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Summary Nike is one of the best fashion firms in the world. It is an American corporation that has been performing well in the market for the past two decades. The company has invested in better strategic management and innovation, which has ensured its success in the market. As such, this analysis has included the statistical analysis that was done via the excel analysis tool pack. Data analysis is a crucial concept in research as it gives the business an assessment of performance for the past and prediction for the future (Fiori, 2019). The data was further presented through tables and graphs to make it more appealing to the audience.
Analysis 1 1st Quartile -0.8 3rd Quartile 0.580002 Minimum -3.81 Mean -0.22667 Median -0.11 Figure 1: Box and Whisker Plot for Daily Changes. Descriptive Statistics Descriptive statistics of Nike were done through an excel analysis tool pack. The daily change data was executed in the software, and measures of central tendency were computed. Descriptive statistics help the researcher analyze the data and develop predictive analysis in the future for the business (Cicho?, 2020). Therefore, based on the descriptive analysis, Nike performs well despite the challenges in the global market economy. Column1 Mean -0.226668303 Standard Error 0.230931073 Median -0.11 Mode -0.800003 Standard Deviation 1.326598016 Sample Variance 1.759862297 Kurtosis 0.5247532 Skewness -0.533238077 Range 5.770003 Minimum -3.809997 Maximum 1.960006 Sum -7.480054 Count 33 Largest (1) 1.960006 Smallest (1) -3.809997 Confidence Level (95.0%) 0.470391203 Skewness The data skewed to the right because the mean is less than the median. (-0.22667<-011). Correlation and Regression Analysis SUMMARY OUTPUT Regression Statistics Multiple R 0.958998 R Square 0.919676 Adjusted R Square 0.917085 Standard Error 1.343235 Observations 33 ANOVA df SS MS F Significance F Regression 1 640.4083 640.4083 354.9383 1.57E-18 Residual 31 55.9327 1.804281 Total 32 696.341 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3.469349 7.042994 0.492596 0.625769 -10.8949 17.83363 -10.8949 17.83363 X Variable 1 0.976132 0.051812 18.83981 1.57E-18 0.87046 1.081804 0.87046 1.081804 Based on the regression done, Nike has a strong regression coefficient of 0.917, which translates to 91.7%. Thus, Nike has been performing well because of the right marketing strategies and innovation skills. Probability Plots Figure 2: Residual Plot. Figure 3: Line Plot. Figure 4: Normal Probability Plot 1. Figure 5: Normal Probability Plot 2. Distribution The data is not normally distributed because the mean is not equal to the median. Figure 6: Central Limit Theorem. There is a higher likelihood of variation because of the large ranges between the daily changes. The probability also has supported the existence of an alteration in the values. For instance, when the probability of the change is higher, then the values are likely to be more altered. Alternatively, the standard error is small thus, will support the positive impacts on the firm. Therefore, Nike will change to the positive side because of the better strategies implemented. References Cicho?, M. (2020). Reporting statistical methods and outcomes of statistical analyses in research articles. Pharmacological Reports, 72(3), 481-485. Web. e Fiori, A. (2019). On firm size distribution: statistical models, mechanisms, and empirical evidence. Statistical Methods & Applications, 29(3), 447-482. Web. Appendix A Date Open High Low Close Adj Close Volume Daily change 11/10/20 130.050003 130.119995 126.25 127.709999 127.453568 8014700 -2.340004 11/11/20 128.690002 129.800003 127.18 127.660004 127.403671 6058700 -1.029998 11/12/20 126.620003 127.839996 125.629997 126.639999 126.385719 4780400 0.019996 11/13/20 127.910004 128.600006 126.830002 128.279999 128.02243 3986100 0.369995 11/16/20 129.460007 130.320007 127.370003 130.110001 129.848755 6116200 0.649994 11/17/20 130.479996 132.600006 129.110001 132.210007 131.94455 7002900 1.730011 11/18/20 133.070007 133.979996 131.529999 131.630005 131.365707 5572800 -1.440002 11/19/20 131.919998 132.110001 129.929993 131.910004 131.645142 4642800 -0.009994 11/20/20 133.300003 133.529999 131.910004 132.979996 132.712982 4318100 -0.320007 11/23/20 134.380005 134.889999 133.089996 134.130005 133.860687 6118400 -0.25 11/24/20 135 135.990005 134.210007 134.699997 134.429535 7204700 -0.300003 11/25/20 134.25 135.800003 133.619995 135.539993 135.267838 4484500 1.289993 11/27/20 136 136.130005 133.339996 134.25 133.980438 3506800 -1.75 11/30/20 133.910004 135.289993 132.690002 134.699997 134.429535 9652500 0.789993 12/1/20 136.440002 136.5 134.75 135.440002 135.168045 3834500 -1 12/2/20 135.160004 136.320007 134.669998 135.580002 135.30777 4132700 0.419998 12/3/20 135.100006 137.949997 135 136.960007 136.684998 4930900 1.860001 12/4/20 137.080002 137.399994 135.639999 137.190002 137.190002 4344000 0.11 12/7/20 137 138.860001 136.800003 138.75 138.75 4590800 1.75 12/8/20 138.240005 140.440002 137.649994 139.119995 139.119995 6953600 0.87999 12/9/20 140.570007 140.570007 138.270004 138.789993 138.789993 4341300 -1.780014 12/10/20 138.279999 139.139999 137.240005 137.580002 137.580002 4511000 -0.699997 12/11/20 137.389999 138.139999 136.229996 137.410004 137.410004 4172400 0.020005 12/14/20 138.919998 139 136.199997 136.279999 136.279999 7599000 -2.639999 12/15/20 137.429993 139.440002 137.25 139.389999 139.389999 7642600 1.960006 12/16/20 139.070007 140.490005 137.460007 138.339996 138.339996 6573400 -0.730011 12/17/20 139.919998 140.740005 138.75 140.5 140.5 8727000 0.580002 12/18/20 141.089996 141.139999 137.169998 137.279999 137.279999 17970800 -3.809997 12/21/20 144.820007 147.949997 142.509995 144.020004 144.020004 16111300 -0.800003 12/22/20 143.050003 143.470001 141.089996 142.449997 142.449997 6339400 -0.600006 12/23/20 142.559998 143.600006 141.699997 141.759995 141.759995 3388300 -0.800003 12/24/20 141.100006 142.190002 141.100006 141.600006 141.600006 1821900 0.5 12/28/20 142.539993 142.919998 141.039993 142.429993 142.429993 4080100 -0.11 Appendix B RESIDUAL OUTPUT PROBABILITY OUTPUT Observation Predicted Y Residuals Percentile Y 1 128.1311 1.918854 1.515152 126.62 2 128.0823 0.607655 4.545455 127.91 3 127.0867 -0.46669 7.575758 128.69 4 128.6875 -0.77754 10.60606 129.46 5 130.4739 -1.01386 13.63636 130.05 6 132.5238 -2.04375 16.66667 130.48 7 131.9576 1.112415 19.69697 131.92 8 132.2309 -0.31091 22.72727 133.07 9 133.2754 0.024642 25.75758 133.3 10 134.3979 -0.01792 28.78788 133.91 11 134.9543 0.045691 31.81818 134.25 12 135.7743 -1.52426 34.84848 134.38 13 134.5151 1.484947 37.87879 135 14 134.9543 -1.04431 40.90909 135.1 15 135.6767 0.763351 43.93939 135.16 16 135.8133 -0.65331 46.9697 136 17 137.1604 -2.06037 50 136.44 18 137.3849 -0.30488 53.0303 137 19 138.9076 -1.90765 56.06061 137.08 20 139.2688 -1.0288 59.09091 137.39 21 138.9467 1.623323 62.12121 137.43 22 137.7656 0.514425 65.15152 138.24 23 137.5996 -0.20963 68.18182 138.28 24 136.4966 2.423399 71.21212 138.92 25 139.5324 -2.10238 74.24242 139.07 26 138.5074 0.562579 77.27273 139.92 27 140.6159 -0.69588 80.30303 140.57 28 137.4727 3.617265 83.33333 141.09 29 144.0519 0.768142 86.36364 141.1 30 142.5193 0.530672 89.39394 142.54 31 141.8458 0.7142 92.42424 142.56 32 141.6896 -0.58962 95.45455 143.05 33 142.4998 0.040189 98.48485 144.82
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