examples of levels of measurement

Examples of Nominal Level of Measurement

  • Religion (Muslim, Hindu, Christian, Buddhist)
  • Race (Hispanic, African, Asian)
  • Language (Urdu, English, French, Punjabi, Arabic)
  • Gender (Male, Female)
  • Marital Status (Married, Single, Divorced)
  • Number plates on Cars/ Models of Cars (Toyota, Mehran)
  • Parts of Speech (Noun, Verb, Article, Pronoun)

Examples of Ordinal Level of Measurement

  • Rankings (1st, 2nd, 3rd)
  • Marks Grades (A, B, C, D)
  • Evaluations such as High, Medium, and Low
  • Educational level (Elementary School, High School, College, University)
  • Movie Ratings (1 star, 2 stars, 3 stars, 4 stars, 5 stars)
  • Pain Ratings (more, less, no)
  • Cancer Stages (Stage 1, Stage 2, Stage 3)
  • Hypertension Categories (Mild, Moderate, Severe)

Examples of Interval Levels of Measurement

  • Temperature with Celsius scale/ Fahrenheit scale
  • Level of happiness rated from 1 to 10
  • Education (in years)
  • Standardized tests of psychological, sociological, and educational discipline use interval scales.
  • SAT scores

Examples of Ratio Level of Measurement

  • Height
  • Weight
  • Age
  • Length
  • Volume
  • Number of home computers
  • Salary

In essence, levels of measurement act like a roadmap for statistical analysis. They guide us in selecting the most appropriate methods to extract valuable insights from the data under study. The level of measures is very important because they help us in

  • Choosing the right statistical tools: Different levels of measurement are used for different statistical methods. For example, One can compute a measure of central tendency (such as mean and median) for data on income (which is interval level), but a measure of central tendency (such as mean and median) cannot be computed for data on favorite color (which is nominal level, the mode can be computed regarding the measure of central tendency).
  • Drawing valid conclusions: If the statistical test is misused because of a misunderstanding of the measurement level of the data, the conclusions might be misleading or even nonsensical. Therefore, measurement levels help us ensure that analysis reflects the actual characteristics of the data.
  • Making meaningful comparisons: Levels of measurement also allow us to compare data points appropriately. For instance, one can say someone is 2 years older than another person (ordinal data), but one cannot say that their preference for chocolate ice cream is twice as strong (nominal data).
Levels of Measurement

FAQS About Levels of Measurements

  1. What do you mean by measurement levels?
  2. What is the role of Levels of Measurement in Statistics?
  3. Compare, nominal, ordinal, ratio, and interval scale.
  4. What measures of central tendency can be performed on which measurement level?
  5. What is the importance of measurement levels?
  6. Give at least five, five examples of each measurement level.

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Contingency Tables

Introduction to Contingency Tables

Contingency Tables also called cross tables or two-way frequency tables describe the relationship between several categorical (qualitative) variables. A bivariate relationship is defined by the joint distribution of the two associated random variables.

Contingency Tables

Let $X$ and $Y$ be two categorical response variables. Let variable $X$ have $I$ levels and variable $Y$ have $J$. The possible combinations of classifications for both variables are $I\times J$. The response $(X, Y)$ of a subject randomly chosen from some population has a probability distribution, which can be shown in a rectangular table having $I$ rows (for categories of $X$) and $J$ columns (for categories of $Y$).

The cells of this rectangular table represent the $IJ$ possible outcomes. Their probability (say $\pi_{ij}$) denotes the probability that ($X, Y$) falls in the cell in row $i$ and column $j$. When these cells contain frequency counts of outcomes, the table is called a contingency or cross-classification table and it is referred to as a $I$ by $J$ ($I \times J$) table.

Joint and Marginal Distribution

The probability distribution {$\pi_{ij}$} is the joint distribution of $X$ and $Y$. The marginal distributions are the rows and columns totals obtained by summing the joint probabilities. For the row variable ($X$) the marginal probability is denoted by $\pi_{i+}$ and for column variable ($Y$) it is denoted by $\pi_{+j}$, where the subscript “+” denotes the sum over the index it replaces; that is, $\pi_{i+}=\sum_j \pi_{ij}$ and $\pi_{+j}=\sum_i \pi_{ij}$ satisfying

$l\sum_{i} \pi_{i+} =\sum_{j} \pi_{+j} = \sum_i \sum_j \pi_{ij}=1$

Note that the marginal distributions are single-variable information, and do not pertain to association linkages between the variables.

Contingency Tables, Cross Tabulation

In (many) contingency tables, one variable (say, $Y$) is a response, and the other $X$) is an explanatory variable. When $X$ is fixed rather than random, the notation of a joint distribution for $X$ and $Y$ is no longer meaningful. However, for a fixed level of $X$, the variable $Y$ has a probability distribution. It is germane to study how this probability distribution of $Y$ changes as the level of $X$ changes.

Contingency Table Uses

  • Identify relationships between categorical variables.
  • See if one variable is independent of the other (i.e. if the frequency of one category is the same regardless of the other variable’s category).
  • Calculate probabilities of specific combinations occurring.
  • Often used as a stepping stone for further statistical analysis, like chi-square tests, to determine if the observed relationship between the variables is statistically significant.

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