What is a Potential Confounding Variable?
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A confounding variable is a particular type of extraneous variable which for some reason has been left uncontrolled. The result of this is that when we look at the outcome of an experimental study, rather than only one possible variable (IV) exerting an influence on the DV, there are found to be others, and these are said to be confounding the results.\n\nFor example, consider a study in which a new drug for the control of hypertension (high blood pressure) was being investigated. We set up a trial in which two groups are compared, one group with the drug, another with a placebo. When we look at the data we find the the group receiving the drug do indeed have lower blood pressure than the control group. However then we notice, that for some reason the average age of the experimental group, i.e. those receiving the drug, is significantly lower than that of the control group. Now we know hypertension is age related, and therefore the difference in blood pressure between the two groups might be due to age differences rather than the effect of the drug. We say the age differences has confounded the findings.
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To answer that question you would have to know if the differences between "having a variation" and "not having the variation" are normally distributed.  If so, you could make an assumption on the number to look at based on normal distribution and 3 standard deviations.\n\nIf there is no normal distribution to the data, then you would have to determine your own "statistics" for the odds that the deviation would occur, and calculate the number based on the possible number of "variations" that could occur, versus the odds that any one could occur.
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The "variables" are the things in the experiment you are testing.  If you wanted to see if the rate of polar bear births increased when the sale of ice cream increased in Georgia (btw, it does), then the variables would be polar bear births and ice cream sales in Georgia.\n\nYou should note that just because the values of your variables change, does not mean that a cause and effect relationship exists.  As in the example above, just because more polar bears are born in years where ice cream sales in Georgia are high, does not mean that Georgia can make polar bears have more babies just by buying more ice cream.
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often it refers to the controllable factors. It could mean that other possible influences on the research have been accounted for.
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Causality itself is a problem in QM. What QM does is give the probability density of these virtual particles and the adjustments to other calculations that must be made to take them into account. It also gives the probabilities of actually measuring certain particles when you do specific experiments. It does not talk about cause or effect for actual observations, though. Just probabilities.
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Are you sure there is a relationship between these variables?  \n\nCan you define pressure tendency? Do they make a pressure tendency gauge?
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His findings support the \nlack of a dependable variable in identifying where population growth will be bad.  \n\nThe reason for that is that in exploratory research very often we do not know which variables are more likely to influence the dependable variable\n\nGravitation is merely a dependable variable of attraction\n\ndependable variable is supported by empirical data obtained from the research
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It appears that the constants are calculated at one condition (dry,wet,etc) whereas the effective is closer to reality.  See\n\nhttp://www.pnas.org/cgi/content/full/102/45/16293
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if i am not wrong..presence of one component( i forget the name)..makes its positive...n negative..no need to worry..cheers
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There are many, many things that are highly likely to eventually occur: Diabetes (type II), certain types of cancer, high blood pressure, high cholesterol, weight problems, heart problems, varicose veins, stroke. The unfortunate possibilities are endless.
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I am not sure that I follow what you are asking. Are you looking for the above items to be charted as variables on your graph?\n\nPerhaps if you post additional details in your question, it would help us.
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Thats pretty basic.  The control in the experiment is the constant.  Something that has a predictable outcome.   The variable is the item that you are experimenting on.\nKind of like haveing a placebo in a drug test...
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Theoretically, no, not according to our current understanding.  In fact, that would violate causality, because an effect would then predate it's cause
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Good lord. I had to teach myself statistics, and I don't remember a lot of it, but I'll do what I can.\n\na) The null hypothesis is that the two learning environments have no impact on the grades of the course. The alternative hypothesis is that they will be different.\nb) Two-tailed, since you do not know which environment will be better or worse.\nc) The time spent in one vs. the other, and the quality of the teacher (as measured as a variable.)\nd) That there would be a standard margin for disparity.\ne) I have no clue. I never got that far.\n\n2) Don't know.
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remember that variables are something that you can change. Your independent variable or manipulated variable is what remains consistent. Your dependent variable is something that you can change throughout the experiment or project. For example if you're doing an experiment plants in which you see if light affects it; the independent variable is the plant. Your dependent variable is what you change or in this case if you put a lamp in the sun and one in the shade. That would be your dependent variable...at least that's what I remembe from science. I might have it backwards...i'm not sure
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I would expect light exposure variance has much to do with it. Look at how it effects circadian rhythm, for instance. Probably begins a cascade of varying hormonal changes.
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BMI alone cannot predict your risk for disease.  Other physical (exercise capacity, family history, etc.) and behavioral characteristics (smoking, drinking/driving, sexual activity, etc.) are required to estimate your risk.\n\nYou may be interested in these free online health risk estimators:\n\nhttp://www.yourdiseaserisk.harvard.edu/