Weight Scale Estimator App Free Download

Weight Scale Estimator is a cost-free business and productivity tool that allows you to quickly estimate a specific item's density on your smartphone or tablet device. It is a state-of-the-art utility that further expands the usage of your gadget by giving it a digital scale weight power.

There's a weight limit set to avoid screen scratching from happening. More than objects, it also permits you to measure liquid's heft. Keep in mind, though, that the Weight Scale Estimator only enables weighing objects up to 500 grams, 1.1 pounds, or 17.64 ounces.

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With Weight Scale Estimator, you can now instantly determine a load of a specific object or liquid. It's a small suite with a unique sensor that allows you to measure items' weight simply by putting them on top of your gadgets. It aims to be as accurate as possible. However, high precision is not guaranteed as its algorithm optimization is still in the development stage.

Weight Scale Estimator is a valuable download on your smartphone and tablet. This way, you can instantly weigh an object in a solid and liquid form whenever and wherever you need to. It saves you from the hassle of bringing or looking for an actual weighing scale. However, you must first prepare the two necessary items to easily identify a particular object load.

You undoubtedly already know that mobile phones can be used for practically anything... But did you know that they can even function as scales for weighing things? Yes, you can turn your phone into a weighing tool thanks to the app Weight Scale Estimator.

This application is able to turn your phone into a scale that can give you an approximate idea of the weight of small objects. It is not a precisely accurate scale, so forget about using it to prepare your methamphetamine mixes. Its functioning is very simple: all you have to do is follow the instructions by placing your mobile face up on a flat surface and calibrating the weight with the help of a half-liter bottle.

It can measure weights of up to 500 grams (don't try weighing more than that if you don't want to break the screen) and, as well as grams, it supports units of weight like pounds or ounces. You can use this app to find out whether the chicken you were sold really weighs a gram or half a gram, or to calculate the amount of sugar to add to your cakes.

The network scale-up method (NSUM) is a technique designed to generate size estimates for hard-to-reach and hidden populations without having to directly interview or send a survey to a member of the target group. Rather, the estimation process takes advantage of conventional sampling frames to recruit a representative sample of the larger population of which the hidden population is a part. This sample is then asked to estimate the number of people each respondent knows who would fall into the hidden population in question. This approach has two major advantages. First, NSUM methods do not ask respondents about their own characteristics. Stigmatized or hidden populations may be reluctant to disclose their own status due to perceived risk, even in an anonymous survey. Rather, by limiting questions to the anonymous enumeration of contacts, the hope is that second-hand reporting will lessen the burden of stigma on those involved in providing project data. Second, because we do not need to directly interview members of a hidden population, we can return to general sampling techniques such as random digit dialing or addressed based sampling, survey methods that are considerably cheaper and easier to implement and which take advantage of established sampling frames.

In recent years, NSUM techniques have been used to estimate the prevalence of HIV/AIDs [8,9], deaths after an earthquake [10], the size of MSM (i.e. men who have sex with men) populations [11,12], and other hard-to-enumerate populations [13,14]. The increasing number of NSUM projects and the variety of populations/locations where it has been implemented demonstrates the flexibility of the method. Questions remain, however, about both the potential and the limits of the technique. As the method becomes more popular, developing new versions of the estimator and ways to improve the accuracy of their estimates will become increasingly important.

We propose a step in this direction via a new form of the NSUM estimator and a novel, recursive trimming process that is applied to the set of study variables that are used as the basis of the scale-up estimates. Additionally, we demonstrate how to incorporate sampling weights into the NSUM estimation process. Using data collected in 2014 from an address-based random sample of Nebraska, we show how estimates evolve via the iterative trimming of poor performing elements of the estimator. By improving the overall accuracy of the remaining set of NSUM predictors, this recursive process provides more accurate and consistent estimation within the NSUM framework.

Provided these assumptions can be justified, the estimation process for gauging the size of a hidden population can be reduced to a series of questions about the number of persons known to be members of a range of scaling populations and the number of persons they know in the target population(s). When asked of a sample of respondents drawn from a conventional sampling frame, researchers can harness the advantages that come from working in known sampling scenarios, such as conventions around the treatment of outliers, and the weighting of outcomes according to sampling results (both of which were carried out here).

Finding scaling variables of uniform size may be difficult, however, and muting the effect of outliers is not the same as removing them from the estimation process. In both cases, alternatives are available. Toward this end, we discuss an alternative estimator that takes into account the performance of the each scaling variable individually and, allows for the selective removal of those that are performing poorly in comparison with the combination of all others. The new estimator and a comparison of results with the original estimation process are discussed below.

We also propose a way to integrate sampling and post-stratification weights into both of the estimation processes. One of the strengths of the NSUM technique is that it can use mainstream sampling techniques to generate representative samples and thereby accurate estimates of target populations. These types of frames also have the major advantage of having known distributions which can be used to create weights to ensure greater representativeness of the sample. Incorporating these types of weights in to the NSUM estimation process is a logical and much needed addition to the technique. Below we demonstrate how weights can be included into both the original and proposed estimators and compare the differences in the final population estimates.

Though the number of individuals known remains the same, the differential contribution of the elements of the denominator means that the larger target population (10,000), virtually eliminates the fact that here a person from a rare population is known (1/100). Indeed, it would make little difference if she had reported knowing 2 persons in the first population (2/100). Where significant differences exist in the size of the scaling populations (i.e. the denominator), the significance of knowing individuals in smaller populations can make little difference in the estimate of personal network size. Given this, it might make more sense to take the average of the individual scaling variable ratios (Eq 7, here after the mean of sums (MoS) estimator).

Which of these is a better ordinary method for estimating personal network size remains an open question. Below we show that the MoS estimator performs far better in the recursive trimming process, especially when weights and removal of outliers are incorporated as well.

Table 1 shows the differences in population estimates of our target populations using the original estimator (Eq 3 above) and one derived from an estimator that incorporates the MoS method (Eq 9) for three populations in Nebraska.

Differences between these two estimators vary depending upon the target population being estimated. The estimate of the number of people who moved to Nebraska from another state in the U.S. in the previous 2 years increased by a factor of 6 to 75,800 with the MoS estimator. The American Community Survey reports that in the year 2013, 45,854 Nebraskans reported living in a different state 1 year ago [20]. When added to the same report for 2012 of 43,266 people moving into Nebraska [21], this provides a 2 year total of 89,120. The MoS estimate of 75,800 is considerably closer to the 2 year ACS total than the estimate of 12,184 provided by the original NSUM formula, although the 95% confidence intervals for neither estimate contain the ACS statistic. The Internal Revenue Service also compiles state-to-state migration data, but they have not yet released data for 2012 or 2013 at this time. Once that data is available it will provide an additional benchmark to test our estimates against. In addition to an increase in estimate migration into Nebraska, the MoS method also estimates that 22,614 Nebraskans do not approve of interracial dating compared to an estimate of 17,892 Nebraskans from the standard formula. The estimate for the number of Nebraskans that have used heroin in the last 30 days increases from 368 to 454. Not all of the differences in our survey were stark. Overall, of the 46 populations we estimated in the larger study, 76% of them changed by less than a factor of 2 using the MoS estimator compared to the original estimator.

A key indicator of how the results change is shown by the differences in calculated personal network size between the two estimation methods. The average size of the calculated personal network size increases considerably, as does the standard deviation, when using MoS. Perhaps the most dramatic change is the maximum personal network size calculated by both formulas. The largest network size under the MoS estimator is 16,794 while the original estimator peaks at 5,944. 2351a5e196