Statistics

Diffusion is essentially the movement of solute molecules from a region of higher concentration to a region of lower concentration. A semi permeable membrane need not be used to study this phenomenon

1. Formulate a clear research question or hypothesis.
2. Design a study and collect data.
3. Analyze the data using appropriate statistical methods.
4. Draw conclusions and make inferences based on the results.
5. Communicate findings through written reports or presentations.

There are several as best I can recall. Here they are with basic reason you might encounter them. Using the built in Excel help you will get more detail and assistance in solving them

#VALUE! - You are trying to perform a calculation or function with an improper type, e.g. taking the absolute value of a string

#DIV/0! - You are dividing by zero

#NAME? - The function you are using is unknown

#REF! - The cell you are refering to is no longer a valid cell address

#NUM! - The numeric result in that cell is too big

#N/A - Several different situations can cause this

#NULL - A null or 'no data' is in that cell

The 13 colonies were grouped geographically into three regions: New England, Middle, and Southern colonies. New England colonies included Massachusetts, Connecticut, Rhode Island, and New Hampshire. Middle colonies consisted of New York, New Jersey, Delaware, and Pennsylvania. Southern colonies included Maryland, Virginia, North Carolina, South Carolina, and Georgia.

A protosun is a sun (star) that is still forming. It takes millions of years of solar winds and gravitational forces to gather and compress massive clouds of hydrogen to the point where it's gravity is powerful enough to cause nuclear fusion at it's core and "ignite" a new star.

Two examples of cubic crystal systems are the face-centered cubic (FCC) structure, found in metals such as copper and aluminum, and the body-centered cubic (BCC) structure, found in metals such as iron and chromium.

Matter is anything that takes up space and is either a solid, liquid, or gas

Mesoamerica is a geographical region that includes Mexico and Central America. In this areas, different civilizations flourished, such as the Maya and Aztec empires. The word 'Mesoamerica' means 'Middle America', which is from the Latin.

* Foliated (banded) - minerals oriented along visible planes * Example: Schist (fine grain) (from slate) * Example: Gneiss (coarse grain) (from a variety of rock, one of which is granite) * Non-foliated (no banding) usually rock of one mineral) * Example: Quartzite (from sandstone - quartz) * Example: Marble (from limestone - calcium carbonate

The traditional view that one cannot prove the null hypothesis by a statistical analysis is a consequence of Ronald Fisher's structuring of the problem of probabilistic inference. Fisher argued that if one wanted to determine whether an experimental manipulation (e.g., a drug treatment or a treatment of some crop land) had an effect (on e.g., recovery rate or crop yield), one should compute the probability that one would obtain an effect (that is, a difference in the means of two samples, a control sample, and an experimental sample) as big or bigger than the one in fact obtained if both samples were in fact drawn from the same distribution (that is, if there were in fact no effect). In other words, how likely is it that one would see an effect that big or bigger by chance? If this probability is sufficiently low (say, less than one chance in 20), then one is justified in concluding that what one did had an effect (or that there was a difference in the average values in the populations that one drew the two samples from). Thus, if the difference in the means of the two samples was sufficiently greater than expected by chance, then one was justified in concluding that something more than chance was at work. This way of framing the problem has come to be called Null Hypothesis Significance Testing (NHST, for short). In this formulation, you cannot prove the null hypothesis, because failing to reject it is not the same as accepting it--anymore than a verdict of "not proven" is the same as a verdict of "not guilty." Thus, if you think this is the right way to formulate the problem of probabilistic inference, then you cannot prove the null.

But this way of formulating the problem flies in the face of common sense. We all draw a strong and clear distinction between "not proven" and "not guilty." In this formulation, the only possible verdict as regards the null hypothesis is "not proven." This would perhaps be okay if null hypotheses were of no scientific or practical importance. But, in fact, they are of profound scientific and practical importance. The conservation laws, which are at the foundation of modern physics, are null hypotheses; they all assert that "under no circumstance does this change." And, for most people it matters whether a generic drug costing 1/10 the cost of a brand drug really has "the same effect" as the brand drug or just "has not been proven to have a different effect." If you frame it in the latter way, then many more people will opt to pay the additional cost than if you say that "the evidence shows that the effects of the generic drug and the brand drug do not differ" (a null hypothesis).

Moreover, and this is more technical, the traditional formulation violates basic mathematical/logical considerations. One of these is consistency: a rational treatment of the evidence for and against any hypothesis should have the property that as the number of observations "goes to infinity" (becomes arbitrarily large), then the probability of drawing the correct conclusion should go to 1. But, under the NHST formulation, when the null hypothesis is true, the probability of rejecting it remains .05 or .01 (whatever one regards at the crucial degree of improbability) no matter how many observations there are. Another curious aspect of the traditional formulation is that it licenses concluding the one's own (non-null) hypothesis is correct because the null hypothesis appears to fail, even though one's own hypothesis is never tested against the data, whereas the null hypothesis is. This is a little bit like concluding that one could oneself climb a formidable mountain just because someone else has failed to climb it. Fairness would seem to require that one's own hypothesis, whatever it may be, should undergo the same test that the null hypothesis has undergone. At a somewhat simpler level, How can a statistical method for drawing conclusions be valid if it prohibits ever drawing a conclusion in favor of some hypotheses (null hypotheses) that are of fundamental scientific and practical importance?

There is an alternative to the NHST formulation of the problem of probabilistic inference that dates back to the work of the Reverend Thomas Bayes in the 18th century. In the Bayesian formulation, both the null hypothesis and one or more alternatives to it are tested against the data. In this formulation, each of the hypotheses places a bet on where the data from the experimental treatment (or the other condition of observation) will fall. The hypothesis that does the best job of anticipating where the data in fact fall obtains the greatest odds of being correct (or, at least, more valid than the alternatives to it that have been proposed). In this formulation, it is perfectly possible for the null hypothesis to be the odds on favorite. In other words, in this conception of how to do probabilistic inference, it is possible to prove the null in the sense that the null may have arbitrarily greater odds as against any of the proposed alternative to it. Thus, in this formulation, the null is no different than any other hypothesis. This approach to the problem of probabilistic inference has gained considerable currency in recent years. According to its advocates, it is the only "normative" (mathematically correct) formulation, because, among other things, it does not prohibit any hypothesis from being accepted, and because it is consistent: as the data (number of observations) become arbitrarily large, the odds that the true hypothesis will be accepted increase toward infinity, regardless of whether the true hypothesis is the null hypothesis or an alternative to it. In short, the Bayesian formulation places the null hypothesis on the same footing as any other hypothesis, so it is just as susceptible of proof as any other hypothesis.

There is no inherent disadvantage with a simple respirometer for what I would assume to be a teaching application. There needs to be an understanding of the difference between teaching and research. A teaching unit is a black box that gives you numbers, a research grade respirometer gives you the actual data from the analyzers. Many manufacturers of teaching and or instructional systems such as Qubit Systems market their products as research grade but they are generally recognized as being strictly for teaching.

Percent Error is the difference between the true value and the estimate divided by the true value and the result is multiplied by 100 to make it a percentage. The percent error obviously can be positive or negative; however, some prefer taking the absolute value of the difference.

The formula is the absolute value of the experimental value (minus) the theoretical value divided by theoretical value times 100.

% error = (|Your Result - Accepted Value| / Accepted Value) x 100

Robert Wadlow died in 1940, and there is no way of knowing if there were taller people off record, before people cared, of in a third world country, but he is the tallest person there is record of.

A Pearson correlation measures the strength and direction of a linear relationship between two continuous variables, ranging from -1 (perfect negative correlation) to 1 (perfect positive correlation). An example could be studying the correlation between the amount of rainfall and crop yield in agricultural research to understand how variations in rainfall affect crop productivity.

Eco is short for ecology or ecosystem, which refer to the relationships between living organisms and their environment. It is also used as a prefix for terms related to environmental protection and sustainability.

"Adiabatic process" refers to processes that take place in a closed system with no

heat interaction with it's surroundings.

"Isentropic process" refers to processes that take place in a closed system with no

heat interaction with the surroundings (adiabatic process) and internally reversible.

This is, no internal generation of entropy, entropy stays constant, which is what is

meant by "isentropic".

We can also say, an isentropic process is one where entropy stays constant, and no

heat interaction of the system with the surroundings takes place (adiabatic process).

Or, an adiabatic process can be irreversible, or reversible (isentropic).

"Halogen" refers to a group of non-metallic elements that include fluorine, chlorine, bromine, iodine, and astatine. Halogens are known for their high reactivity and tendency to form salts when they react with metals.

Geometric symbols are visual representations of geometric concepts, such as shapes, angles, and figures, that are used to convey mathematical ideas and relationships in a concise and precise manner. These symbols help simplify complex geometric information and aid in understanding and communication within the field of geometry.

OK, I looked this up online and couldn't find a very clear definition. From what i found it is the random pattern of homologues at the metaphase plate.. I have no idea what the metaphase plate it either.

The same chemicals as here on earth, in varying amounts. Our bodies are carbon, hydrogen , oxygen... the planet itself has many rocks eg limestone, which is calcium carbonate , and a lot of iron, for example the core is iron. The sun is helium and hydrogen for the most part. The moon is made of the same stuff as earth which is part of the evidence that leads us to believe it was formed by a large meteorite smacking into the early earth. Venus is a rocky planet with an atmosphere of carbon dioxide adn sulphuric acid. Jupiter is made of hydrogen, helium and ammonia. All the elements that exist have been formed by reactions inside stars that have lived and died, spreading their mass all over the universe when they go supernova. Older stars contain heavier elements (ie further down the periodic table).

In a certain city the mean price of a quart of milk is 63 cents and the standard deviation is 8 cents. The average price of a package of bacon is $1.80 and the standard deviation is 15 cents. If we pay $0.89 for a quart of milk and $2.19 for a package of bacon at a 24-hour convenience store, which is relatively more expensive? To answer this, we compute Z-scores for each: