Business Decision Making Assiment Essay

You should sign this sheet to show that you accord with these regulations. Students Signature Date Ac have sexledgement I take this chance to give thanks Miss. M. PriyanthimalaWho helped me to improve and developed this particular project. She explained well slightly the project and sacrificed her nearly of the quantify to explain and to a fault made certainly that every last(predicate) the students understood. She was ready to help out in any time and gave her full support for this particular project.I finally would like to thank my parents, friends and some others for helping to do this project. Thank you TASKS PAGE NO tax 01 04 chore 02 09 labor 03 14 assign 04 16 labour 05 24 Task 06 27 Task 07 31 Task 08 32 Task 09 34 Task 10 35 Task 11 38 Task 12 43 Task 13 44 Task 14 47 Task 15 49 Reference 51 Task 1 T 1. 1 Difference between a ingest and a commonwealth Population Sample * Population is the area in which you are trying to get info from. * This fuddleding of nation is also used in survey research, but this is however unitary of many a nonher(prenominal) possible definitions of people. Examples Cedar Crest students trees in North America automobiles with quadruple wheels people who consume olive oil. * Sample is a section of your population that you are actually going to survey. It is important to have a model that will represent your entire population in order to belittle biases.Survey research is based on taste, which involves getting information from exactly rough members of the population. * Samples can be drawn in several diametrical ways, a great deal(prenominal) as probability experiments, quota samples, purposive samples, and volunteer samples. Examples assuming the populations verbalize above 47 Cedar Crest students chosen haphazardly 8463 trees hit-or-missly selected in North America 20 sample autos from each gull (e. g. , GM, Ford, Toyota, Honda, and so on ) 1% of the oil consuming population per countr y T 1. 2 Describe the advantages of sample distribution * Samplingsaves m iodineyas it is oft cheaper tocollectthe desired information from a smallsamplethan from the upstanding population. * Samplingsaves a lot of time and energy as the choose data are collected and act uponed much faster than number information.And this is a very important consideration in all types of investigations or surveys. * Samplingprovides information that is al to the highest degree as accurate as that begined from a complete census rather a properly designed and cautiously executedsamplesurvey will provide much accurate results. More all over, owing to the reduced volume of extend, persons of higher caliber and properly instruct can be employed to analyze the data. * Samplingmakes it possible to obtain more detailed information from each unit of thesampleas collecting data from a few units of the population (i. e. ample) can be more complete and thorough. * Samplingis essential to obtaining the data when the eyeshadement attend tophysicallydamages or destroys thesamplingunit underinvestigation. For example, in order to measure the medium lifetime of argus-eyed bulbs, the measurement process destroys thesamplingunits, i. e. the bulbs, as they are used until they burn out.A manufacturing business will therefore use only asampleoflight bulbsfor this purpose and will not burn out all the bulbs produced. Similarly, the hearty pot of soup cannot be tasted to determine if it has an acceptable flavor. Sampling whitethorn be the only hatefuls available for obtaining the needed information when the population appears to be infinite or is inaccessible such as the population of mountainous or thickly forested areas. In such cases, victorious $ complete census tocollectdata would neither bephysicallypossible nor practically feasible. * Samplinghas much smaller non-response, following up of which is much easier. The term non-response call backs the no availability of informat ion from somesamplingunits included in thesamplefor any solid ground such as failure to locate or measure some of the units, refusals, not-at-home, etc. Samplingis extensively used to obtain some of the census information. * The most important advantage ofsamplingis that it provides a valid measure of dependability for thesampleestimatesand this is one of the ii basic purposes ofsampling. * Reliability If we collect the information about all the units of population, the collected information may be admittedly. save we are never sure about it. We do not know whether the information is true or is completely false. Thus we cannot verbalize anything with dominance about the quality of information. We say that the reliability is not possible.This is a very important advantage of sampling. The inference about the population parameters is possible only when the sample data is collected from the selected sample. * Sometimes the experiments are done on sample basis. The fertilizers, t he gather inds and the medicines are initially tested on samples and if found useful, so they are applied on large scale. Most of the research work is done on the samples. * Sample data is also used to crisp the accuracy of the census data. T 1. 3 Difference between immemorial data and secondary data T1. 4 Difference between a statistic and a parameterParameter is any characteristic of the population. Statistic on the other hand is a characteristic of the sample. Statistic is used to estimate the revalue of the parameter. billhook that the value of statistic changes from one sample to the next which leads to a study of the sampling distribution of statistic. When we draw a sample from a population, it is just one of many samples that might have been drawn and, therefore, observations made on any one sample are likely to be different from the true value in the population (although some will be the same). judge we were to draw an infinite (or very large) number of samples of ind ividuals and calculate a statistic, say the arithmetic mean, on each one of these samples and that we then plotted the mean value obtained from each sample on a histogram (a chart utilise bars to represent the number of times a particular value occurred). This would represent the sampling distribution of the arithmetic mean. T1. 5 Define sampling errors with example? Sampling error is an error that occurs when using samples to make inferences about the populations from which they are drawn.There are two kinds of sampling error random error and bias. Random error is a pattern of errors that tend to explode one another out so that the overall result salve accurately reflects the true value. Every sample design will get down a certain amount of random error. Bias, on the other hand, is more serious because the pattern of errors is loaded in one direction or another and therefore do not balance each other out, producing a true distortion. These are the errors which occur due to the nature ofsampling.Thesampleselected from the population is one of all possible samples. Any value mensural from thesampleis based on the sampledata and is calledsamplestatistic. Task 2 T2. 1 Advantages and disadvantages of arithmetic mean. Advantages * Fast and free to calculate- As the most basic measure in statistics,arithmetic fairish is very easy to calculate. For a small data set, you can calculate the arithmetic mean quickly in your head or on a patch up of paper. Incomputer programslike Excel, the arithmetic mediocre is always one of the most basic and best known functions.Here you can see thebasics of arithmetic average calculation. * Easy to work with and use in foster analysis- Because its calculation is straightforward and its meaning known to everybody,arithmetic averageis also more comfortable touse as input to further analyses and calculations. When you work in a team of more people, the others will much more likely be familiar witharithmetic averagethan nonrepr esentational averageormode. Disadvantages * Sensitive to extreme set- arithmetic average is exceedingly sensitive to extreme values.Therefore,arithmetic averageis not the best measure to use with data sets containing a few extreme valuesor with moredispersed (volatile) data setsin general. Mediancan be a better alternative in such cases. * Not qualified for time series type of data- Arithmetic averageis perfect for measuring rod central tendency when youre working with data sets of independent values taken at one point of time. There was an example of this in one of the previous articles, when we were stratum. However, in finance you often work with share returns over a series of multiple time periods.Forcalculating average percentage return over multiple periods of time,arithmetic average is unsubstantial as it fails to take the different basis in every year into consideration (100% equals a different price or portfolio value at the beginning of each year). The more volatile the returns are, the more significant this flunk of arithmetic average is. Here you can see the example and reason whyarithmetic average fails when measuring average percentage returns over time. * Works only when all values are equally important- Arithmetic average treats all the individual observations equally.In finance and investing, you often need to work with unequal weights. For example, you have a portfolio of stocks and it is highly unlikely that all stocks will have the same weight and therefore the same electric shock on the substance performance of the portfolio. Calculating the average performance of the total portfolio or a basket of stocks is a typical case whenarithmetic average is not suitableand it is better to use weighted average instead. You can find more details and an example here wherefore you need weighted average for calculating total portfolio return. T2. 2 comparative degree picture of medial, mode, mean The MedianThe Median is the substance value i n your appoint. When the totals of the list are odd, the median is the middle entry in the list after sorting the list into change magnitude order. When the totals of the list are up to now, the median is equal to the sum of the two middle (after sorting the list into increasing order) numbers divided by two. Thus, remember to line up your values, the middle number is the median Be sure to remember the odd and even rule.That is, if the data is in meters, the specimen release is in meters as well. The discrepancy is in meters2, which is more difficult to interpret. Neither the standard deviation nor the strain is robust to outliers. A data value that is separate from the body of the data can increase the value of the statistics by an arbitrarily large amount. The meanabsolute deviation (MAD) is also sensitive to outliers. But the MAD does not move quite as much as the standard deviation or variance in response to bad data. Theinterquartile target (IQR) is the variety between the 75th and 25th percentile of the data.Since only the middle 50% of the data affects this measure, it is robust to outliers. T3. 2 What are the different characteristics of the following measures of dispersion. Therangeis the simplest measure ofdispersion. The range can be thought of in two ways. 1. As a quantity the difference between the highest and lowest scads in a distribution. 2. As an interval the lowest and highest marks may be reported as the range. By far the most ordinarily used measures of dispersion in the social sciences arevarianceandstandard deviation. Varianceis the average squared difference of scores from the mean score of a distribution.