Essays on Understanding business information Coursework

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Understanding business information Program Supervisor January 16, Understanding business information Data Survey Analysis Advantages and disadvantages of online survey Different research designs exist and advantages and disadvantages of a design, together with its suitability to a study dictate its selection. Online survey design has many advantages that could inform its selection for the study. Cost effectiveness is one of the advantages of online survey because only one document is prepared, in a soft copy, and is distributed to all research participants. In addition, distribution of the online survey instrument is cheap, as compared to physical survey instrument that may require postal delivery or hand delivery.

Online survey is also fast because of instant delivery to and from research participants. Online survey is also scalable with respect to number of research participants besides such advantages as anonymity, possible automation of data entry, and popularity of the internet (Rubin and Babbie 2012, p. 148). It is also easy to include online or automated objects in an online survey document and the attractive consequence is likely to improve response rate (Morrison, Kisiel, and Smith 2012, p.

25). One of the main disadvantages of online survey is the possible restricted internet access among some population segments, such as the elderly who may not be conversant with digital applications, and elderly employees may face this challenge. Possible anonymity from online survey is also a threat to authenticity of collected data and the research participants may lack understanding of the scope of a study, leading to unreliable data (Morrison, Kisiel, and Smith 2012, p. 25). The instrument may also fail to exploit a representative sample and yield a low response rate due to technology use that may be discriminative (Rubin and Babbie 2012, p.

148). Opinion and security The following is a summary of some of the descriptive statistics on the data. Table 1: Descriptive statistics   Communication Innovation Relationship Security Satisfaction Mean 2.0125 2.05 2.2625 3.5875 2.275 Median 2 2 2 4 2.5 Mode 1 1 3 5 3 Standard Deviation 1.185287 1.210712 0.74194 1.472909 0.810922 The respondents are not free to express their opinions for fear of negative reaction and the low mean (2.01) that is consistent with median shows this. Respondents also perceive job insecurity, as high mode (5) and median (4) that is consistent with a mean of 3.59 shows. The response is the same across the job groups as the following ANOVA results show (p> 0.05).

Table 2: ANOVA table ANOVA Sum of Squares df Mean Square F Sig. comm Between Groups 12.780 4 3.195 2.440 . 054 Within Groups 98.207 75 1.309 Total 110.988 79 security Between Groups 11.852 4 2.963 1.393 . 245 Within Groups 159.535 75 2.127 Total 171.388 79 Innovation Employees are not motivated into innovation as a majority of them reported strong disagreement or just disagreement (mean=2.05, mode= 1) and the opinion is the same across the age groups (p=>0.05). Table 3: ANOVA for innovation across age groups ANOVA innovation Sum of Squares df Mean Square F Sig. Between Groups 12.857 7 1.837 1.285 . 270 Within Groups 102.943 72 1.430 Total 115.800 79 Relationships Employees’ relationships with managers and supervisors are weak as it occurs sometimes or never. None of the investigated factors affect relationships between employees and either managers or supervisors, as regression analysis results show (p>0.05 for all factors). Table 4: Regression analysis for relationship and other factors Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std.

Error Beta 1 (Constant) 1.939 . 494 3.924 . 000 jobdesc. 005 . 064 . 010 . 085 . 933 comm -. 089 . 077 -. 143 -1.164 . 248 innovation -. 035 . 070 -. 057 -. 501 . 618 security -. 020 . 059 -. 040 -. 344 . 732 satisfaction. 201 . 107 . 220 1.881 . 064 age. 051 . 049 . 123 1.056 . 294 a. Dependent Variable: relationship Conclusion Employees’ general level of satisfaction is low as all the indicators received fair to negative responses, showing low level of satisfaction. Correlation and Regression Data summary The following table summarises descriptive statistics (mean, median and standard deviation) for operating revenue, operating expenses, and profit or loss made before tax, all figures are in £ millions. Table 5: Mean, median, and standard deviation   Operating Operating Profit (Loss) Revenue Expenses Before Tax Mean 13861.23 13281.7 341.426 Median 13580.2 13140.65 360 Standard Deviation 4539.784 4529.782 342.6109 Scatter plots Operating revenue & expenses The following graph shows the relationship between operating revenues and operating expenses Scatter 1: Operating revenues and operating expenses Operating revenues & profit or loss The following graph shows the relationship between operating revenues and profit (loss) before tax Scatter 2: Operating revenues and profit (loss) before tax Operating expense & profit or loss The following graph shows the relationship between operating expenses and profit (loss) before tax Scatter 3: Operating expenses and profit (loss) before tax The graphs suggest that a strong correlation only exists between operating revenues and operating expenses.

Correlation Based on the visuals, operating revenues and operating expenses are strongly related.

This is because the scatter plot for the two variables identifies a pattern that converges to a straight line of best fit and this suggests a strong linear relationship between operating revenues and operating expenses. Correlation analysis confirms this with a correlation coefficient of 0.996873 that shows a nearly perfect correlation. Regression analysis for the relationship between operating revenues and operating expenses The following tables shows the regression analysis results for the relationship between operating revenues and operating expenses Table 6: Summary output SUMMARY OUTPUT Regression Statistics Multiple R 0.996873 R Square 0.993756 Adjusted R Square 0.993472 Standard Error 366.7953 Observations 24 Table 7: ANOVA table ANOVA   df SS MS F Significance F Regression 1 4.71E+08 4.71E+08 3501.308 9.49E-26 Residual 22 2959854 134538.8 Total 23 4.74E+08       Table 8: Table of coefficients   Coefficients Standard Error t Stat P-value Intercept 591.8239 236.4208 2.503265 0.020222 X Variable 1 0.999074 0.016884 59.17185 9.49E-26 The ANOVA table shows significance of the relationship between the variables and that accounts for more than 99 percent of the analyzed data.

From the table of coefficients, the following is the linear regression equation for the relationship between the two variables. Operating revenues= 591.82 + 0.999(operating expenses) + e Where e defines an error term for the relationship and is a variable for each data set. The coefficient of determination, as computed by Excel, is 0.993472 and shows that the line of fit is almost perfect, accounting for almost all of the data.

The intercept is also significant. Predictions for 2014 and 2015 The table below shows regression analysis for operating revenues with year Table 9: Table of coefficients for revenue and yea   Coefficients Standard Error t Stat P-value Intercept -1246383 53526.01 -23.2856 5.43E-17 X Variable 1 629.6498 26.74279 23.54466 4.29E-17 Regression analysis between operating revenues and time (by year) yields the following equation. Operating revenue= -1246383 +629.65(Year) For the years 2014 and 2015, Operating revenue 2014= -1246383 +629.65(2014)= £ 21731.86 millions Operating revenue 2015= -1246383 +629.65(2015) = £ 22361.51 millions Based on the predictions, an increasing trend in the revenues is expected.

Time Series Analysis Graph and equation of the trend of profit (loss) The following is the graph, with equation, of the trend based on least square method. Graph 1: Trend of profit (loss) Equation for the trend is shown below, a sixth order polynomial and suggest increase in profits in the near future. y = 0.0005x6 - 0.036x5 + 0.9861x4 - 12.507x3 + 65.151x2 - 33.378x + 95.234 Average annual increase over the period The following table shows annual average increase in profit or loss Table 10: Average annual increase over the period Year average 1990 1991 210.60 1992 234.95 1993 243.05 1994 351.45 1995 573.85 1996 779.80 1997 808.05 1998 586.95 1999 415.25 2000 461.55 2001 222.80 2002 165.20 2003 416.40 2004 579.80 2005 621.95 2006 356.65 2007 457.60 2008 157.75 2009 -478.35 2010 -134.75 2011 409.90 2012 385.65 2013 196.01 A trend in percentage increment is therefore expected, though with a level of volatility.

Indices for operating revenue, expense, and profit (loss) the following table shows the indices Table 11: Indices for operating revenue, expense, and profit (loss) Year indices p/l Indices operating revenues Indices operating expenses 1990 100 100 100 1991 209.4783 110.0202 107.3055 1992 135.7825 115.1347 113.121 1993 221.3813 134.606 129.5995 1994 295.0771 141.8643 134.1803 1995 548.1999 155.516 144.9556 1996 597.7223 174.5937 167.3244 1997 589.7134 189.5071 183.2645 1998 272.8141 201.3581 194.723 1999 337.399 207.2314 207.87 2000 340.8523 222.9621 219.9222 2001 -13.446 214.7094 220.2362 2002 256.2087 201.132 196.431 2003 355.6943 214.6904 209.3113 2004 496.3262 226.6822 218.2077 2005 417.6341 249.2269 239.5061 2006 106.4658 262.9672 254.1839 2007 565.9809 272.1527 264.18 2008 -334.166 296.4222 304.7483 2009 -368.773 281.9674 297.0069 2010 170.7568 274.0198 268.5867 2011 431.5944 337.7158 337.4012 2012 135.1212 339.2446 340.1389 2013 152.9197 335.7081 333.0923 Percentage changes for operating revenue, expenses, and profit (loss) The following table shows percentage change for the variables Table 12: Percentage change Year percentage change for profit/loss percentage change in operating revenue percentage change in operating expense 1990 1991 109.4783 10.02024 7.305528 1992 -73.6958 5.114463 5.815467 1993 85.59882 19.47132 16.47851 1994 73.69581 7.258269 4.580798 1995 253.1227 13.65174 10.77529 1996 49.52241 19.07766 22.36881 1997 -8.00882 14.91336 15.94007 1998 -316.899 11.85101 11.4585 1999 64.58486 5.873332 13.14702 2000 3.453343 15.73073 12.05225 2001 -354.298 -8.2527 0.313954 2002 269.6547 -13.5774 -23.8052 2003 99.48567 13.55846 12.88024 2004 140.6319 11.99172 8.896444 2005 -78.6921 22.54474 21.29844 2006 -311.168 13.74028 14.67775 2007 459.5151 9.18548 9.996096 2008 -900.147 24.26959 40.56837 2009 -34.6069 -14.4549 -7.74148 2010 539.5298 -7.94757 -28.4201 2011 260.8376 63.69601 68.81446 2012 -296.473 1.528805 2.737743 2013 17.79847 -3.53655 -7.04664 Index chart for operating revenue, expenses, and profit (loss) Below is the chart for the indices Graph 2: Index chart for operating revenue, expenses, and profit (loss) The graphs suggests a trend in increase in profits due to increase in profitability while and simultaneous decrease in expenses.

Reference list Morrison, J Kisiel, K and Smith, C 2012, Working through synthetic worlds, Ashgate Publishing, Ltd. , Burlington. Rubin, A and Babbie, E 2012, Brooks/Cole Empowerment series: Essential research methods for social work, Cengage Learning, Belmont.

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