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: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: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.
Some times. At other times it uses mutually dependent variables (changes in each variable affect the other).
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.
Whenever you suspect that the passage of time is one of the factors influencing one or more of the other variables you are investigating.
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.
Statistics and Logic - the political theorist must possess a broad scientific background and a knowledge of current political problems, and he must employ scientific methods in gathering and evaluating data and in drawing conclusions. These involve a proper application of statistical procedures for the quantitative measurement of social phenomena and of logical procedures for the analysis of reasoning.
A quantitative variable is numeric and therefore can be counted discretely or continuously. The other side of the spectrum is qualitative variables.
Some times. At other times it uses mutually dependent variables (changes in each variable affect the other).
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.
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.
Whenever you suspect that the passage of time is one of the factors influencing one or more of the other variables you are investigating.
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
Examples: granulation, temperature, stirring, volume of liquid.
(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
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.
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.
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.
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.