The value to be used for updating the estimated value has been chosen as 181 (the value of range of each subgroup) for the first iteration of the algorithm. The value of an intermediate data has been taken as the range of each subgroup/ number of subgroup (181/6 = 30.16). That intermediate value 30.16 has been chosen as a change in temperature for the second iteration of algorithm. For third iteration, the value (30.16/6) 5.02 has been considered as a change in temperature. Similarly for the next iteration the value has been chosen as (5.02/6) 0.83 and so on. The value encoding is considered as the data encoding and the (1/estimated error) of the data with the actual data is considered as a fitness function
Step 2
The least square technique based on linear, exponential, asymptotic, curvilinear and logarithmic equations has been applied on the available data to produce the estimated data. The error analysis has been made to produce estimated error. It has been observed that average error based on least square technique based on linear equation has shown the minimum error (2.25%) as compared to the other models according to table 2. Therefore least square technique based linear equation has been chosen as the best known solution.
Step 3
The updating of estimated data has been made based on the process of simulating annealing algorithm. Initially, in the simulating annealing algorithm, high temperature values have to be considered for the material and decrease in the
Finally we got all our number and determine the slope, and the intercept in order to find out the forecast for the next
For the engine cost, there is also a positive correlation thus; increase in this cost may also vary in the increase in average age of fleet per hour. However, on this cost, only 61% is determined in the regression equation. Like in the airframe cost, there will be additional 2.6 in cost for every hour of average age in thousands.
13. Calculate the change in temperature for the water caused by the addition of the aluminum by subtracting the initial temperature of the water from the
Level 1 :As per the Considered Data Sets: (Generated nearby values through Rough Data Sets Theory Produces)
4) Use cubic regression to determine an equation for the data (or lwh where (12 – x) represents the sides and (x) represents the height of the box).
For the first five data point, the value of exponential model is close to the actual value. However, the exponential model didn’t work well for the
The total sum of squares is 400 and the sum of squares errors is 100, what is the coefficient of determination?
Many years ago, acinent people used fur to protect their bodies from hurm and keep warm in winter. They used animals’ skin to make the earliest fur while they were living in caves. A lot of animals can be the materiasl of fur, such as rabbit, fox, mink, beaver, ferret, otter, sable, seals, cats, dogs, coyotes, chinchillas and opossum. Nowaday, the values are excatly different in different materials. However, the acinent people did not really care. It was hard enough for the people who did not have high rechonology and firearms to live, the forsets always fulled of the carnivores. However, some leaders of clans found that there were some carnivores much more pricous than the Herbivores. They decided to use the fur of the lions or tigers to be
Miller (1994) suggests that species value cannot be properly assessed until a species economic value is established within a community. Economic value can be focused on two main categories: use values and non-use values. Use values can further be dived into direct and indirect use values. Direct use defines what resources a particular organism can be used for alone or systematically while indirect use refers to the benefits that are attributed to the organism when left in it’s natural habitat. There are currently no record of harvesting or selling that can be listed as a direct use value for Sorex hoyi.
A linear formula idea will be used and the decision variables will be labeled as follow:
The topic is growing Borax crystals at certain temperatures.The scientist’s problem is that they are trying to figure out at what temperatures they grow best.The data that is being collected is crystal size (in centimeters).The variable being changed in the experiment is the temperature (in degrees) that the crystals will be grown in. In all of the experiments,the same jar, pencil, string and thermometer will be used.
The line of best-fit is used to find the gradient, the T2/L value, if straight or linear it shows that the relationship between the two is directly proportional. Using the original equation, you can square both sides and rearrange it to make . Then you can input the gradient value (T2/L) and work out g. , where g equals 10.13 m/s2. This value is close to the
To start with, the 1st model used is regression line method. According to this method, the technique fits a trend line to a series of historical data point and the projects the line into the future for medium to long range forecasts
To find the effect of temperature on the activity of an enzyme, the experiment deals with the steps as follows. First, 3 mL if pH 7 phosphate buffer was used to fill three different test tubes that were labeled 10, 24, and 50. These three test tubes were set in three different temperature settings. The first test tube was placed in an ice-water bath for ten minutes until it reached a temperature of 2° C or less. The second tube’s temperature setting was at room temperature until a temperature of 21°C was reached. The third tube was placed in a beaker of warm-water until the contents of the beaker reached a temperature setting of 60° C. There were four more test tubes that were included in the procedure. Two of the test tubes contained potato juice were one was put in ice and the other was placed in warm-water. The other two test tubes contained catechol. One test tube was put in ice and the other in warm water. After
The trendline, known as the line of best fit or the least squares regression line, shows the linear equation which best explains the sums up the data’s trend. The formula on the right is the formula of the line of best fit.