Data Analysis for Decision Makers Process

Question: Discuss about the Data Analysis for Decision Makers Process. Answer: Introduction: The data that can give some valuable information about anything are regarded as statistical data. The datasets can be analyzed and used for making some important decisions. In this report, a dataset about the sports management has been analyzed using various statistical techniques. The sports management discusses the various aspects required for the improvement of sports (Cunha et al. 2015). In this report the various factors of the customers who are visiting a gym has been studied. The factors include the gender of the persons, which class the people are taking, the sauna used by the persons, the time spent by them in the sauna and others. The data has been analyzed using various techniques such as descriptive statistics techniques, inferential techniques and others. The conclusions that can be drawn from the results of analysis can help to take make important business decision. The dependence of gender on the type of sports can be studied with the help of statistical techniques. Th ere are almost five types of sports that are played by the people. The sports include dance, spin, yoga, circuit training and others. The various graphs and charts are also drawn to explain the datasets. Discussion: The variables that were considered for the analysis were height (cms) and Time in Sauna (mins). The mean value of the variable height (cms) was found to be 175.674898cms and the mean value of the variable Time in Sauna (mins) was found to be 7.232959184 min. Mean is defined as the average of all the values of a variable. It represents the central value of a data set (Bickel and Lehmann 2012). It can be interpreted that the average value of the continuous variable height (cms) is 175.674898 cms and it is the mean value of the data set of height (cms). It can also be interpreted that the average value of the continuous variable Time in Sauna (mins) is 7.232959184 min and it is the mean value of the data set of Time in Sauna (mins). Median is defined as the central value of a data set when the values of the variable are arranged in either ascending order or descending order (Cardinal and Aitken 2013). The median value of the variable height (cms) was found to be 176.82 cms while the median value of the variable Time in Sauna (mins) was found to be 0. It can be interpreted that on arranging the values of the variable height (cms) in an order, the central value was found to be 176.82 cms. It can also be interpreted that on arranging the values of the variable Time in Sauna (mins) in an order, the central value was found to be 0 mins. Standard deviation of a variable is defined as the deviation of the values of a variable from the mean value of the variable (Xie et al. 2013). It is seen that the standard deviation of the variable height (cms) is 9.63769706 cms while the standard deviation of the variable Time in Sauna (mins) is 8.763928203 mins. It can be interpreted that the values of height (cms) is 9.63769706 cms deviated on an average from the mean value of the variable. It can also be interpreted that the values of Time in Sauna (mins) is 8.763928203 mins deviated from the average value of the variable. The range of a variable is defined as the difference between the maximum and minimum value of the variable (Gu 2013). The range of the variable height (cms) is found to be 42.29 while the range of the variable Time in Sauna (mins) is found to be 23.03. It can be interpreted that the difference between the minimum and maximum value of the variable height (cms) is found to be 42.29cms. It can also be interpreted that the difference between the minimum and maximum value of the variable Time in Sauna (mins) is found to be 23.03 mins. On considering the variable Sauna Used, it was seen that the variable follows binomial distribution. Binomial distribution is defined as a discrete distribution (Leys et al. 2013). The binomial distributions have the parameters as n and p (DeVeaux et al. 2015). n is defined as the total number of independent cases while p is defiend as the probability of success (Hong 2013). In this case, the probability of using sauna; i.e. the response yes is considered to be p while the probability of not using sauna is considered to be (1-p). The probability of having the response as yes or no was found to be 0.5 and the total number of cases was 100. Thus, the distribution of the discrete variable was found to be binomial distribution with parameters 100 and 0.5. The inferential statistical measures include the testing of hypothesis. The hypothesis test is to be performed for the continuous variable. A t test can be performed by using the height of the people who are visiting the sauna as the dependent variable and the gender of the person as the dependent variable or the grouping variable. Te t-test compares the mean of two groups (Bates et al. 2014). The null hypothesis is to test whether the mean height is same for both the gender or varies among the two genders. The test has been 1.86 *10^-6. The p-value of the test is less than the given level of significance =0.05. On the basis of the p-value of the test obtained, the null hypothesis of the test is rejected. Therefore, it can be said that the mean value varies among the males and females. The time spend by the people in sauna may vary according to the sports played by the peoples. A one way ANOVA has been conducted by taking the time spends by the people in the gym as the dependent variable and the sports played by the person as the independent variable. The model for the analysis is given below: Yij = + i + eij (Gu 2013) The variable yij is the response variable that is the time spent in the sauna and i is the effect due to the factor that is the type of sport played in the gym. The null hypothesis of the test is that the mean value of time spent in the sauna are equal ffor all the sports. The results obtained from the analysis of variance are shown in the following table: ANOVA Source of Variation SS df MS F P-value F crit Between Groups 197.5472 5 39.50944 0.501176 0.774637 2.313431 Within Groups 7252.677 92 78.83345 Total 7450.224 97 Table: Analysis of variance or different type of sports played Source: Created by author The p-value obtained from the analysis is 0.774637. Therefore, the null hypothesis of the test could be accepted on the basis of the given p-value. The acceptance of null hypothesis indicates that the time spent in the sauna is equal for all sports. A regression analysis has also been performed by taking the height of the persons visiting the height of the persons as the dependent variable and the time spend in sauna as the independent variable. The objective of the regression is to know the dependence of the height of the persons on the time spend on sauna. The results of the regression analysis are given below: Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -1.79205 16.30239 -0.10993 0.912698 -34.152 30.56795 -34.152 30.56795 X Variable 1 0.051373 0.092661 0.554424 0.580578 -0.13256 0.235303 -0.13256 0.235303 Table: Regression analysis results (Source: Created by author) The results of the regression analysis show that the regression coefficient for the dependent variable is 0.05. Therefore, there is a positive correlation between the time spend in sauna and the height of the people. Conclusion: The report gives an idea about the various strategies used in the gym. There are different kinds of sports that are used in the gym. The datasets contain different variables like the identity of the people visiting the gym, the time spent by them in the gym, the type of sport undertaken, the sauna bath taken by the people, the time spent in the sauna and others. The variables has been studied using various statistical techniques such as descriptive statistics measures and the inferential measures. The variables are at first identified as the continuous variables and discrete variables. The descriptive statistics measures have been calculated for each of the continuous variables. Some of the persons who visit the gym take sauna bath. Therefore, whether the person takes sauna bath or not is a discrete variable. The distribution of the variable has been found to be binomial. The ANOVA test has also been conducted by taking the time sent at sauna as the dependent variable and the type of sports as the independent variable. The time spent at sauna is found to be equal for all type of sports. A regression analysis has also been conducted. The results of regression analysis suggest that the time spent at sauna and the height of the persons has a positive association. This implies as the height increases, the time spent by the people in sauna also increases. References and bibliography: Bates, D., Maechler, M., Bolker, B. and Walker, S., 2014. lme4: Linear mixed-effects models using Eigen and S4.R package version,1(7). Bickel, P.J. and Lehmann, E.L., 2012. Descriptive statistics for nonparametric models IV. Spread. InSelected Works of EL Lehmann(pp. 519-526). Springer US. Cardinal, R.N. and Aitken, M.R., 2013.ANOVA for the behavioral sciences researcher. Psychology Press. Cunha, E.C.M., Silva, V.H.R., Pedroso, C.D.Q., Barros Filho, M.A., Santos, . and Miranda, Y., 2015. Influences of management and marketing sports in shares of development and strategies for soccer as a business in Brazil: a systematic review.Revista Intercontinental de Gesto Desportiva,5(2), pp.143-152. DeVeaux, M., Kane, M.J. and Zelterman, D., 2015. A Stopped Negative Binomial Distribution.arXiv preprint arXiv:1508.01264. Draper, N.R. and Smith, H., 2014.Applied regression analysis. John Wiley Sons. Forte, A., 2013. The Moral Reasoning Of Sports Management Students In The United States And Italy.Journal of International Education Research,9(2), p.177. Gu, C., 2013.Smoothing spline ANOVA models(Vol. 297). Springer Science Business Media. Hong, Y., 2013. On computing the distribution function for the Poisson binomial distribution.Computational Statistics Data Analysis,59, pp.41-51. Leys, C., Ley, C., Klein, O., Bernard, P. and Licata, L., 2013. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median.Journal of Experimental Social Psychology,49(4), pp.764-766. Shao, J. and Ji, J., 2014, April. Research on the Impact of China's Athletics College Sports Management Model. In2014 International Conference on e-Education, e-Business and Information Management (ICEEIM 2014). Atlantis Press. Weiss, N.A. and Weiss, C.A., 2012.Introductory statistics. London: Pearson Education. Xie, L., Kang, H., Xu, Q., Chen, M.J., Liao, Y., Thiyagarajan, M., ODonnell, J., Christensen, D.J., Nicholson, C., Iliff, J.J. and Takano, T., 2013. Sleep drives metabolite clearance from the adult brain.science,342(6156), pp.373-377. Forte, A., 2013. The Moral Reasoning Of Sports Management Students In The United States And Italy.Journal of International Education Research,9(2), p.177. Gu, C., 2013.Smoothing spline ANOVA models(Vol. 297). Springer Science Business Media. Hong, Y., 2013. On computing the distribution function for the Poisson binomial distribution.Computational Statistics Data Analysis,59, pp.41-51. Leys, C., Ley, C., Klein, O., Bernard, P. and Licata, L., 2013. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median.Journal of Experimental Social Psychology,49(4), pp.764-766. Shao, J. and Ji, J., 2014, April. Research on the Impact of China's Athletics College Sports Management Model. In2014 International Conference on e-Education, e-Business and Information Management (ICEEIM 2014). Atlantis Press. Weiss, N.A. and Weiss, C.A., 2012.Introductory statistics. London: Pearson Education. Xie, L., Kang, H., Xu, Q., Chen, M.J., Liao, Y., Thiyagarajan, M., ODonnell, J., Christensen, D.J., Nicholson, C., Iliff, J.J. and Takano, T., 2013. Sleep drives metabolite clearance from the adult brain.science,342(6156), pp.373-377.