BUS 308 Statistics for Managers

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Running Head:What I’ve learned about StatisticsDebra JohnsonWhat I’velearnedaboutStatisticsBUS 308 Statistics for ManagersInstructor: Travis HayesFebruary 18, 2025Reflecting on your experience in the course "Statistics for Managers," what key concepts ortechniques have you learned about descriptive statistics, particularly in relation to measures ofcentral tendency? Discuss the importance of these concepts in decision-making and theirapplication in real-world business contexts. In your response, include examples of how thesestatistical methods can be used to analyze data effectively. Your answer should be between600-800 words.

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What I’velearned about StatisticsPage2IntroductionThe processof data analysis is thatof turning data intosignificantinformation.Whilethere are no hard and fast distinctions for analyzingstatistical data, ensuring that you have amethodical approach is vital toan accurate analysis.Transformingdata intostatisticalinformationand then communicating itaccurately is acrucialdynamicof effective decisionmaking.Once a policy has been implemented,it is necessary to monitor and evaluatetheeffectiveness of that policy.As Othman (2005) states, ‘Good statistics, therefore, represent a keyrole in good policy making.”The impact of policy can be measured with good statistics.I.Descriptive StatisticsIn the single factor tests and analysis we have a limitation to chooseonly one factor forcomparison whichoftenfails. In reality,more than one factor may influence the outcome andcomparison of phenomena by including all those factors in a model giving anaccurate degree ofcomparison.Multiple regression is one such technique in which we can include controlvariables,andas many as possible. We can test the impact of their inclusion, and there is achance of eliminating the non-significant variables to fit the accurate model.(Hoffmann, 1981).A.Measures of CentralTendency.Westart thinking about how wecan represent a set ofnumbers with one number that somehow represents the "center". Wecanthen talk about thedifferences between populations, samples, parameters and statistics.A measure of centraltendency is a single value that attempts to describe a set of data by identifying the centralposition within that set of data. As such, measures of central tendency are sometimes calledmeasures of central location. They are also classed as summary statistics. The mean (often called

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What I’velearned about StatisticsPage3the average) is most likely the measure of central tendency that weare most familiar with, butthere are others, such as the median and the mode.(Lund & Lund, 2013).The mean, median and mode are all valid measures of central tendency, but underdifferentconditions, some measures of central tendency become more appropriate to use thanothers. In the following sections, we will look at the mean, mode and median, and learn how tocalculate them and under what conditions they are most appropriate to be used.(Lund & Lund,2013).TheMean (Arithmetic).The mean (or average) is the most popular and well knownmeasure of central tendency. It can be used with both discrete and continuous data, although itsuse is most often with continuous data.The mean is equal to the sum of all the values in the dataset divided by the number of values in the data set.If we have n values in a data set and theyhave values x1, x2, ..., xn, the sample mean, usually denoted by (pronounced x bar), is:(Lund & Lund, 2013).This formula is usually written in a slightly different manner using the Greek capitolletter,pronounced "sigma", which means "sum of...”(Lund & Lund, 2013).In statistics, samples and populations have very different meanings and these differencesare very important, even if, in the case of the mean, they are calculated in the same way. Tocalculatethe population mean and not the samplemean;usethe Greek lower case letter "mu",denoted as μ:(Lund & Lund, 2013).The mean is essentially a model of thedata set.It is the value that is most common.Themean is not oftenone of the actual values observed in a data set. One of its important properties

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What I’velearned about StatisticsPage4is that itminimizeserror in the prediction of any one value in adata set. That is, it is the valuethat produces the lowest amount of error from all other values in the datasetAnimportantproperty of the mean is thatit includes every value in adata set as part of the calculation. Inaddition, the mean is the only measure of central tendency where the sum of the deviations ofeach value from the mean is always zero.(Lund & Lund, 2013).TheMedian.Weusually prefer the median over the mean (or mode)when ourdata is skewed (i.e., the frequency distribution for our data is skewed). If we consider the normaldistribution-as this is the most frequently assessed in statistics-when the data is perfectlynormal, the mean, median andmode are identical.They all represent the most typical value inthe data set.But as the data becomes skewed the mean loses its ability to provide the best centrallocation for the data because the skewed data is dragging it away from the typical value.However, the median best retains this position and is not as strongly influenced by the skewedvalues.(Lund & Lund, 2013).The median is the middle score for a set of data that has been arranged in order ofmagnitude. The median is less affected by outliers and skewed data. In order to calculate themedian, suppose we have the data below:6555895635145655874592We first need to rearrange that data into order of magnitude (smallest first):1435455555565665878992(Lund & Lund, 2013).Our median mark is themiddle mark-in this case, 56.It is the middle mark becausethere are 5 scores before it and 5 scores after it. This works fine when you have an odd numberof scores, but what happens when you have an even number of scores? What if you hadonly 10

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