define variable presentation

1.1 Definitions of Statistics, Probability, and Key Terms

The science of statistics deals with the collection, analysis, interpretation, and presentation of data . We see and use data in our everyday lives.

Collaborative Exercise

In your classroom, try this exercise. Have class members write down the average time—in hours, to the nearest half-hour—they sleep per night. Your instructor will record the data. Then create a simple graph, called a dot plot, of the data. A dot plot consists of a number line and dots, or points, positioned above the number line. For example, consider the following data:

5, 5.5, 6, 6, 6, 6.5, 6.5, 6.5, 6.5, 7, 7, 8, 8, 9.

The dot plot for this data would be as follows:

Does your dot plot look the same as or different from the example? Why? If you did the same example in an English class with the same number of students, do you think the results would be the same? Why or why not?

Where do your data appear to cluster? How might you interpret the clustering?

The questions above ask you to analyze and interpret your data. With this example, you have begun your study of statistics.

In this course, you will learn how to organize and summarize data. Organizing and summarizing data is called descriptive statistics . Two ways to summarize data are by graphing and by using numbers, for example, finding an average. After you have studied probability and probability distributions, you will use formal methods for drawing conclusions from good data. The formal methods are called inferential statistics . Statistical inference uses probability to determine how confident we can be that our conclusions are correct.

Effective interpretation of data, or inference, is based on good procedures for producing data and thoughtful examination of the data. You will encounter what will seem to be too many mathematical formulas for interpreting data. The goal of statistics is not to perform numerous calculations using the formulas, but to gain an understanding of your data. The calculations can be done using a calculator or a computer. The understanding must come from you. If you can thoroughly grasp the basics of statistics, you can be more confident in the decisions you make in life.

Statistical Models

Statistics, like all other branches of mathematics, uses mathematical models to describe phenomena that occur in the real world. Some mathematical models are deterministic. These models can be used when one value is precisely determined from another value. Examples of deterministic models are the quadratic equations that describe the acceleration of a car from rest or the differential equations that describe the transfer of heat from a stove to a pot. These models are quite accurate and can be used to answer questions and make predictions with a high degree of precision. Space agencies, for example, use deterministic models to predict the exact amount of thrust that a rocket needs to break away from Earth’s gravity and achieve orbit.

However, life is not always precise. While scientists can predict to the minute the time that the sun will rise, they cannot say precisely where a hurricane will make landfall. Statistical models can be used to predict life’s more uncertain situations. These special forms of mathematical models or functions are based on the idea that one value affects another value. Some statistical models are mathematical functions that are more precise—one set of values can predict or determine another set of values. Or some statistical models are mathematical functions in which a set of values do not precisely determine other values. Statistical models are very useful because they can describe the probability or likelihood of an event occurring and provide alternative outcomes if the event does not occur. For example, weather forecasts are examples of statistical models. Meteorologists cannot predict tomorrow’s weather with certainty. However, they often use statistical models to tell you how likely it is to rain at any given time, and you can prepare yourself based on this probability.

Probability

Probability is a mathematical tool used to study randomness. It deals with the chance of an event occurring. For example, if you toss a fair coin four times, the outcomes may not be two heads and two tails. However, if you toss the same coin 4,000 times, the outcomes will be close to half heads and half tails. The expected theoretical probability of heads in any one toss is 1 2 1 2 or .5. Even though the outcomes of a few repetitions are uncertain, there is a regular pattern of outcomes when there are many repetitions. After reading about the English statistician Karl Pearson who tossed a coin 24,000 times with a result of 12,012 heads, one of the authors tossed a coin 2,000 times. The results were 996 heads. The fraction 996 2,000 996 2,000 is equal to .498 which is very close to .5, the expected probability.

The theory of probability began with the study of games of chance such as poker. Predictions take the form of probabilities. To predict the likelihood of an earthquake, of rain, or whether you will get an A in this course, we use probabilities. Doctors use probability to determine the chance of a vaccination causing the disease the vaccination is supposed to prevent. A stockbroker uses probability to determine the rate of return on a client's investments.

In statistics, we generally want to study a population . You can think of a population as a collection of persons, things, or objects under study. To study the population, we select a sample . The idea of sampling is to select a portion, or subset, of the larger population and study that portion—the sample—to gain information about the population. Data are the result of sampling from a population.

Because it takes a lot of time and money to examine an entire population, sampling is a very practical technique. If you wished to compute the overall grade point average at your school, it would make sense to select a sample of students who attend the school. The data collected from the sample would be the students' grade point averages. In presidential elections, opinion poll samples of 1,000–2,000 people are taken. The opinion poll is supposed to represent the views of the people in the entire country. Manufacturers of canned carbonated drinks take samples to determine if a 16-ounce can contains 16 ounces of carbonated drink.

From the sample data, we can calculate a statistic. A statistic is a number that represents a property of the sample. For example, if we consider one math class as a sample of the population of all math classes, then the average number of points earned by students in that one math class at the end of the term is an example of a statistic. Since we do not have the data for all math classes, that statistic is our best estimate of the average for the entire population of math classes. If we happen to have data for all math classes, we can find the population parameter. A parameter is a numerical characteristic of the whole population that can be estimated by a statistic. Since we considered all math classes to be the population, then the average number of points earned per student over all the math classes is an example of a parameter.

One of the main concerns in the field of statistics is how accurately a statistic estimates a parameter. In order to have an accurate sample, it must contain the characteristics of the population in order to be a representative sample . We are interested in both the sample statistic and the population parameter in inferential statistics. In a later chapter, we will use the sample statistic to test the validity of the established population parameter.

A variable , usually notated by capital letters such as X and Y , is a characteristic or measurement that can be determined for each member of a population. Variables may describe values like weight in pounds or favorite subject in school. Numerical variables take on values with equal units such as weight in pounds and time in hours. Categorical variables place the person or thing into a category. If we let X equal the number of points earned by one math student at the end of a term, then X is a numerical variable. If we let Y be a person's party affiliation, then some examples of Y include Republican, Democrat, and Independent. Y is a categorical variable. We could do some math with values of X —calculate the average number of points earned, for example—but it makes no sense to do math with values of Y —calculating an average party affiliation makes no sense.

Data are the actual values of the variable. They may be numbers or they may be words. Datum is a single value.

Two words that come up often in statistics are mean and proportion . If you were to take three exams in your math classes and obtain scores of 86, 75, and 92, you would calculate your mean score by adding the three exam scores and dividing by three. Your mean score would be 84.3 to one decimal place. If, in your math class, there are 40 students and 22 are males and 18 females, then the proportion of men students is 22 40 22 40 and the proportion of women students is 18 40 18 40 . Mean and proportion are discussed in more detail in later chapters.

The words mean and average are often used interchangeably. In this book, we use the term arithmetic mean for mean.

Example 1.1

Determine what the population, sample, parameter, statistic, variable, and data referred to in the following study.

We want to know the mean amount of extracurricular activities in which high school students participate. We randomly surveyed 100 high school students. Three of those students were in 2, 5, and 7 extracurricular activities, respectively.

The population is all high school students.

The sample is the 100 high school students interviewed.

The parameter is the mean amount of extracurricular activities in which all high school students participate.

The statistic is the mean amount of extracurricular activities in which the sample of high school students participate.

The variable could be the amount of extracurricular activities by one high school student. Let X = the amount of extracurricular activities by one high school student.

The data are the number of extracurricular activities in which the high school students participate. Examples of the data are 2, 5, 7.

Find an article online or in a newspaper or magazine that refers to a statistical study or poll. Identify what each of the key terms—population, sample, parameter, statistic, variable, and data—refers to in the study mentioned in the article. Does the article use the key terms correctly?

Example 1.2

Determine what the key terms refer to in the following study.

A study was conducted at a local high school to analyze the average cumulative GPAs of students who graduated last year. Fill in the letter of the phrase that best describes each of the items below.

1. Population ____ 2. Statistic ____ 3. Parameter ____ 4. Sample ____ 5. Variable ____ 6. Data ____

• a) all students who attended the high school last year
• b) the cumulative GPA of one student who graduated from the high school last year
• c) 3.65, 2.80, 1.50, 3.90
• d) a group of students who graduated from the high school last year, randomly selected
• e) the average cumulative GPA of students who graduated from the high school last year
• f) all students who graduated from the high school last year
• g) the average cumulative GPA of students in the study who graduated from the high school last year

1. f ; 2. g ; 3. e ; 4. d ; 5. b ; 6. c

Example 1.3

As part of a study designed to test the safety of automobiles, the National Transportation Safety Board collected and reviewed data about the effects of an automobile crash on test dummies (The Data and Story Library, n.d.). Here is the criterion they used.

Cars with dummies in the front seats were crashed into a wall at a speed of 35 miles per hour. We want to know the proportion of dummies in the driver’s seat that would have had head injuries, if they had been actual drivers. We start with a simple random sample of 75 cars.

The population is all cars containing dummies in the front seat.

The sample is the 75 cars, selected by a simple random sample.

The parameter is the proportion of driver dummies—if they had been real people—who would have suffered head injuries in the population.

The statistic is proportion of driver dummies—if they had been real people—who would have suffered head injuries in the sample.

The variable X = whether driver dummies—if they had been real people—would have suffered head injuries.

The data are either: yes, had head injury, or no, did not.

Example 1.4

An insurance company would like to determine the proportion of all medical doctors who have been involved in one or more malpractice lawsuits. The company selects 500 doctors at random from a professional directory and determines the number in the sample who have been involved in a malpractice lawsuit.

The population is all medical doctors listed in the professional directory.

The parameter is the proportion of medical doctors who have been involved in one or more malpractice suits in the population.

The sample is the 500 doctors selected at random from the professional directory.

The statistic is the proportion of medical doctors who have been involved in one or more malpractice suits in the sample.

The variable X records whether a doctor has or has not been involved in a malpractice suit.

The data are either: yes, was involved in one or more malpractice lawsuits; or no, was not.

Do the following exercise collaboratively with up to four people per group. Find a population, a sample, the parameter, the statistic, a variable, and data for the following study: You want to determine the average—mean—number of glasses of milk college students drink per day. Suppose yesterday, in your English class, you asked five students how many glasses of milk they drank the day before. The answers were 1, 0, 1, 3, and 4 glasses of milk.

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All science is about understanding variability in different characteristics, and most characteristics vary, hence we call the characteristics that we are studying ‘variables. When we work in a quantitative area, we make measurements. The scale of measurement is very important because one criterion for selecting the appropriate statistical technique is the scale of measurement used to measure whatever it is, we are studying.

There are different statistical techniques to use with each kind of measurement.

✓       Nominal Scale is the lowest level of measurement. Sometimes this is referred to as qualitative data – not to be confused with qualitative research. This scale uses numbers to describe names of discrete categories. One determines for each case whether they have or do not have the attribute in question.

✓       Ordinal Scale is used to rank people in order (e.g. least politically active to most politically active). This is the lowest level of quantitative data and involves the process of assignment of numbers to cases in terms of how much of the attribute is possessed by each subject.

✓       Continuous data can assume different values within a range. Interval Scale is where a number assigned is the amount of attribute possessed. Most statistics procedures can be used with interval data. Ratio Scale is considered the highest level of measurement, because all statistics tools can be used on ratio data.

When you read an article, you need to figure out what all the variables are in a study. Then you need to identify three things for each variable one at a time: the scale of measurement; the possible score range; and the meaning of high score and low score. Variables take on different functions in a study. We have to be able to tease these functions out. When you are conducting research, you have to recognize the different variables that are at play in your study so you can account for them during your analyses. Variables can take on different functions within the same study, so don’t classify them at the start. Researchers decide on a classification of variables in each analysis. Let’s take a look at the different classifications of variables.

Classification of variables

•          Dependent Variable : The outcome variable of interest is observed to see whether it is influenced by a manipulated variable. This is called a dependent variable. In other words, a characteristic that is dependent on, or thought to be influenced by, an independent variable. This is sometimes called outcome or response variable.

•         Independent Variable :  In experimental research, the researcher can manipulate one variable and measure the effect of that manipulation on another variable. The variable that is manipulated is called an independent variable. In other words, a characteristic that affects, or is thought to influence an outcome or dependent variable, or an antecedent condition. Independent variables are sometimes called factors, treatments, predictors, or manipulated variables.

In a better scenario, the only consistent feature that varies between an intervention and control group would be the outcome variable of interest. However, this is not generally the case, and we often have confounding or extraneous variables that play a part. When we design our research studies, we need to pay attention to and account for these variables also.

•       Control Variable : any variable that is held constant in a research study by observing only one of the instances or levels. Control variables are not necessarily of central interest, but things that a researcher cannot change or remove from participants. They might be known to exert some influence on the dependent variable. We can ’ t study everything, so a researcher may be interested, for example, in how parental education (and some other variable) is related to reading ability in younger children. He/she happens to know through previous research that gender is related to reading. So, for the purposes of the study, they chose to study only girls. Thus, gender is the control variable and is “ held constant ”.

•         Mediator (Intervening) Variable : a hypothetical variable that explains the relationship but is not observed directly in the research study. Rather, it is inferred from the relationship between the independent and dependent variable. This is an important concept to understand because most theory is based on notions of intervening variables and understanding how or why such effects occur. These variables might be clearly identified before doing a study, i.e. measured and analyzed within a study. Often, mediating variables surface as researchers interpret findings and emerge as suggestions for future research.

•         Moderator Variable : a variable/characteristic that moderates or changes the direction and/or strength of the relationship between two other variables. When, under what conditions, a relationship holds; influences on the strength of the relationship. For example, if a researcher were looking at the relationship between Socio economic status and AIDs prevention, age might be a moderator variable such that the relationship is stronger for older kids than younger kids.

Understanding the distinction between mediators and moderators is not always easy. Basically, in a mediation model the independent variable cannot influence the dependent variable directly and does so by means of another variable – the mediator. As a simple example, older people tend to be better drivers than young people. So, age is a predictor of good driving. However, when we think about why this is the case, we see that older people typically make wiser decisions and so wisdom could be seen as the mediating variable.

There are a number of tests that can be used within your statistical software program to test for mediating and moderating effects. Moderated regression is an example. A moderator analysis is used to determine whether the relationship between two variables depends on (is moderated by) the value of a third variable. You can find online tutorials to explore how this is conducted for the statistical package you are using. Regression can also be used to test for a mediating effect.

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Types of Variables in Psychology Research

Examples of Independent and Dependent Variables

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

 James Lacy, MLS, is a fact-checker and researcher.

Dependent and Independent Variables

• Intervening Variables
• Extraneous Variables
• Controlled Variables
• Confounding Variables
• Operationalizing Variables

Frequently Asked Questions

Variables in psychology are things that can be changed or altered, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.

Variables in psychology play a critical role in the research process. By systematically changing some variables in an experiment and measuring what happens as a result, researchers are able to learn more about cause-and-effect relationships.

The two main types of variables in psychology are the independent variable and the dependent variable. Both variables are important in the process of collecting data about psychological phenomena.

This article discusses different types of variables that are used in psychology research. It also covers how to operationalize these variables when conducting experiments.

Students often report problems with identifying the independent and dependent variables in an experiment. While this task can become more difficult as the complexity of an experiment increases, in a psychology experiment:

• The independent variable is the variable that is manipulated by the experimenter. An example of an independent variable in psychology: In an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable. The experimenters would have some of the study participants be sleep-deprived while others would be fully rested.
• The dependent variable is the variable that is measured by the experimenter. In the previous example, the scores on the test performance measure would be the dependent variable.

So how do you differentiate between the independent and dependent variables? Start by asking yourself what the experimenter is manipulating. The things that change, either naturally or through direct manipulation from the experimenter, are generally the independent variables. What is being measured? The dependent variable is the one that the experimenter is measuring.

Intervening Variables in Psychology

Intervening variables, also sometimes called intermediate or mediator variables, are factors that play a role in the relationship between two other variables. In the previous example, sleep problems in university students are often influenced by factors such as stress. As a result, stress might be an intervening variable that plays a role in how much sleep people get, which may then influence how well they perform on exams.

Extraneous Variables in Psychology

Independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.

For example, in our previous example of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender, and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so any impact can be controlled for.

There are two basic types of extraneous variables:

• Participant variables : These extraneous variables are related to the individual characteristics of each study participant that may impact how they respond. These factors can include background differences, mood, anxiety, intelligence, awareness, and other characteristics that are unique to each person.
• Situational variables : These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.

Other extraneous variables include the following:

• Demand characteristics : Clues in the environment that suggest how a participant should behave
• Experimenter effects : When a researcher unintentionally suggests clues for how a participant should behave

Controlled Variables in Psychology

In many cases, extraneous variables are controlled for by the experimenter. A controlled variable is one that is held constant throughout an experiment.

In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors don't interfere with the results. Holding these variables constant is important for an experiment because it allows researchers to be sure that all other variables remain the same across all conditions.  

Using controlled variables means that when changes occur, the researchers can be sure that these changes are due to the manipulation of the independent variable and not caused by changes in other variables.

It is important to also note that a controlled variable is not the same thing as a control group . The control group in a study is the group of participants who do not receive the treatment or change in the independent variable.

All other variables between the control group and experimental group are held constant (i.e., they are controlled). The dependent variable being measured is then compared between the control group and experimental group to see what changes occurred because of the treatment.

Confounding Variables in Psychology

If a variable cannot be controlled for, it becomes what is known as a confounding variabl e. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable, or an interaction of the two.

Operationalizing Variables in Psychology

An operational definition describes how the variables are measured and defined in the study. Before conducting a psychology experiment , it is essential to create firm operational definitions for both the independent variable and dependent variables.

For example, in our imaginary experiment on the effects of sleep deprivation on test performance, we would need to create very specific operational definitions for our two variables. If our hypothesis is "Students who are sleep deprived will score significantly lower on a test," then we would have a few different concepts to define:

• Students : First, what do we mean by "students?" In our example, let’s define students as participants enrolled in an introductory university-level psychology course.
• Sleep deprivation : Next, we need to operationally define the "sleep deprivation" variable. In our example, let’s say that sleep deprivation refers to those participants who have had less than five hours of sleep the night before the test.
• Test variable : Finally, we need to create an operational definition for the test variable. For this example, the test variable will be defined as a student’s score on a chapter exam in the introductory psychology course.

Once all the variables are operationalized, we're ready to conduct the experiment.

Variables play an important part in psychology research. Manipulating an independent variable and measuring the dependent variable allows researchers to determine if there is a cause-and-effect relationship between them.

A Word From Verywell

Understanding the different types of variables used in psychology research is important if you want to conduct your own psychology experiments. It is also helpful for people who want to better understand what the results of psychology research really mean and become more informed consumers of psychology information .

Independent and dependent variables are used in experimental research. Unlike some other types of research (such as correlational studies ), experiments allow researchers to evaluate cause-and-effect relationships between two variables.

Researchers can use statistical analyses to determine the strength of a relationship between two variables in an experiment. Two of the most common ways to do this are to calculate a p-value or a correlation. The p-value indicates if the results are statistically significant while the correlation can indicate the strength of the relationship.

In an experiment on how sugar affects short-term memory, sugar intake would be the independent variable and scores on a short-term memory task would be the independent variable.

In an experiment looking at how caffeine intake affects test anxiety, the amount of caffeine consumed before a test would be the independent variable and scores on a test anxiety assessment would be the dependent variable.

Just as with other types of research, the independent variable in a cognitive psychology study would be the variable that the researchers manipulate. The specific independent variable would vary depending on the specific study, but it might be focused on some aspect of thinking, memory, attention, language, or decision-making.

American Psychological Association. Operational definition . APA Dictionary of Psychology.

American Psychological Association. Mediator . APA Dictionary of Psychology.

Altun I, Cınar N, Dede C. The contributing factors to poor sleep experiences in according to the university students: A cross-sectional study .  J Res Med Sci . 2012;17(6):557-561. PMID:23626634

Skelly AC, Dettori JR, Brodt ED. Assessing bias: The importance of considering confounding .  Evid Based Spine Care J . 2012;3(1):9-12. doi:10.1055/s-0031-1298595

• Evans, AN & Rooney, BJ. Methods in Psychological Research. Thousand Oaks, CA: SAGE Publications; 2014.
• Kantowitz, BH, Roediger, HL, & Elmes, DG. Experimental Psychology. Stamfort, CT: Cengage Learning; 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

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Video transcript

TIMESTAMPS and Presentation Variables

TIMESTAMPS and Presentation Variables can be some of the most useful tools a report creator can use to invent robust, repeatable reports while maximizing user flexibility.  I intend to transform you into an expert with these functions and by the end of this page you will certainly be able to impress your peers and managers, you may even impress Angus MacGyver.  In this example we will create a report that displays a year over year analysis for any rolling number of periods, by week or month, from any date in time, all determined by the user.  This entire document will only use values from a date and revenue field.

The TIMESTAMP is an invaluable function that allows a user to define report limits based on a moving target. If the goal of your report is to display Month-to-Date, Year-to-Date, rolling month or truly any non-static period in time, the TIMESTAMP function will allow you to get there.  Often users want to know what a report looked like at some previous point in time, to provide that level of flexibility TIMESTAMPS can be used in conjunction with Presentation Variables.

To create robust TIMESTAMP functions you will first need to understand how the TIMESTAMP works. Take the following example:

Here we are saying we want to include all dates greater than or equal to 7 days ago, or from the current date.

• The first argument, SQL_TSI_DAY, defines the T ime S tamp I nterval (TSI) . This means that we will be working with days.
• The second argument determines how many of that interval we will be moving, in this case -7 days.
• The third argument defines the starting point in time, in this example, the current date.

So in the end we have created a functional filter making Date >= 1 week ago, using a TIMESTAMP that subtracts 7 days from today.

Note: it is always a good practice to include a second filter giving an upper limit like "Time"."Date" < CURRENT_DATE. Depending on the data that you are working with you might bring in items you don’t want or put unnecessary strain on the system.

We will now start to build this basic filter into something much more robust and flexible.

To start, when we subtracted 7 days in the filter above, let’s imagine that the goal of the filter was to always include dates >= the first of the month. In this scenario, we can use the DAYOFMONTH() function. This function will return the calendar day of any date. This is useful because we can subtract this amount to give us the first of the month from any date by simply subtracting it from that date and adding 1.

Our new filter would look like this:

For example if today is December 18 th , DAYOFMONTH(CURRENT_DATE) would equal 18. Thus, we would subtract 18 days from CURRENT_DATE, which is December 18 th , and add 1, giving us December 1 st .

(For a list of other similar functions like DAYOFYEAR, WEEKOFYEAR etc. click here .)

To make this even better, instead of using CURRENT_DATE you could use a prompted value with the use of a Presentation Variable (for more on Presentation Variables, click here ). If we call this presentation variable pDate, for prompted date, our filter now looks like this:

A best practice is to use default values with your presentation variables so you can run the queries you are working on from within your analysis. To add a default value all you do is add the value within braces at the end of your variable. We will use CURRENT_DATE as our default, @{pDate}{CURRENT_DATE}.  Will will refer to this filter later as Filter 1.

{Filter 1}:

As you can see, the filter is starting to take shape. Now lets say we are going to always be looking at a date range of the most recent completed 6 months. All we would need to do is create a nested TIMESTAMP function. To do this, we will “wrap” our current TIMESTAMP with another that will subtract 6 months. It will look like this:

Now we have a filter that is greater than or equal to the first day of the month of any given date (default of today) 6 months ago.

To take this one step further, you can even allow the users to determine the amount of months to include in this analysis by making the value of 6 a presentation variable, we will call it “n” with a default of 6, @{n}{6}.  We will refer to the following filter as Filter 2:

{Filter 2}:

For more on how to create a prompt with a range of values by altering a current column, like we want to do to allow users to select a value for n, click here .

Our TIMESTAMP function is now fairly robust and will give us any date greater than or equal to the first day of the month from n months ago from any given date. Now we will see what we just created in action by creating date ranges to allow for a Year over Year analysis for any number of months.

Consider the following filter set:

This appears to be pretty intimidating but if we break it into parts we can start to understand its purpose.

Notice we are using the exact same filters from before (Filter 1 and Filter 2).  What we have done here is filtered on two time periods, separated by the OR statement.

The first date range defines the period as being the most recent complete n months from any given prompted date value, using a presentation variable with a default of today, which we created above.

The second time period, after the OR statement, is the exact same as the first only it has been wrapped in another TIMESTAMP function subtracting 1 year, giving you the exact same time frame for the year prior.

This allows us to create a report that can run a year over year analysis for a rolling n month time frame determined by the user.

A note on nested TIMESTAMPS:

You will always want to create nested TIMESTAMPS with the smallest interval first. Due to syntax, this will always be the furthest to the right. Then you will wrap intervals as necessary. In this case our smallest increment is day, wrapped by month, wrapped by year.

Now we will start with some more advanced tricks:

• Instead of using CURRENT_DATE as your default value, use yesterday since most data are only as current as yesterday.  If you use real time or near real time reporting, using CURRENT_DATE may be how you want to proceed. Using yesterday will be valuable especially when pulling reports on the first day of the month or year, you generally want the entire previous time period rather than the empty beginning of a new one.  So, to implement, wherever you have @{pDate}{CURRENT_DATE} replace it with @{pDate}{TIMESTAMPADD(SQL_TSI_DAY,-1,CURRENT_DATE)}
• Presentation Variables can also be used to determine if you want to display year over year values by month or by week by inserting a variable into your SQL_TSI_MONTH and DAYOFMONTH statements.  Changing MONTH to a presentation variable, SQL_TSI_@{INT}{MONTH} and DAYOF@{INT}{MONTH}, where INT is the name of our variable.  This will require you to create a dummy variable in your prompt to allow users to select either MONTH or WEEK.  You can try something like this: CASE MOD(DAY("Time"."Date"),2) WHEN 0 'WEEK' WHEN 1 THEN 'MONTH' END

In order for our interaction between Month and Week to run smoothly we have to make one more consideration.  If we are to take the date December 1st, 2014 and subtract one year we get December 1st, 2013, however, if we take the first day of this week, Sunday December 14, 2014 and subtract one year we get Saturday December 14, 2014.  In our analysis this will cause an extra partial week to show up for prior years.  To get around this we will add a case statement determining if '@{INT}{MONTH}' = 'Week' THEN subtract 52 weeks from the first of the week ELSE subtract 1 year from the first of the month.

Our final filter set will look like this:

With the use of these filters and some creative dashboarding you can end up with a report that easily allows you to view a year over year analysis from any date in time for any number of periods either by month or by week.

That really got out of hand in a hurry! Surely, this will impress someone at your work, or even Angus MacGyver, if for nothing less than he or she won’t understand it, but hopefully, now you do!

Also, a colleague of mine Spencer McGhin just wrote a similar article on year over year analyses using a different approach. Feel free to review and consider your options.

Calendar Date/Time Functions

These are functions you can use within OBIEE and within TIMESTAMPS to extract the information you need.

• Current_Date
• Current_Time
• Current_TimeStamp
• Day_Of_Quarter
• Month_Of_Quarter
• Quarter_Of_Year
• TimestampAdd
• TimestampDiff
• Week_Of_Quarter
• Week_Of_Year

Back to section

Presentation Variables

The only way you can create variables within the presentation side of OBIEE is with the use of presentation variables. They can only be defined by a report prompt. Any value selected by the prompt will then be sent to any references of that filter throughout the dashboard page.

In the prompt:

From the “Set a variable” dropdown, select “Presentation Variable”. In the textbox below the dropdown, name your variable (named “n” above).

When calling this variable in your report, use the syntax @{n}{default}

If your variable is a string make sure to surround the variable in single quotes: ‘@{CustomerName]{default}’

Also, when using your variable in your report, it is good practice to assign a default value so that you can work with your report before publishing it to a dashboard. For variable n, if we want a default of 6 it would look like this @{n}{6}

Presentation variables can be called in filters, formulas and even text boxes.

Dummy Column Prompt

For situations where you would like users to select a numerical value for a presentation variable, like we do with @{n}{6} above, you can convert something like a date field into values up to 365 by using the function DAYOFYEAR("Time"."Date").

As you can see we are returning the SQL Choice List Values of DAYOFYEAR("Time"."Date") <= 52.  Make sure to include an ORDER BY statement to ensure your values are well sorted.

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Presentation

• Written By Gregg Rosenzweig
• Updated: November 8, 2023

We’re here to help you choose the most appropriate content types to fulfill your content strategy. In this series, we’re breaking down the most popular content types to their most basic fundamentals — simple definitions, clarity on formats, and plenty of examples — so you can start with a solid foundation.

What is a Presentation?

A communication device that relays a topic to an audience in the form of a slide show, demonstration, lecture, or speech, where words and pictures complement each other.

Why should you think of presentations as content?

The beauty of content creation is that almost anything can become a compelling piece of content . Just depends on the creativity used to convert it and the story that brings it to life.

The long and short of it

Although the length of a presentation in terms of time can depend on the overall approach (Are you talking a lot? Are you referring to the screen in detail or not?), consider the number of informational content slides when tallying the overall presentation length. For instance, don’t include title slides in your tally when conveying length to a content creator.

A general guide to presentation length:

• Short Form (5 content slides)
• Standard Form (10 content slides)
• Long Form (20+ content slides)

Popular use cases for presentations…

Let’s consider TED Talks for a minute: one of the best examples (bar none) of how words, pictures, and a narrative can make people care about something they otherwise might not.

These “talks” pre-date podcasts and blend a compelling use of language and imagery in presentation format to spread ideas in unique ways.

TED Talks have been viewed a billion-plus times worldwide (and counting) and are worth considering when it comes to how you might use video-presentation content to connect with your customers in creative, cool, new ways.

Business types:

Any company that has a pitch deck, executive summary , sales presentation, or any kind of internal document that can be repurposed into external-facing content pieces — without pain.

Presentation Examples – Short Form

Presentation Examples – Standard Form

Presentation Examples – Long Form

Understanding Content Quality in Examples

Our team has rated content type examples in three degrees of quality ( Good, Better, Best ) to help you better gauge resources needed for your content plan. In general, the degrees of content quality correspond to our three content levels ( General, Qualified, Expert ) based on the criteria below. Please consider there are multiple variables that could determine the cost, completion time, or content level for any content piece with a perceived degree of quality.

Impress your clients, co-workers, and leadership team with exceptional content for your next presentation, product demonstration, and more. If you need help getting your message across in a succinct, attention-grabbing, and persuasive way, talk to one of our content specialists today.

Stay in the know.

We will keep you up-to-date with all the content marketing news and resources. You will be a content expert in no time. Sign up for our free newsletter.

Elevate Your Content Game

Transform your marketing with a consistent stream of high-quality content for your brand.

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Advanced Techniques: Reference Stored Values in Variables

You might want to create an analysis whose title displays the current user's name. You can do this by referencing a variable.

You can reference several different types of variable in your analyses, dashboards, and actions: session , presentation , request , and global . Content authors can define presentation, request, and global variables themselves.

About Session Variables

Session variables are initialized when a user signs in.

These variables exist for each user for the duration of their browsing session and expire when the user closes the browser or signs out. There are two types of session variable: system and non-system.

System Session Variables

There are several system session variables that you can use in your reports and dashboards.

The system session variables have reserved names so you can’t use them for any other kind of variable.

Non-System Session Variables

The non-system session variables are named and created in your semantic model.

For example, your data modeler might create a SalesRegion variable that initializes to the name of a user's sales region when they sign in.

About Repository Variables

A repository variable is a variable that has a single value at any point in time.

Repository variables can be static or dynamic. A static repository variable has a value that persists and doesn’t change until the administrator changes it. A dynamic repository variable has a value that is refreshed by data returned from queries.

About Presentation Variables

A presentation variable is a variable that you can create as part of the process of creating a column prompt or a variable prompt.

The value of a presentation variable is populated by the column or variable prompt with which it was created. That is, each time a user selects one or more values in the column or variable prompt, the value of the presentation variable is set to the value or values that the user selects.

About Request Variables

A request variable enables you to override the value of a session variable but only for the duration of a database request initiated from a column prompt. You can create a request variable as part of the process of creating a column prompt.

You can create a request variable as part of the process of creating one of the following types of dashboard prompts:

A request variable that is created as part of a column prompt is associated with a column, and the values that it can take come from the column values.

To create a request variable as part of a column prompt, in the New Prompt dialog, you must select Request Variable in the Set a variable field. Enter the name of the session variable to override in the Variable Name field.

A request variable that is created as part of a variable prompt isn’t associated with any column, and you define the values that it can take.

To create a request variable as part of a variable prompt, in the New Prompt dialog (or Edit Prompt dialog), you must select Request Variable in the Prompt for field. Then enter a name of the session variable that you want to override in the Variable Name field.

The value of a request variable is populated by the column prompt with which it was created. That is, each time a user selects a value in the column prompt, the value of the request variable is set to the value that the user selects. The value, however, is in effect only from the time the user presses the Go button for the prompt until the analysis results are returned to the dashboard.

Certain system session variables (such as, USERGUID or ROLES) can’t be overridden by request variables. Other system session variables, such as DATA_TZ and DATA_DISPLAY_TZ (Timezone), can be overridden if configured in the Oracle BI Administration Tool.

Only string and numeric request variables support multiple values. All other data types pass only the first value.

About Global Variables

A global variable is a column created by combining a specific data type with a value. The value can be a Date, Date and Time, Number, Text, and Time.

The global variable is evaluated at the time the analysis is executed, and the value of the global variable is substituted appropriately.

Only users with the BI Service Administrator role can manage (add, edit, and delete) global variables.

You create a global value during the process of creating an analysis by using the Edit Column Formula dialog. The global variable is then saved in the catalog and made available to all other analyses within a specific tenant system.

Create Global Variables

You can save a calculation as a global variable then reuse it in different analyses. By just creating a global variable, you don’t have to create a new column in the Data Modeler .

• Open the analysis for editing.
• Select Edit Formula to display the Column Formula tab.
• Click Variable and select Global .

"Base Facts"."1- Revenue"*@{global.variables.gv_qualified}

• If you’re selecting "Date and Time" as the data type, then enter the value as in the following example: 03/25/2004 12:00:00 AM
• If you’re entering an expression or a calculation as a value, then you must use the Text data type, as in the following example: "Base Facts"."1- Revenue"*3.1415
• Click OK . The new global variable is added to the Insert Global Variable dialog.
• Select the new global variable that you just created, and click OK . The Edit Column Formula dialog is displayed with the global variable inserted in the Column Formula pane. The Custom Headings check box is automatically selected.
• Enter a new name for the column to which you have assigned a global variable to reflect the variable more accurately.

Syntax for Referencing Variables

You can reference variables in analyses and dashboards.

How you reference a variable depends on the task that you’re performing. For tasks where you’re presented with fields in a dialog, you must specify only the type and name of the variable (not the full syntax), for example, referencing a variable in a filter definition.

For other tasks, such as referencing a variable in a title view, you specify the variable syntax. The syntax that you use depends on the type of variable as described in the following table.

You can also reference variables in expressions. The guidelines for referencing variables in expressions are described in the following topics:

Session Variables

Presentation variables, repository variables.

You can use the following guidelines for referencing session variables in expressions.

• Include the session variable as an argument of the VALUEOF function.
• Enclose the variable name in double quotes.
• Precede the session variable by NQ_SESSION and a period.
• Enclose both the NQ_SESSION portion and the session variable name in parentheses.

For example:

"Market"."Region"=VALUEOF(NQ_SESSION."SalesRegion")

You can use the following guidelines for referencing presentation variable in expressions.

When referencing a presentation variable, use this syntax:

@{ variablename }{ defaultvalue }

where variablename is the name of the presentation variable and defaultvalue (optional) is a constant or variable reference indicating a value to be used if the variable referenced by variablename isn’t populated.

To type-cast (that is, convert) the variable to a string or include multiple variables, enclose the entire variable in single quotes, for example:

'@{user.displayName}'

If the @ sign isn’t followed by a {, then it’s treated as an @ sign. When using a presentation variable that can have multiple values, the syntax differs depending on the column type.

Use the following syntax in SQL for the specified column type in order to generate valid SQL statements:

Text — (@{ variablename }['@']{' defaultvalue '})

Numeric — (@{ variablename }{ defaultvalue })

Date-time — (@{ variablename }{timestamp ' defaultvalue '})

Date (only the date) — (@{ variablename }{date ' defaultvalue '})

Time (only the time) — (@{ variablename }{time ' defaultvalue '})

You can use the following guidelines for referencing repository variables in expressions.

• Include the repository variable as an argument of the VALUEOF function.
• Refer to a static repository variable by name.
• Refer to a dynamic repository variable by its fully qualified name.

CASE WHEN "Hour" >= VALUEOF("prime_begin") AND "Hour" < VALUEOF("prime_end") THEN 'Prime Time' WHEN ... ELSE...END

OBIEE - Presentation Variables

The presentation variable is the only variable offer by the presentation service.

You can use direct some system presentation variable (pre-populate or system presentation variable).

You can set it up (with a dashboard prompt, with a javascript, …)

With a dashboard prompt, it will take the data type of the presentation column .

The presentation variable can be referenced in various areas of presentation service (such as answer and dashboard).

In OBIEE, you have also as variable the OBI server variable that are managed by the oracle BI Server

Articles Related

• OBIEE - BI Server Variables (session and repository)
• OBIEE 10G/11G - The (dashboard|column) prompt
• OBIEE 10G/11G - Direct Database Request
• OBIEE - How and where can I set a Request variable (SET VARIABLE) ?
• OBIEE 10G/11G - How to set a presentation variable ?
• OBIEE - Presentation Variable System (reserved variable)
• OBIEE - Where can I use a presentation variable ?
• OBIEE - Date Format in presentation variable, dashboard prompt and logical SQL
• OBIEE - Criteria Tab
• OBIEE - All type of variables

The syntax for referencing presentation variables is as follows:

• variables - (optional)
• variableName - a reference to an object available in the current evaluation context that is not a reserved variable name
• for a string : 'String'
• for a number : 3
• for a date : date 'YYYY-MM-DD'
• format - (optional) - a format mask dependent on the data type of the variable.

For example: :

• MM/DD/YY hh:mm:ss.

More format example : Using Custom Date/Time Format Strings in Oracle BI Answers

• @{myFavoriteRegion}{'Central'}
• @{myFirstDate}{DATE '1973-07-24'}
• @{myFavoriteString}{'Nico'}
• @{myYear}{max(Time.Year)}

More : OBIEE - Where can I use a presentation variable ?

What is the scope of a presentation variable ?

The scope of a presentation variable (as a request variable ) depend of the prompt scope.

For instance in the dashboard prompt definition:

Then you can use it exclusively in a dashboard and not for the entire session. For instance, if you leave the dashboard by opening a new report or going into an other dashboard, the value will be reinitialized to the default value defined in the syntax.

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Python Variables

Python Variable is containers that store values. Python is not “statically typed”. We do not need to declare variables before using them or declare their type. A variable is created the moment we first assign a value to it. A Python variable is a name given to a memory location. It is the basic unit of storage in a program. In this article, we will see how to define a variable in Python .

Example of Variable in Python

An Example of a Variable in Python is a representational name that serves as a pointer to an object. Once an object is assigned to a variable, it can be referred to by that name. In layman’s terms, we can say that Variable in Python is containers that store values.

Here we have stored “ Geeksforgeeks ”  in a variable var , and when we call its name the stored information will get printed.

Notes: The value stored in a variable can be changed during program execution. A Variables in Python is only a name given to a memory location, all the operations done on the variable effects that memory location.

Rules for Python variables

• A Python variable name must start with a letter or the underscore character.
• A Python variable name cannot start with a number.
• A Python variable name can only contain alpha-numeric characters and underscores (A-z, 0-9, and _ ).
• Variable in Python names are case-sensitive (name, Name, and NAME are three different variables).
• The reserved words(keywords) in Python cannot be used to name the variable in Python.

Variables Assignment in Python

Here, we will define a variable in python. Here, clearly we have assigned a number, a floating point number, and a string to a variable such as age, salary, and name.

Declaration and Initialization of Variables

Let’s see how to declare a variable and how to define a variable and print the variable.

Redeclaring variables in Python

We can re-declare the Python variable once we have declared the variable and define variable in python already.

Python Assign Values to Multiple Variables 

Also, Python allows assigning a single value to several variables simultaneously with “=” operators.  For example: 

Assigning different values to multiple variables

Python allows adding different values in a single line with “,” operators.

Can We Use the S ame Name for Different Types?

If we use the same name, the variable starts referring to a new value and type.

How does + operator work with variables?  

The Python plus operator + provides a convenient way to add a value if it is a number and concatenate if it is a string. If a variable is already created it assigns the new value back to the same variable.

Can we use + for different Datatypes also?  

No use for different types would produce an error.

Global and Local Python Variables

Local variables in Python are the ones that are defined and declared inside a function. We can not call this variable outside the function.

Global variables in Python are the ones that are defined and declared outside a function, and we need to use them inside a function.

Global keyword in Python

Python global is a keyword that allows a user to modify a variable outside of the current scope. It is used to create global variables from a non-global scope i.e inside a function. Global keyword is used inside a function only when we want to do assignments or when we want to change a variable. Global is not needed for printing and accessing.

Rules of global keyword

• If a variable is assigned a value anywhere within the function’s body, it’s assumed to be local unless explicitly declared as global.
• Variables that are only referenced inside a function are implicitly global.
• We use a global in Python to use a global variable inside a function.
• There is no need to use a global keyword in Python outside a function.

Python program to modify a global value inside a function.

Variable Types in Python

Data types are the classification or categorization of data items. It represents the kind of value that tells what operations can be performed on a particular data. Since everything is an object in Python programming, data types are actually classes and variables are instances (object) of these classes.

Built-in Python Data types are:

• Sequence Type ( Python list , Python tuple , Python range )

In this example, we have shown different examples of Built-in data types in Python.

Object Reference in Python

Let us assign a variable x to value 5.

Another variable is y to the variable x.

When Python looks at the first statement, what it does is that, first, it creates an object to represent the value 5. Then, it creates the variable x if it doesn’t exist and made it a reference to this new object 5. The second line causes Python to create the variable y, and it is not assigned with x, rather it is made to reference that object that x does. The net effect is that the variables x and y wind up referencing the same object. This situation, with multiple names referencing the same object, is called a Shared Reference in Python. Now, if we write:

This statement makes a new object to represent ‘Geeks’ and makes x reference this new object.

Now if we assign the new value in Y, then the previous object refers to the garbage values.

Creating objects (or variables of a class type)

Please refer to Class, Object, and Members for more details. 

Frequently Asked Questions

1. how to define a variable in python.

In Python, we can define a variable by assigning a value to a name. Variable names must start with a letter (a-z, A-Z) or an underscore (_) and can be followed by letters, underscores, or digits (0-9). Python is dynamically typed, meaning we don’t need to declare the variable type explicitly; it will be inferred based on the assigned value.

2. Are there naming conventions for Python variables?

Yes, Python follows the snake_case convention for variable names (e.g., my_variable ). They should start with a letter or underscore, followed by letters, underscores, or numbers. Constants are usually named in ALL_CAPS.

3. Can I change the type of a Python variable?

Yes, Python is dynamically typed, meaning you can change the type of a variable by assigning a new value of a different type. For instance:

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