Sentences with phrase «see changes in variable»

For business and law school students, they can expect to see changes in variable interest rates on private student loans, but not fixed interest rates.

Not exact matches

Operators are facing four big areas of challenge that Technomic sees as transformative, bound to drive changes in how operators approach business: 1) coping with supply chain challenges, including driver shortages; 2) meeting consumer demand for «food with integrity»; 3) dealing with «regulation nation» where industry - disrupting changes may include a higher minimum wage; and 4) incorporating innovations into operations, including new delivery models, variable pricing, self - ordering systems, and robotics.

In their conclusion, the authors state that it is important for conservationists to see climate change not as one of numerous independent variables acting on species survival in mountain landscapes, but as an exacerbating force over the many direct human alterations to these areaIn their conclusion, the authors state that it is important for conservationists to see climate change not as one of numerous independent variables acting on species survival in mountain landscapes, but as an exacerbating force over the many direct human alterations to these areain mountain landscapes, but as an exacerbating force over the many direct human alterations to these areas.

While in some instances this feedback is easy to see — changes in marine life after a water pollution event, for example — in other instances it can be subtle, variable and subject to distortions, which can include political motivation or bias.

«Once you dramatically change the climate and change water patterns, as is now seen in the Three Gorges region,» he says, «you change a lot of environmental variables.

«I was surprised to see positive changes in several of the variables at most sites,» Lawrence says.

Nearly every paper that I have seen recently that has indicated a meaningful change in rate for a variable related to warming has suggested that, if anything, average model sensitivity may be too low, with positive feedbacks underestimated.

So here's the lesson, the moral of this story: before we assume that low - carbohydrate diets are just one tool in the dietary arsenal against overweight and obesity, and before we assume that everyone is different and that some of us lose weight and keep it off because we eat less fat (and more carbohydrates) and some because we cut carbs (and so eat maybe more fat), we should make an effort to understand the concept of controlling variables and look to see which variables are really changing and by how much.

«Kink» refers to the change in the slope of the curve representing the correlation between two variables (in this case, family income and college attendance) that one should see in the portion of curve where the hypothesized causal intervention (in this case, the phase out of the tax credit) is operative.

Perhaps the most respected study to date, conducted by researchers Jason Richwine and Andrew Biggs (in which they account for all variables — including the fact that teachers work on average for 180 days, while private industry workers toil for 240 - 250 days) found that workers «who switch from non-teaching jobs to teaching jobs receive a wage increase of roughly 9 percent, while teachers who change to non-teaching jobs see their wages decrease by approximately 3 percent.»

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For those with loans at a fixed interest rate, nothing would change for them, while borrowers with a variable rate would see a change in their monthly payments based on interest rates going up and down.

Nearly every paper that I have seen recently that has indicated a meaningful change in rate for a variable related to warming has suggested that, if anything, average model sensitivity may be too low, with positive feedbacks underestimated.

Ultimately, it would be nice to see followup forward modeling studies which attempt to use all potentially relevant variables, rather than the simplified model form we use (which is generally justified at treeline — but perhaps only to first order approximation, i.e. some of the details of changing hydrology and solar insolatino might still matter in these environments.).

The magnitude and spatial distribution of the high - latitude climate changes can be strongly affected by sea ice characteristics, but evaluation of sea ice in models is hampered by insufficient observations of some key variables (e.g., ice thickness)(see Section 4.4).

Even seemingly «straightforward» applications like the assessment of the impact of changes in the mean annual cycle of temperature on shifting butterfly populations did find more climate variables than just temperature to be of importance (see e.g. WallisDeVries et al, 2011; http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3172409/), and this generally applies to many sectors and applications.

I hardly see how one can engage in field proxy studies without knowing how the dependent variables change with the parameter of interest.

This seems intuitively obvious taking into account the very wide freedom offered by possible changes of variables, and I have seen sufficiently statements that are equivalent to that in high quality sources to confirm that the intuitive conclusion is indeed true.

Because the work is based on modeling at a high level to see how species are impacted in their range, it's hard to account for variables that will ultimately make a huge difference for species trying to survive climate change.

In addition to the basic Verve vest, You Saw Me is also producing an RGB version that can change colors, with users able to choose from 20 steady - state colors and 19 color changing functions (and that can also be dimmed or set to display colors with variable speeds).

Before moving on to how this analysis can be used for pricing there is the obvious caveat: The allocation of expenses between fixed and variable is an important exercise and not a simple one as some expenses will be seen as both fixed and variable — e.g. associates and staff salaries — because they can't always move on a timely basis with changes in revenue.

But you can also see how over time this variable changes when run against that variable for all the countries for which there is data or just for those countries in which you're interested.

(to see even more granular benchmarks, including detailed settings changes and number of other variables in their review,