Then we counted the number of 3x3 unit squares, we found 4. We found each of there by starting from each corner of the Figure 1. One 3x3 unit square can be found shaded in Figure 1d. Figure 1d (3 x 3) Lastly, we counted Figure 1 as one whole 4x4 unit square, shaded in Figure 1e. When figuring out how many squares were in Figure 2, we first noticed that the first four rows represent Figure 1; therefore, we did not recount since we previously figured out there were forty squares in Figure 1. We then
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This week’s programming assignment on how to sort fruits and arrange them in alphabetical order was not easy to do. It took me to do many examples and readings to understand the code in looping structure that is for loop, in the while loop. I have to run module several times, back and forth and carefully observe and do many examples to be able to arrange all the fruit in alphabetical order. What I have learnt so far programming is that, no matter what you code if you do not give the right codes
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1. For the first part of this assignment I am going to use exp(r*t) to solve for the size of a future population. 10,000*e^(.01*10)=11,051 so the population after 10 years is 11,051. 2. I will be repeating the process but using a different number for time. 10,000*e^(.01*20)= 12,214 so the population after 20 years will be 12,214. 3. Next I will repeat the process but with a different growth rate. 10,000*e^(.05*10)= 16,487 3.1 10,000*e^(.06*10)= 18,221 4. While graphing my information I noticed
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Q3. How does increased loading change L, W, and t? When F doubles from 4.0 N to 8.0 N for the small rubber band, does the deformation, L-L0, also double? When F doubles from 4 N to 8 N for the large rubber band, does the deformation also double? As the load increases, L increases and W and t decrease. From 4 N to 8 N, the deformation does not double; it doubles from 1N to 4N, and from 2N to 15N. The deformation rate slows as more force is applied. From 4N to 8N on the larger rubber band, the deformation
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VI) Discussion After reviewing the completed data charts after the experiment, some of the results supported the original hypothesis, but there was also evidence from the experiment that supported the idea that the recessive gene can also be strong throughout generations, and somewhat refuting the claim. The frequency table showed that the thumb, earlobe, and hairline all have the dominant trait more frequent. The dominant form frequency for earlobe was 65%, for the thumb was 75%, and hairline had
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1. In range of data, data in the cells are not structured, they can be specified by the block of cells. Also the name of the table is manually done and not automatically set. In the structured range data, data is organized in columns and fields. The names of the table can be automatically set, and table headers can also be automatically set with drop down boxes. 2. A field is represented by the columns and a records are represented by rows. A field is single unit of data that is unique within
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This means that the probability of missingness is not related neither to the recorded variables of the data nor the unobserved variable $Z$ itself. In other words, the behavior of $M_{Z}$ is statistically independent of all remaining variables of the data. For example, if the participants throw a coin to decide if they will answer a question or not, the missing item is MCAR. The independence of $M_{Z}$ from the missing values $Z$ cannot be tested because we do not have access to the values which
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Incidentally this was difficult for me to understand. I think I get it but correct me if I don't. 1. For what size of problem is algorithm A faster an B Algorithm A: 1000n³ Algorithm B: 2^n Javanotes.com states "the larger the exponent, the greater the rate growth of the function. Exponential functions such as 2^n and 10^n, where the n is in the exponent, have a growth rate that is faster than that of any power function. In fact, exponential functions grow so quickly that an algorithm whose
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CHEM 1310 Laboratory Expt. 8: Rainbow in a Test Tube Data & Results: A. Measuring Miscibility of Unknown Soltutions Table 1. Miscibility Test Mass of Solution (±0.001 g) Volume (±0.05 mL) Density (g/mL) DIW 0.632 0.60 1.0533 LIG 1.466 1.20 1.222 FIF 1.292 1.00 1.292 HEA 1.460 1.10 1.327 EAC 1.140 1.20 0.950 DCB 1.325 1.00 1.325 B. Measuring Density of Unknown Solutions Table 2. Density Test Number of layers Polarity DIW 1 Polar LIG 1 Polar FIF 2 Nonpolar HEA 1 Polar EAC 2 Nonpolar DCB 2 Nonpolar
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This is a part to evaluate the storyboard produced in question 2a and question 2b according to Nielsens Heuristics. Question 2a is creating a new order record and stores it in the storage of order record. Question 2b is loop of the records to enable user to fill or skip the order record. Visibility of system status Users are well-informed after every step that they made. For instance, when users reached the graphical user interface to input some details such as order number and customer id, there
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