Business Decision Making Assiment Essay Example for Free

Business Decision Making Assiment Es produceYou should sign this sheet to depict that you comply with these regulations. Students Signature Date Ac agnizeledgement I make up this chance to convey Miss. M. PriyanthimalaWho helped me to improve and developed this particular project. She explained well ab bulge out the project and sacrificed her more or less of the time to explain and likewise made sure that entirely the students understood. She was ready to help out in any time and gave her amply support for this particular project.I finally would like to thank my p arnts, friends and separates for helping to do this project. Thank you TASKS scalawag NO line 01 04 under victorious 02 09 Task 03 14 Task 04 16 Task 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 exemplification and a existence universe of discourse Sample * Population is the area in whic h you are trying to get training from. * This meaning of population is also example in survey research, but this is only one of many practical definitions of population. Examples Cedar Crest students trees in North America automobiles with four wheels people who consume olive oil. * Sample is a section of your population that you are actually going to survey. It is important to confound a have that bequeath represent your total population in order to minimize biases.Survey research is based on sampling, which involves getting data from only rough members of the population. * Samples dismiss be drawn in several different ways, such as probability take ins, quota essays, purposive samples, and volunteer samples. Examples assuming the populations stated above 47 Cedar Crest students elect randomly 8463 trees randomly selected in North America 20 sample autos from all(prenominal) make (e. g. , GM, Ford, Toyota, Honda, and so on ) 1% of the oil consuming population per cou ntry T 1. 2 Describe the advantages of sampling * sample distributionsaves moneyas it is much cheaper tocollectthe desired discipline from a weensysamplethan from the square population. * tastesaves a lot of time and energy as the engageed data are equanimous and processed much faster than enumerate information.And this is a very important consideration in all types of investigations or surveys. * Sampling offer ups information that is almost as accurate as that obtained from a complete census rather a properly patterned and carefully executedsamplesurvey will provide more accurate results. Moreover, owing to the reduced volume of work, persons of higher caliber and properly trained preserve 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 meter processphysicallydamages or destroys thesamplingunit underinvestigation. For example, in order to measure the norm lifetime oflight bulbs, the measurement process destroys thesamplingunits, i. e. the bulbs, as they are utilize until they burn out.A manufacturer will thence use only asampleoflight bulbsfor this purpose and will not burn out all the bulbs produced. Similarly, the whole pot of soup cannot be tasted to determine if it has an acceptable flavor. Samplingmay be the only means available for obtaining the needed information when the population appears to be infinite or is outback(prenominal) such as the population of mountainous or thickly forested areas. In such cases, taking $ complete census tocollectdata would neither bephysicallypossible nor practically feasible. * Samplinghas much smaller non-response, pursual up of which is much easier. The term non-response means the no availability of information from somesamplingunits included in thesamplefor any reason such as also-ran 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 reliability for thesampleestimatesand this is one of the devil basic purposes ofsampling. * Reliability If we collect the information about all the units of population, the collected information may be true. But we are never sure about it. We do not know whether the information is true or is completely false. Thus we cannot say anything with confidence 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, the seeds and the medicines are initially tested on samples and if found useful, then they are applied on large scale. most of the research work is done on the samples. * Sample data is also used to check the true statement of the census data. T 1. 3 Difference between primary 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 value of the parameter. Note 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 vindicatory 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 comparable).Imagine we were to draw an infinite (or very large) number of samples of individuals and calculate a statistic, say the arithmetical mean, on each one of these samples and that we then plotted the mean value obtained from each sample on a histogram (a chart using 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 defects with example? Sampling phantasm 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 cancel one some other out so that the overall result still accurately reflects the true value. Every sample design will generate a certain amount of random error. Bias, on the other hand, is more secure 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 calculated from thesampleis based on the sampledata and is calledsamplestatistic. Task 2 T2. 1 Advantages and disadvantages of arithmetic mean. Advantages * Fast and easy to calculate- As the most basic measure in statistics,arithmetic honest is very easy to calculate. For a small data set, you can calculate the arithmetic mean quickly in your head or on a piece of paper. Incomputer programslike Excel, the arithmetic average is always one of the most basic and best known functions.Here you can see thebasics of arithmetic average calculation. * short to work with and use in further 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 averagethangeometric averageormode. Disadvantages * Sensitive to utmost(a) set- arithmetical average is positively slender 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. normalcan be a better alternative in such cases. * Not suitable 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 wereyear. However, in finance you often work with percentage returns over a series of multiple time periods.For collusive average percentage return over multiple periods of time,arithmetic average is useless 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 weakness of arithmetic average is. Here you can see the e xample 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 impact on the total 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 hereWhy you need weighted average for calculating total portfolio return. T2. 2 Comparative picture of median, mode, mean The MedianThe Median is the middle value in your make. When the totals of the list are odd, the median is the middle entry in the list after sorting the list into increasing order. When the totals of the list are even, 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 criterion going is in meters as well. The variance is in meters2, which is more difficult to interpret. Neither the standard deviation nor the variance 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 range (IQR) is the contrast between the 75th and twenty-fifth percentile of the data.Since only the middle 50% of the data affects this measure, it is robust to outliers. T3. 2 What are th e 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 concluding scores in a distribution. 2. As an interval the lowest and highest scores may be reported as the range. By far the most commonly used measures of dispersion in the social sciences arevarianceandstandard deviation. Varianceis the average squared difference of scores from the mean score of a distribution.