Experimentand Analysis of the Results
Thisis an experiment conducted to determine the factors that affect thedrying of the clothes by two different dryers. The factors are variedaccordingly to closely monitor any notable changes. Also, the twotypes of dryers vary in their sizes which implies that they havedifferent capacities in the clothes that they hold. Final evaluationof the appropriate dryer will then be done by evaluation of theresults. For instance, the time taken for each dryer to dry theclothes can be crucial in determining the appropriate dryer amongstthe two. In addition, the capacity of the dryer can also be crucialand this is with respect to the cost incurred in each case.Typically, changing the different factors of interest bringssignificant changes in the final results obtained. Depending on theresults obtained, we will finally draw a conclusion and a plot arecommendation that will state which one among the two dryers is themost efficient and appropriate.
Thisis a description experiment involving two dryers, the cloth dryersagainst the time taken for each cloth to be dry. Type A with smallcapacity and type B with large capacity. In this case, there arethree types of cloth loads, high, medium and low. The primary goal ofthe study is to investigate the trend of the time taken with thevariance in temperature on cloth loads. Analysis of the results ofthis experiment has also been conducted in the most appropriate waysso that the comparison of the two dryers can be meaningful. Graphicalmethods at different capacities have been useful to analyze theefficiencies of the dryers under various conditions. In most cases,the observation of time taken for both the dryers to dry the clothesunder different conditions of temperature has been made. This makestemperature on of the most critical factors for this experiment.Also, the density of the clothes to be dried is another importantfactor that is varied over time so that the effect of the density ofthe clothes of drying them using the dryers can be observed overtime. Typically, in this experiment, there are two factors withdifferent levels that make factorial analysis to be an effectivemethod for the evaluation of the results of this experiment(Kulkarni,et al. 2015).
NullHypothesis- Lower time is taken when the clothes load is low, and thetemperature is high.
Alternativehypothesis- Longer time is taken when the cloth loads are high, andthe temperature is low (Naboulsi,2015).
Sincethe data collected entails smaller sample sizes with twocharacteristics of interest, the categorical data, and quantitativedata. Thus, the randomized test sequence is applied in the collectionof the data. Besides, it is crucial to note that the time taken todry the clothes was collected using Minitab to ensure accuracy is ofkeen interest. This methods are most appropriate for the collectionof data in this case because of the complex design of the experimentthat needs such methods of collecting data to ensure accuracy.
StatisticalAnalysis of data
Thisis a graph of time taken against cloth load. According to the chart,for the case of type A, when the clothes load is small, lesser timeis taken to dry the cloth. When the cloth load is medium, more timeis taken for dryer A to dry the clothes. In the case of high clothesload, a longer time is taken for the dryer A to dry the clothes. Thisimplies that as the clothes load increases in density, more time istaken to dry the cloth.
Regardlessof the clothes load, determining the time taken for the dryer A todry the clothes, temperature increase also decreases the time takenfor the dryer to dry the clothes. This has the implication that thetemperature is inversely proportional to the time is taken for thedryer A to dry the clothes. In other words, lower temperatureincreases the time taken for dryer A to dry the clothes. As thetemperature increases to medium, relatively lower time is taken fordryer A to dry the clothes. The least time is taken for dryer A todry the clothes when the temperature is high. Since an increase intemperature decreases the time for dryer A to dry the clothes.Besides, the increase in clothes load leads to an increase in timetaken for dryer A to dry the clothes, we can conclude that thereexists a relationship between temperature and clothes load. In thiscase, the temperature is inversely proportional to clothes load.Additionally, it is crucial to note that high clothes load requireshigh temperature for the dryer A to dry the clothes. As the clothesload gets lower, a lower temperature is needed for dryer A to dry theclothes.
Factorialanalysis was crucial since it helped us to integrate the variableswith two factors for three levels. For us to achieve this, the twofactors are compared by combining the three different levels to anincomplete factorial design so that the number of levels can becompared to both factors. It is an appropriate analysis techniquesince the different levels of each factor needs to be observedindependently, and this gives factorial analysis an upper hand inthis experiment. In addition, it is crucial to note that varioussimilar observations have been made in this case, and the resultsgive an illustration of the most recurrent. Therefore, factorialanalysis has been crucial in the analysis of the data of thisexperiment.
Inthe graph above, in higher temperature there is a lower cumulativetime taken for dryer B to dry the clothes. Also, when the temperatureis low, more time is taken for it to dry the clothes. Besides it isnoted that the time is relatively lower at medium temperature. Thishas the implication that regardless of the nature of the clothesload, temperature increase decreases the cumulative time for thisdryer to dry the clothes. Therefore, change in temperature plays acrucial role in the rate at which the clothes dry and this is evidentfrom the graph three above.
Inthe case of the nature of the cloths load, lower cloths load leads toless cumulative time taken for Dryer B to dry the clothes. It’sseen that as the clothes load increases, the time that is taken forthe dryer B to dry the clothes also rises. It can then be concludedthat when the temperature is high, and the clothes load is low thelowest time is taken for dryer B to dry the clothes. Also, when thetemperature is lower, and the clothes load is high, the longest timeis taken for dryer B to dry the clothes. This implies that lowerclothes load requires a lower temperature to dry.
Insummary, there is a credible variability between the two factors (A),small capacity and (B), large capacity.There is a relatively largeamount of time taken for dryer A than in dryer B by inspection. Inthis case, B is the best compared to A. We can confirm this bycomparing the cumulative time taken for each factor regardless of thelevels i.e. when taking an assumption that the levels for each factorare uniformly distributed. It is also important to note that thereare two factors in this experiment that are of keen interest. Theyare temperature and the cloth loads that are being investigated todetermine the time taken for the clothes to dry.
Mostof the time consumed by small capacity dryer is a lot. Therefore, ascompared to B it is economical if the large capacity is of merit. Therate at which type B dries high clothes load is higher compared tothe rate in A. Assuming that time is the major factor over levels ofclothes available, and dryer B becomes the most efficient (Colombo,et al. 2014).
However,if the temperature available ranges between lower to medium, then thelower capacity type is appropriate and, therefore, more efficient.This is because it is easy to work with high-level clothes load henceconsiderable. Factorial design allows for estimation of experimentalerror. They can be used when there are more than two levels of eachfactor. However, the number of experimental runs required for threefactorial designs with considerably greater than that of the twolevel counterparts (Tamburro,2016).In this cases, it is common only to run a single replicate of thedesign and make assumptions that factor interactions of more than aparticular order are negligible (Naboulsi,2015).
Sincehigh capacity dryer takes overall relatively less time than thattaken by low capacity, type B is more appropriate. It can requireless manpower since time is less and it is fast. The factorialexperiment can be used when there are more than three levels for eachfactor. It is relatively easier to estimate the main effect for afactor. Therefore in most cases where there is a number of levelscorresponding with factors, the ANOVA analysis is appropriate. Inthis experiment, the factorial analysis was crucial in the evaluationof each factor independently and breaking down of larger observationsto fewer results so that it could ease the analysis (Colombo,et al. 2014).From a keen observation of the results, it is evident that type Bdrier is much cost effective and efficient too. Therefore, I wouldrecommend one to use type B drier instead of drier A. This is evidentthroughout the essay from the arguments that have been made in eachcase. Observations have been made in both the dryers under differentconditions that have been varied to note any change in the timetaken. Type B dryer is effective since it can accommodate a largernumber of clothes and it takes a shorter time to dry the clothes.
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