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From none to completely. The regression or correlation coefficients measure the degree to which the two APPEAR to be related. However, there are some problems. First, if the model is mis-specified, you may find zero correlation even if there is complete determination. For example, suppose y = x^2 and you [wrongly] assume the reltionship is linear. If you carry out a regression of y on x between -a and a for any a, then the regression coefficient will turn out to be zero even though the dependent variable, y, is completely determined by x. Another mis-specification is where cause and effect are assigned the wrong way round because the system is not well understood.. Yet another problem is that a correlation does not necessarily imply a causal relationship. Each of the two variables may be independently affected by a third variable. For example, my age is probably highly correlated to the world population but neither affects the other. Both are affected by time.
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What are the Difference between endogenous and exogenous variables?
Endogenous variables are important in econometrics and economic modeling because they show whether a variable causes a particular effect. Economists employ causal modeling to explain outcomes (dependent variables) based on a variety of factors (independent variables), and to determine to which extent a result can be attributed to an endogenous or exogenous cause.
What does a regression model predict about the dependent variable?
It gives a measure of the extent to which values of the dependent variable move with values of the independent variables. This will enable you to decide whether or not the model has any useful predictive properties (significance). It also gives a measure of the expected changes in the value of the dependent variable which would accompany changes in the independent variable. A regression model cannot offer an explanation. The fact that two variables move together does not mean that changes in one cause changes in the other. Furthermore it is possible to have very closely related variables which, because of a wrongly specified model, can show no correlation. For example, a LINEAR model fitted to y=x2 over a symmetric range for x will show zero correlation!
What is the similarities between correlation analysis and regression analysis?
Correlation analysis seeks to establish whether or not two variables are correlated. That is to say, whether an increase in one is accompanied by either an increase (or decrease) in the other most of the time. It is a measure of the degree to which they change together. Regression analysis goes further and seeks to measure the extent of the change. Using statistical techniques, a regression line is fitted to the observations and this line is the best measure of how changes in one variable affect the other variable. Although the first of these variables is frequently called an independent or even explanatory variable, and the second is called a dependent variable, the existence of regression does not imply a causal relationship.
What does Variable term means in math?
A variable is a symbol that represents one or more numbers.
What question does the adverb phrase answer?
The adverb phrase commonly answers questions such as how, when, where, why, or to what extent.
Does the dependent variable depend on the independent variable?
Yes, the dependent variable is influenced by changes in the independent variable. The relationship between the two variables is typically investigated through statistical analysis to determine the extent of this influence.
What is the literary definition of total effect?
the extent to which the dependent variable changes
What axis does temperature go on?
Whichever axis you like. To some extent it depends on whether temperature is the independent or the dependent variable. If the graph is of the temperature of some food when it has been in an over for different lengths of time, then the independent variable is the time and the temperature should be on the vertical axis. However, if the graph is of the temperature of the same food and the number of bacteria present in it, then the temperature is the independent variable and should be on the horizontal axis.
In an experimental study of the extent to which mental alertness is inhibited by sleep deprivation alertness would be the?
dependent variable
Why is an idependent variable needed?
An independent variable is a variable that you can control, you can choose and manipulate this variable. An example is an experiment looking at the growth of trees in the Dark, in a dimly lit room and in the direct sun. So you are going to put one plant in the sun, one in the dark and another in a dimly lit room. The independent variable is the location of the experiment, because this is what is being changed. The dependent variable may be the type of the plants and the height of the plants to begin with, The results on which is determined by the independent variable (ie amount of sunlight on the plants) So basically, without the independent variable you cannot really measure the full extent of the results. Uhm, I hope that makes sense, haha.
What are the Difference between endogenous and exogenous variables?
Endogenous variables are important in econometrics and economic modeling because they show whether a variable causes a particular effect. Economists employ causal modeling to explain outcomes (dependent variables) based on a variety of factors (independent variables), and to determine to which extent a result can be attributed to an endogenous or exogenous cause.
What is mean by compounding variable?
I think there is confusion between the terms "compounding variable" and "confounding variable". My way of looking at it is that compounding variables describe elements of mathematical functions, only. Confounding variables apply to any research in any domain and are external variables to the research design which might impact on the dependent variable to a lesser or greater extent than the independent variable, which are part of the research design. I am Peter Davies at classmeasures@aol.com
What does a regression model predict about the dependent variable?
It gives a measure of the extent to which values of the dependent variable move with values of the independent variables. This will enable you to decide whether or not the model has any useful predictive properties (significance). It also gives a measure of the expected changes in the value of the dependent variable which would accompany changes in the independent variable. A regression model cannot offer an explanation. The fact that two variables move together does not mean that changes in one cause changes in the other. Furthermore it is possible to have very closely related variables which, because of a wrongly specified model, can show no correlation. For example, a LINEAR model fitted to y=x2 over a symmetric range for x will show zero correlation!
How is the value of the correlation coefficient related to the accuracy of the predicated value for a specific value x?
The nearer the absolute value of the correlation coefficient is to 1, the higher the accuracy of the predicted value. At r = 0, any prediction based on the independent variable is inaccurate - to the extent of being a waste of time.The nearer the absolute value of the correlation coefficient is to 1, the higher the accuracy of the predicted value. At r = 0, any prediction based on the independent variable is inaccurate - to the extent of being a waste of time.The nearer the absolute value of the correlation coefficient is to 1, the higher the accuracy of the predicted value. At r = 0, any prediction based on the independent variable is inaccurate - to the extent of being a waste of time.The nearer the absolute value of the correlation coefficient is to 1, the higher the accuracy of the predicted value. At r = 0, any prediction based on the independent variable is inaccurate - to the extent of being a waste of time.
Compare and Contrast Internal validity vs external validity?
Internal validity is the degree to which the results are attributable to the independent variable and not some other explanations.External validity is the extent to which the results of a study can be generalized.
Is c plateform- independent. if yes then why . if no then why?
To an extent, C is platform dependent; for example, if you declare a variable as a "short" then the number of bytes allocated to the variable depends on the CPU. Also, the byte order of variables (either Intel or Motorola) isn't hidden. However, it is possible - and commonly done - to write platform independent software. Usually this is done by using macros and defined variable types to handle the variation in word length and byte order and, occasionally, using #ifdef/#enddef to handle particular dependences. This might be seen as a weakness of the language, but it is actually a strength - other languages "hide" the dependances making it difficult to write efficient code that runs on a wide range of processors.
What is the similarities between correlation analysis and regression analysis?
Correlation analysis seeks to establish whether or not two variables are correlated. That is to say, whether an increase in one is accompanied by either an increase (or decrease) in the other most of the time. It is a measure of the degree to which they change together. Regression analysis goes further and seeks to measure the extent of the change. Using statistical techniques, a regression line is fitted to the observations and this line is the best measure of how changes in one variable affect the other variable. Although the first of these variables is frequently called an independent or even explanatory variable, and the second is called a dependent variable, the existence of regression does not imply a causal relationship.
