The Wavelength Of The Beam Separation

Answer = The first part of the graph resembles a bell shape. At the latter part of the graph it takes a sharp increase.

However the inconsistencies in the data were that we had designed a fair test and therefore we could only test on modification at a time. One inconsistency in the data depended on which person in my group wound the lever and string which propels the mousetrap car to go down the runway this causes an icosistinsy in the data as each one of us will whines the string on the mousetrap a different way therefore causing an inconsistency in the data.

The concentrations and absorbances of the red and blue dyes were used to find the concentration of the purple dyes. From the graph of the blue dye, the linear equation for absorbance was y = mx + b. From that formula came the equation y = 7.915 x 104 (x) + 0.02489, where y represents absorbance, m is slope, x is concentration/molarity, and b is the constant/y-intercept. The same set up was performed for the red dye, but the equation produced was y = 1.045 x 104 (x) +.001298. The equations found when graphing absorbance vs. concentration were used to find the concentration of the purple dyes. The absorbance for purple dye 3 on the red wavelength of 470 nm equaled 0.149 and 0.818 for the blue wavelength of 635 nm. For purple dye 1

• Secondly, we also used a colorimeter which is extremely accurate when it comes to measuring the percentage light transmission. As it measures the percentage light transmission as a numerical value.

From this graph and chart we can see that the higher the concentration the higher the absorbance, all the different concentrations were tested at the same wavelength (625nm). Also we can determine our unknown substances concentration by using the absorbance we got for it. The red dot on the graph followed by the line towards the horizontal axis indicates that the concentration of fast green was 34% or 5.1x10-3.

In such a long term, a snowball effect will probably incur on the fair value estimates based on small changes in assumptions, making the estimates inaccurate and meaningless.

2. The Impression in data: Larger the cell less is the error, smaller the cell more is the error.

As a group we encountered several possible errors throughout the experiment. Blunder error is a common error than can occur.

Attending to the average accuracy results, the highest accuracy obtained by the Hassanat distance which achieved the first rank in all noise levels, followed by Lorentzian distance. The Lorentzian distance achieved the second rank only at level noise 10 %, 30%, 40%, 50%, 80% and 90%, but at level noise 20%, 60%, and 80% outperformed by Canberra distance and at level noise 40 %, it outperformed by Manhattan distance, where these distances, Canberra and Manhattan achieved the second rank in theses level of noise. The Canberra distance achieved the third rank at a level of noise 80% and 50%, 40%, 30% and 10 %. This distance at level noise 10 % was equivalent to the Square chi-square distance in the result. At a level of noise 90%, the

Some potential issues for my data analyse would be outliers because they could mess up the final results and give me a different result from everyone else. If this were to happen, I will find the outliers and then either highlight them so I can see them and not put them in my final result or I will replace them with extra data I collected in case this were to happen. I could also be given the same results from the random number generator, as they don’t monitor what numbers are given. The extra data I collected when getting my sample could then replace one of the same results.

The accuracy of this experiment cannot be commented on as there is no ‘true value’ to compare the gathered results with however, if more results were to be collected the accuracy would increase. The precision of the experiment can be interpreted through the range of the values (min and max), as shown in the fig 4 the gap between the minimum and maximum is large which leads to believe that the data was low in precision. The validity of the experiment was moderate as the investigation carried out measured what it was supposed to measure however, not with the amount of accuracy that was

The height and width of this optotype is designed to be five times the thickness of the stroke width and gap width, therefore the size of each critical detail is 1/5th of the overall height (Kalloniatis & Luu, 2005). Four stimulus sizes were used in the experiment as visual acuity was to be tested at four eccentricities (0˚, 20˚, 40˚ and 60˚). These four stimulus sizes were calculated using the approximate minimum angle of resolution (MAR) of each eccentricity obtained from Millodot et al.’s (1975) normal data. This value was then used to find the gap size and hence the stimulus size using a distance of 600cm. The reason for using the distance value of 600cm was because it is close to the threshold and therefore would be the starting point of stimulus presentation for each of the eccentricities. The formula used was:

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.

The fact that the reconstructed solutions using the replicated data (figure 6.8 (a)) moved away from the true profile as the levels of data error are increased, and still continued to show reduction in objective function also suggests that the algorithm might have been trapped in local minimum. This means additional priori information may need to be incorporated within the algorithm to constrain the solution around the true profile, in particular in the situations where the replicated calibration error is more than 2% of the original data.

With today’s science, light becomes more specifically perceived as a spectrum. The specific name for this spectrum is the electromagnetic spectrum, which contains many types of waves (NASA, Electromagnetic Spectrum). In Figure 2, the spectrum shows a variety of wavelengths with specific wavelengths classified by its length. The range of wavelengths humans can see wavelengths that are around 400 – 700 nanometers (nm) as colors while all other electromagnetic waves are simply blind to humans. The interesting classes for light that are used to measure are microwave and infrared light. The laser is