0
What are the independent and dependent variables in a small size bottle of water cost 1.99 and a large size bottle of water cost 3.49?
Either- and most people are ignorant of this fact.
If your study is about how the size of the bottle affects the price, then the independent variable is the size of the bottle and the dependent is the price.
However, if your study is to determine how the price that you pay affects the size of the bottle, the independent variable is the amount of money and the dependent is the bottle size.
Add your answer:
Earn +20 pts
Where do we use Gaussian distribution?
It is used when there are a large number of independent, identically distributed variables.
What are the types of variables in statistics?
dependent variables, independent variable, nominal, ordinal, interval, ratio variableThere are three main kinds:Nominal: such as colour of eyes, or gender, or species of animal. With nominal variables there is no intrinsic sense in which one category can be said to be "more" than another.Ordinal: Such as Small/Medium/Large, orStrongly Disagree/Disagree/Indifferent/Agree/Srongly Agree. The categories can be ordered but the differences between pairs is not comparable. For example, it is not really possible to say that the difference betwen Strongly disagree and disagree is the same as (or double or half or whatever) the difference between indifferent and agree.Interval: These are variables where the distance between one pair of values (their interval) can be related to the distance between another pair. Such variables can be subdivided into discrete and continuous.Another way of classifying variables is independent and dependent.The dependent variable is a random variable but the independent variable can be random or non-random.
When do you use a normal distribution?
When studying the sum (or average) of a large number of independent variables. A large number is necessary for the Central Limit Theorem to kick in - unless the variables themselves were normally distributed. Independence is critical. If they are not, normality may not be assumed.
Kinds of variables used in statistics?
There are three main kinds:Nominal: such as colour of eyes, or gender, or species of animal. With nominal variables there is no intrinsic sense in which one category can be said to be "more" than another.Ordinal: Such as Small/Medium/Large, orStrongly Disagree/Disagree/Indifferent/Agree/Srongly Agree. The categories can be ordered but the differences between pairs is not comparable. For example, it is not really possible to say that the difference betwen Strongly disagree and disagree is the same as (or double or half or whatever) the difference between indifferent and agree.Interval: These are variables where the distance between one pair of values (their interval) can be related to the distance between another pair. Such variables can be subdivided into discrete and continuous.Another way of classifying variables is independent and dependent.The dependent variable is a random variable but the independent variable can be random or non-random.
What is regression analysis and inferential analysis?
Regression AnalysisIt is a collection of statistical techniques to understand the relationship among several independent variables and one or more dependent random variables.Inferential AnalysisIt is collection of mathematical techniques to make prediction about unseen large data sets on the basis of a study of available small samples of that data.Regression analysis is a specific way of performing inferential analysis.
How many variables should there be in a good experiment?
A good experiment should have a limited number of variables, typically one or two, to ensure that the relationship between the variables can be clearly identified. Having too many variables can make it difficult to determine which factors are influencing the outcome of the experiment.
Where do we use Gaussian distribution?
It is used when there are a large number of independent, identically distributed variables.
What are the types of variables in statistics?
dependent variables, independent variable, nominal, ordinal, interval, ratio variableThere are three main kinds:Nominal: such as colour of eyes, or gender, or species of animal. With nominal variables there is no intrinsic sense in which one category can be said to be "more" than another.Ordinal: Such as Small/Medium/Large, orStrongly Disagree/Disagree/Indifferent/Agree/Srongly Agree. The categories can be ordered but the differences between pairs is not comparable. For example, it is not really possible to say that the difference betwen Strongly disagree and disagree is the same as (or double or half or whatever) the difference between indifferent and agree.Interval: These are variables where the distance between one pair of values (their interval) can be related to the distance between another pair. Such variables can be subdivided into discrete and continuous.Another way of classifying variables is independent and dependent.The dependent variable is a random variable but the independent variable can be random or non-random.
When do you use a normal distribution?
When studying the sum (or average) of a large number of independent variables. A large number is necessary for the Central Limit Theorem to kick in - unless the variables themselves were normally distributed. Independence is critical. If they are not, normality may not be assumed.
How do you see that the sampling distribution of the mean is normal?
According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.
Kinds of variables used in statistics?
There are three main kinds:Nominal: such as colour of eyes, or gender, or species of animal. With nominal variables there is no intrinsic sense in which one category can be said to be "more" than another.Ordinal: Such as Small/Medium/Large, orStrongly Disagree/Disagree/Indifferent/Agree/Srongly Agree. The categories can be ordered but the differences between pairs is not comparable. For example, it is not really possible to say that the difference betwen Strongly disagree and disagree is the same as (or double or half or whatever) the difference between indifferent and agree.Interval: These are variables where the distance between one pair of values (their interval) can be related to the distance between another pair. Such variables can be subdivided into discrete and continuous.Another way of classifying variables is independent and dependent.The dependent variable is a random variable but the independent variable can be random or non-random.
How did the normal distribution get its name?
According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.
What is the name of a very large bottle?
A magnum is a large bottle, carboy's are another.
What is regression analysis and inferential analysis?
Regression AnalysisIt is a collection of statistical techniques to understand the relationship among several independent variables and one or more dependent random variables.Inferential AnalysisIt is collection of mathematical techniques to make prediction about unseen large data sets on the basis of a study of available small samples of that data.Regression analysis is a specific way of performing inferential analysis.
What are some examples for a centiliter?
One hundredth of a large bottle of pop, One seventy fifth of a bottle of wine, One fiftieth of a large bottle of beer.
What meant by the term intrinsic?
An intrinsic property is an essential or inherent property of a system or of a material itself or within. It is independent of how much of the material is present and is independent of the form of the material, e.g., one large piece or a collection of smaller pieces. Intrinsic properties are dependent mainly on the chemical
Is disease density-dependent or density-independent?
Disease can be both density-dependent and density-independent. Density-dependent diseases spread more easily in populations with higher densities, where close contact facilitates transmission. In contrast, density-independent diseases are not affected by population density and spread regardless of the number of individuals present.
