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You're probably used to the word "variable" in algebra. Letters like x and y are used in place of numbers. Twotypes of variables are used in statistics: Quantitative and categorical. Quantitative variables are numerical: counts, percents, or numbers. Categorical variables are descriptions of groups or things, like "breeds of dog" or "voting preference".

Examples of Quantitative Variables:
  • High school Grade Point Average.
  • Number of pets owned.
  • Bank account balance.
  • Number of stars in a solar system.
  • Average number of lottery tickets sold.
  • How many cousins you have.
  • The amount in your paycheck.

General rule: if you can add it, it's quantitative. For example, a G.P.A. of 3.3 and a G.P.A. of 4.0 can be added together (3.3 + 4.0 = 7.3), so that means it's quantitative.

Examples of Categorical Variables:
  • Class in college (freshman, sophomore, junior, senior).
  • Party affiliation (Republican, Democrat, Independent).
  • Type of pet owned (dog, cat, rodent, fish).
  • Favorite author.
  • Preferred airline.
  • Hair color.
  • Your race.
  • Types of hats.

As a general rule, if you can't add something, then it's categorical. For example, you can't add cat + dog, or Republican + Democrat.

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Q: What are the other examples of quantitative variables in statistics?
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What data can be referred to things that can be counted?

A quantitative variable is numeric and therefore can be counted discretely or continuously. The other side of the spectrum is qualitative variables.


Does quantitative research typically use independent and dependent variables?

Some times. At other times it uses mutually dependent variables (changes in each variable affect the other).


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It means there is no discernable relationship between the two variables. Knowing one variable does not give you any help in working out the other. They are independent of each other.


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Two or more explanatory variables are collinear when they have a linear relationship with each other. You are usually expected to remove at least one of the variables from your multiple regression analysis.


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Whenever you suspect that the passage of time is one of the factors influencing one or more of the other variables you are investigating.


Difference between qualitative and quantitative variables?

A quantitative variable is a variable that can be measured by a number, usually on a ratio scale, but at least on an interval or ordinal scale, such that less and more can be measured and determined.EXAMPLE:CARS IN A CAR PARKA qualitative variable is also called a categorical variable. The items are different, but the difference is not a measure, such as square and round, hungry and fed. Statistically, these variables may be known as binomial or amenable to chi-square analysis.EXAMPLE:RED CARS IN A CAR PARK


What is the other variables of what dissolves more quickly salt or borax?

Examples: granulation, temperature, stirring, volume of liquid.


What is sensitive price index?

(Mathematics & Measurements / Statistics) a numerical scale by means of which variables, such as levels of the cost of living, can be compared with each other or with some base number


What are examples variables in a equation?

Y = 2X Variables are X and Y If X = 2, then Y = 4 If X = 3, then Y = 6 and so forth. If you vary one then he other changes. Their values are variable.


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Standardization of raw data is the process of making its variables proportionate to each other. In statistics, it is often achieved by subtracting the mean from values and then dividing them by their Standard Deviation.


Examples of continuous variables?

A continuous variable is one that can take infinite number of values in an interval. Examples are weight, height. A person's weight can be 150.2 lbs, 150.456 pounds and so on. Discrete variables, on the other hand can only assume a finite number of values. For example, number of people in a movie theatre.


What is the importance of statistics in agriculture?

Statistics are used in the scientific study of agriculture as a tool to determine if the differences in variables are real or due to chance. This translates to the farmer to let him know with confidence which varieties are better than other varieties or which fertilizer treatments will give better yields than others.