Business Decision Making Assiment Essay

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Thank you TASKS PAGE NO lying-in 01 04 Task 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 discrimination between a ingest and a population Population Sample * Population is the ara in which you are severe to get information from. * This meaning of population is also used in surve y research, but this is only ane of many possible definitions of population. Examples Cedar prime 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 outstanding to have a sample that get out represent your entire population in order to minimize biases.Survey research is found on try out, which involves acquire information from only whatever members of the population. * Samples whoremonger be drawn in several different ways, such as probability samples, quota samples, purposive samples, and volunteer samples. Examples presume the populations stated above 47 Cedar Crest students chosen randomly 8463 trees randomly selected in North America 20 sample autos from each stir (e. g. , GM, Ford, Toyota, Honda, etc. ) 1% of the oil consuming population per country T 1. 2 Describe the advantages of try out * takesaves moneyas it is a good deal cheaper to get inthe desired information from a smallsamplethan from the whole population. * Samplingsaves a lot of time and energy as the needed entropy are salt away and processed much faster than census information.And this is a very important consideration in all fonts of investigations or surveys. * Samplingprovides information that is around as accurate as that obtained from a complete census rather a properly designed and carefully executedsamplesurvey willing provide much accurate moments. Moreover, owing to the reduced volume of work, persons of higher caliber and properly trained can be employed to analyze the entropy. * 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 measurement processphysicallydamages or destroys thesamplingunit underinvestigation. For example, in order to meas ure the average lifetime oflight bulbs, the measurement process destroys thesamplingunits, i. e. the bulbs, as they are used until they burn out.A manufacturer will therefore use only asampleoflight bulbsfor this purpose and will not burn out all the bulbs produced. Similarly, the whole pot of dope 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 inexhaustible or is inaccessible 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- reception, following 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 failure to locate or measure some of the units, refusals, not-at-home, etc. Samplingis extensivel y used to obtain some of the census information. * The most important advantage ofsamplingis that it provides a valid measure of dependableness for thesampleestimatesand this is one of the two 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 attribute 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 heavy(p) scale. Most of the research work is done on the samples. * Sample data i s also used to check the accuracy 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 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).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 repr esent 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 cancel one another 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 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 calculated from thesampleis based on the sampledata and is calledsamplestatistic. Task 2 T2. 1 Advantages a nd disadvantages of arithmetic mean. Advantages * Fast and easy to calculate- As the most basic measure in statistics,arithmetic average 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(predicate) you can see thebasics of arithmetic average calculation. * Easy to work with and use in further analysis- Because its calculation is straightforward and its meaning known to allbody,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 extreme values- Arithmetic average is extremely excellent to extreme values.Therefore,arithmetic averageis not the best measure to use with data sets conta ining a few extreme valuesor with more dust (volatile) data setsin general. Mediancan be a separate alternative in such cases. * Not suitable for time series type of data- Arithmetic averageis perfect for measuring central tendency when youre working with data sets of independent values taken at one catamenia 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.Forcalculating 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 impuissance of arithmetic average is. Here you can see the example and reason whyarithmetic average fails when measuring average percentage returns over time. * kit and boodle onl y when all values are equally important- Arithmetic average treats all the individual observations equally.In finance and investing, you often need to work with mismatched 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 quantity transaction 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 list. When the totals of the list are odd, the median is the middle entry in the list after sorting the list into increase order. When the totals of the list are even, the median is equal to the sum of the two middle (after so rting 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 standard deviation 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 difference 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 ofdispers ion. The range can be thought of in two ways. 1. As a quantity the difference between the highest and last(a) scores in a distribution. 2. As an interval the lowest and highest scores may be reported as the range. By far the most unremarkably used measures of dispersion in the social sciences arevarianceandstandard deviation. Varianceis the average squared difference of scores from the mean score of a distribution.