Lab Report

The mole is a convenient unit for analyzing chemical reactions. Avogadro’s number is equal to the mole. The mass of a mole of any compound or element is the mass in grams that corresponds to the molecular formula, also known as the atomic mass. In this experiment, you will observe the reaction of iron nails with a solution of copper (II) chloride and determine the number of moles involved in the reaction. You will determine the number of moles of copper produced in the reaction of iron and copper (II) chloride, determine the number of moles of iron used up in the reaction of iron and copper (II) chloride, determine the ratio of moles of iron to moles of copper, and determine the number of atoms and formula units involved in

Chapter25. When establishing the classes for a frequency table it is generally agreed that the more classes you use the better your

I prefer to have in my sampling accuracy as my result could be more close to the true value.

One of the most popular methods for solving the class imbalance problems is sampling. The most used sampling techniques are undersampling and oversampling. In undersampling, instances of the minority and majority classes are selected randomly in order to achieve a balanced stratified sample with equal class distributions, often using all instances of the minority class and only a subset of the majority class, or undersampling both classes for even smaller subsets with equal class sizes. Alternatively, in oversampling, the cases of the under-represented class are replicated a number of times, so that the class distributions are more equal (Crone and Finlay, 2012). Mujalli et al. (2016) indicated that using balanced datasets, especially those created using oversampling techniques, improved classifying a traffic collision according to its severity and reduced the misclassification of the minority class instances.

In case of a polychromatic beam, the HC is less than one because of beam

This discrepancy also meant that the sample was unrepresentative of the population. Convenience sampling also allows the opportunity for bias to affect the results. Future research could look at a larger more representative sample to overcome this.

CBCT images and found that the use of this index in CBCT images was valid;(101) however, the index obtained from CBCT images did not compare well with that derived from panoramic images, the imaging modality for which the classification was originally devised. Moreover, the CBCT analysis was conducted in templates, i.e., static slices. These aspects could have influenced the results (101).

The dashed line in the center demonstrates the choice edge of classifier. The regions set apart as FN and FP speaks to the inaccurately grouped items.

As aforementioned, the survey poll had a oversample, which means that at times pollsters take additional samples of a sub-population over the original portion of the main sample with the intent to reduce variances of important statistics of a target sub-population. Under those circumstances, the more interviews are conducted the less potential random sampling error should be encountered. Moreover, before tabulating the data for the full sample, pollsters

2 Classification Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Threshold both train and test data to a minimum value of 20, maximum of 16,000.

This is also as preparation of the next step where the histogram will be divided into two regions based on its average value. The stretched-histogram will provide a better pixel distribution of the image channels and thus gives a more accurate average value of the channel which represents the average value of the channel for the whole dynamic range. The equation (6) is used to stretch the histogram of respective color channel to the whole dynamic range. Pin and Pout are the input and output pixels, respectively, and imin, imax, omin, and omax are the minimum and maximum intensity level values for the input and output images,

approaches. Oversampling [26] increases redundancy of data and undersampling results in loss of information. In algorithmic approach, classifier design

Due to its randomness, “freak” results can sometimes be obtained that are not representative of subgroups in the population. In addition, these results may be difficult to spot. Increasing the sample size is the best way to eradicate this problem.

* Error in data collection may be there to some extent and this may lead to inaccurate results.