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Explanatory and response variables

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Category: Explanatory and response variables

Category: Explanatory and response variables

Explanatory and response variables

This Quiz tests your understanding of explanatory and response variables. Note : Explanatory variables have previously been called 'Independent' variables, and Response variables were 'Dependent' variables. If you see this terminology just convert Explanatory to Independent and Response to Dependent. Search Speak now. Questions All questions 5 questions 6 questions 7 questions 8 questions 9 questions 10 questions.

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Remove Excerpt. Removing question excerpt is a premium feature. In questions that involved weight and height, height is likely to be the :. I measure the height and weight of 30 students to see if there is a relationship between height and weight. Height in this example is the :. Weight in this example is the :.

I weigh 37 students and then time them over metres to see if there is a relationship between weight, and how quickly they run metres. In this example, time over metres is the :. I plant 40 seedlings and give them various exposure to light measure in lux. After a month, I measure each seedling to check its growth. I want to know if there is a relationship between the amount of light each plant received, and the amount it has grown.

In this example, light measured in lux is the :. I am planning on changing to a new brand of coffee and test my customers to see if it is wise by offering free samples of the new coffee. I split my customers into male and female, and ask if I should switch, or not. In this example, opinion on switching brands is the :. I am a politician, and I am trying to gauge the opinions of the electorate on environmental policy.

I survey men and women to find if there is a difference in opinion. In this example, gender is the :. I am an advertising agent, putting forward a plan to the education department to help reduce smoking in society.

I survey 50 people on whether they smoke or not, and separate them into those with post secondary education, and those who finished formal education in secondary school. In this example, smoking status is the :.In some research studies one variable is used to predict or explain differences in another variable. In an experimental study, the explanatory variable is the variable that is manipulated by the researcher. A team of veterinarians wants to compare the effectiveness of two fertility treatments for pandas in captivity.

The two treatments are in-vitro fertilization and male fertility medications. A public speaking teacher has developed a new lesson that she believes decreases student anxiety in public speaking situations more than the old lesson. She designs an experiment to test if her new lesson works better than the old lesson. Public speaking students are randomly assigned to receive either the new or old lesson; their anxiety levels during a variety of public speaking experiences are measured.

A researcher believes that the origin of the beans used to make a cup of coffee affects hyperactivity. He wants to compare coffee from three different regions: Africa, South America, and Mexico. A group of middle school students wants to know if they can use height to predict age.

They take a random sample of 50 people at their school, both students and teachers, and record each individual's height and age. This is an observational study. The researcher wants to use grade level to explain differences in height.

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Example: Panda Fertility Treatments A team of veterinarians wants to compare the effectiveness of two fertility treatments for pandas in captivity.

Example: Public Speaking Approaches A public speaking teacher has developed a new lesson that she believes decreases student anxiety in public speaking situations more than the old lesson. Example: Coffee Bean Origin A researcher believes that the origin of the beans used to make a cup of coffee affects hyperactivity.A response variable measures an outcome of a study. It is the particular quantity about which questions are asked.

An explanatory variable is any factor that can influence the response variable. An explanatory variable attempts to explain the observed outcomes. The response variable is usually called dependent, while the explanatory variable is sometimes called independent. Whereas a study can be without a response variable, there can be several explanatory variables.

The naming of a response variable is determined by the type of study. An observational study is an example of an investigation without a response variable, as when a researcher studies the mood and attitudes of a group of first-year students.

All the first-year students may be given several questions to examine the homesickness degree of a student. Students may also indicate how distant home is from the college. In such a case, the response variable is missing because it cannot be seen how the value of one variable influences the value of another.

A second researcher may use the same data and try to answer if there is a greater degree of homesickness in students who came from further away. In this scenario, the data relating to homesickness questions constitute the values of a response variable. The distance from home, in this case, is the explanatory variable.

explanatory and response variables

More From Reference. Estate Planning How to Probate a Will.This tutorial covers explanatory and response variables. As a review, a variable is a characteristic of a person or a thing. And then sometimes when you have the set of variables, it's going to make sense to pick an explanatory variable and a response variable. That's not always going to be true. Sometimes it's not going to have a clear explanatory and response variable, and that's OK. But what an explanatory variable is, it's one that might cause an effect.

So the explanatory variable is the thing that you're looking to cause something to happen. And the response variable, on the other hand, is the one that's going to reflect that effect. And the explanatory variables, when you're making a scatter plot, are going to go in the x-axis. And the response variables are going to go in the y-axis.

Now, if you don't have a clear-cut explanatory and response variable, then it doesn't really matter which one goes on the x and y. You can choose either. You can try graphing it with both on there and choose which one gives the right picture that you're looking for. But when you have explanatory and response, then it has to go x for explanatory, y for response. So in this first example here, it says we're comparing two variables. The first variable is the age of a young farm animal and the second variable is the weight of a young farm animal.

Here, it's going to make sense that there's an explanatory and a response. We think that age would have an effect on the weight. So the age is going to be the explanatory variable. And so then we're going to put that on the x. Whereas, the weight is going to be the response variable, so that would go on the y.

Now with example 2, it says a student's grade in English and a student's grade math. There, it's not as clear cut. We don't know whether the English grade is causing you to have a better math grade or the math grade is causing you to have a better English grade, or there's something else outside.The following examples show different scenarios involving explanatory and response variables.

A botanist wants to compare the effect that two different fertilizers have on plant growth. She randomly selects 20 plants from a field and applies fertilizer A to them for one week. She also randomly selects another 20 plants from the same field and applies fertilizer B to them for one week. After one week she measures the average plant growth for each group. This is the variable we change so that we can observe the effect it has on plant growth.

This is the variable that changes as a result of the fertilizer being applied to it. He randomly assigns 10 players to use training program A for one week, another 10 players to use training program B for one week, and another 10 players to use training program C for one week.

At the end of the week he measures the max vertical jump of each player to see if there are significant differences between the groups. This is the variable we change so that we can observe the effect it has on max vertical jump.

Response Variable: Max vertical jump.

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This is the variable that changes as a result of the training program used by the player. A real estate agent wants to understand the relationship between square footage of a house and selling price.

She collects data about square footage and selling price for houses in her city and analyzes the relationship between the two variables. Explanatory Variable: Square footage. This is the variable that we observe change in so that we can observe the effect it has on selling price. Response Variable: Selling price. This is the variable that changes as a result of the square footage of the house being changed.

In each of the examples above, we changed the values of some explanatory variable and observed the resulting change in values of some response variable. What is a Lurking Variable? What is a Confounding Variable?

What Is the Difference Between Explanatory Variables and Response Variables?

Independent vs. Your email address will not be published. Skip to content Menu. Posted on September 9, February 19, by Zach. Example 1: Plant Growth A botanist wants to compare the effect that two different fertilizers have on plant growth. Example 3: Real Estate Prices A real estate agent wants to understand the relationship between square footage of a house and selling price.

In this example, we have: Explanatory Variable: Square footage. Summary In each of the examples above, we changed the values of some explanatory variable and observed the resulting change in values of some response variable.

explanatory and response variables

Additional Resources What is a Lurking Variable? Published by Zach. View all posts by Zach. Leave a Reply Cancel reply Your email address will not be published.One of the many ways that variables in statistics can be classified is to consider the differences between explanatory and response variables.

Although these variables are related, there are important distinctions between them. After defining these types of variables, we will see that the correct identification of these variables has a direct influence on other aspects of statistics, such as the construction of a scatterplot and the slope of a regression line.

We begin by looking at the definitions of these types of variables. A response variable is a particular quantity that we ask a question about in our study.

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An explanatory variable is any factor that can influence the response variable. While there can be many explanatory variables, we will primarily concern ourselves with a single explanatory variable. A response variable may not be present in a study. The naming of this type of variable depends upon the questions that are being asked by a researcher.

The conducting of an observational study would be an example of an instance when there is not a response variable.

explanatory and response variables

An experiment will have a response variable. The careful design of an experiment tries to establish that the changes in a response variable are directly caused by changes in the explanatory variables.

To explore these concepts we will examine a few examples. For the first example, suppose that a researcher is interested in studying the mood and attitudes of a group of first-year college students. All first-year students are given a series of questions. These questions are designed to assess the degree of homesickness of a student.

Students also indicate on the survey how far their college is from home. One researcher who examines this data may just be interested in the types of student responses. Perhaps the reason for this is to have an overall sense about the composition of a new freshman. In this case, there is not a response variable. This is because no one is seeing if the value of one variable influences the value of another. Another researcher could use the same data to attempt to answer if students who came from further away had a greater degree of homesickness.

In this case, the data pertaining to the homesickness questions are the values of a response variable, and the data that indicates the distance from home forms the explanatory variable. For the second example we might be curious if number of hours spent doing homework has an effect on the grade a student earns on an exam.

In this case, because we are showing that the value of one variable changes the value of another, there is an explanatory and a response variable. The number of hours studied is the explanatory variable and the score on the test is the response variable. When we are working with paired quantitative datait is appropriate to use a scatterplot. The purpose of this kind of graph is to demonstrate relationships and trends within the paired data.

We do not need to have both an explanatory and response variable.

1.1.2 - Explanatory & Response Variables

If this is the case, then either variable can plotted along either axis. However, in the event that there is a response and explanatory variable, then the explanatory variable is always plotted along the x or horizontal axis of a Cartesian coordinate system.

The response variable is then plotted along the y axis.

Explanatory vs Response Variables

The distinction between explanatory and response variables is similar to another classification. Sometimes we refer to variables as being independent or dependent. The value of a dependent variable relies upon that of an independent variable. Thus a response variable corresponds to a dependent variable while an explanatory variable corresponds to an independent variable. This terminology is typically not used in statistics because the explanatory variable is not truly independent.

Instead the variable only takes on the values that are observed. We may have no control over the values of an explanatory variable.In some research studies one variable is used to predict or explain differences in another variable.

In an experimental study, the explanatory variable is the variable that is manipulated by the researcher. A team of veterinarians wants to compare the effectiveness of two fertility treatments for pandas in captivity. The two treatments are in-vitro fertilization and male fertility medications. A public speaking teacher has developed a new lesson that she believes decreases student anxiety in public speaking situations more than the old lesson.

She designs an experiment to test if her new lesson works better than the old lesson. Public speaking students are randomly assigned to receive either the new or old lesson; their anxiety levels during a variety of public speaking experiences are measured. A researcher believes that the origin of the beans used to make a cup of coffee affects hyperactivity.

He wants to compare coffee from three different regions: Africa, South America, and Mexico. A group of middle school students wants to know if they can use height to predict age.

They take a random sample of 50 people at their school, both students and teachers, and record each individual's height and age. This is an observational study.

The researcher wants to use grade level to explain differences in height.

explanatory and response variables

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