Excel and word - Assignment Example

?Excel and word Data analysis is significant to managerial decisions as it offers bases for informed decisions. This paper applies excel spreadsheet to analyze hypothetical data and offers discusses advantages and disadvantages of spreadsheets in data analysis. Section A The average values of the variables across the two years identified systematic increments as shown in the following tables. Table 1: Measures of averages and dispersion for the year 2011 overhead cost statistics labour hours statistics machine hours statistics number of set ups statistics Mean 181.42 Mean 1162.8 Mean 1517.1 Mean 203.42 Standard Deviation 15.3 Standard Deviation 54.725 Standard Deviation 151.95 Standard Deviation 12.31 Sample Variance 234.08 Sample Variance 2994.9 Sample Variance 23090 Sample Variance 151.54 There was also increase in measures of variations as standard deviation of each of the measures indicates. Table 2: Measures of averages and dispersion for the year 2012 overhead cost statistics labour hours statistics machine hours statistics number of set ups statistics Mean 164.17 Mean 1106.5 Mean 1288.08 Mean 199.42 Standard Deviation 16.629 Standard Deviation 63.307 Standard Deviation 208.059 Standard Deviation 15.779 Sample Variance 276.52 Sample Variance 4007.7 Sample Variance 43288.4 Sample Variance 248.99 Comparison of averages for machine hours over the two-year period with the yearly values supports observed increments in variable quantities from 2010 to 2011. The following table summarizes descriptive statistics for machine hours. Table 3: Descriptive statistics for machine hours machine hours Mean 1402.583 Standard Error 43.50599 Median 1428 Mode 1300 Standard Deviation 213.135 Sample Variance 45426.51 Kurtosis -0.97697 Skewness -0.22011 Range 710 Minimum 1060 Maximum 1770 The mean, mode and median are different for the machine hours’ descriptive statistics to suggest skewdness. Even though the variables reported average increments from 2011 to 2012, higher variance in the year 2012 identifies higher-level dispersions that observed levels in the year 2011. The following frequency distribution and histogram illustrate distribution of labour hours by months with the highest number of months reporting labour hours between 1500 and 1600 Table 4: Frequency distribution for labour hours Bin Frequency 0-1000 0 1001-1100 5 1101-1200 1 1201-1300 2 1301-1400 4 1401-1500 3 1501-1600 6 1601-1700 2 1701-1800 1 1801-1900 0 More 0 Graph 1: Histogram for labour hours The frequency distribution table and the histogram confirms dispersion of observed values on the variables and the slight skewedness. Section B Graphical representation of variables is one of the strategies for identifying their correlation. The following graph shows consistency in changes among all the variables across the considered months. Graph 2: Comparative graph of the variables across the considered months The observed consistency in the variables’ trend suggests possible association among them. A more clear causal effect relationship can be observed from correletion coefficientthat is shown in the following table. Table 5: Correletion coefficients   Overhead costs Direct labour hours Machine hours No. Of set ups Overhead costs 1 Direct labour hours 0.703705 1 Machine hours 0.74701 0.680397 1 No. Of set ups 0.555748 0.321948 0.333965 1 The table identifies the strongest association between overhead costs and direct labor hours and machine hours because of the highest coefficient, 0.74701, as compared to coefficients for the other overhead cost drivers. Analysis of variance however offers the most accurate causal effects relationship and the tables bellow shows the regression analysis results. Table 6: ANOVA table for multiple regression analysis ANOVA   df SS MS F Significance F Regression 3 5284.197 1761.399 16.63454 1.17E-05 Residual 20 2117.761 105.8881 Total 23 7401.958       The low significance value, 1.17E-05 that is lower than 0.05, means that the null hypothesis is rejected to the effect that the cost drivers have significant effects on overhead costs. Analysis of effect of each overhead cost drivers generated the following results. Table 7: Table of coefficient for multiple regression analysis   Coefficients Standard Error t Stat P-value Intercept -56.11155 46.7650814 -1.19986 0.244214786 Direct labour hours 0.085749 0.04573425 1.87494024 0.075480239 Machine hours 0.0364197 0.01392943 2.61458987 0.016594445 No. Of set ups 0.399793 0.16425923 2.43391475 0.024435781 From the table, direct labour hours do not have significant impact on overhead costs because of its associated p-value. The higher regression coefficient for number of set ups compared to machine hours then identifies number of set-ups as the strongest cost driver. Section C: Advantages and disadvantages of using a spreadsheet package to compile statistical calculations Operating spreadsheets in calculations has both advantages and disadvantages with which users interact. One of the associated advantages is the ease with which an individual can learn about spreadsheet features and apply the features in analysis. Readily available functions for calculations facilitates these as users can easily identify the functions’ locations and use them at the click of a tab. Spreadsheet functions are also organized into categories with distinct features and this allows users to predict, with ease, location of a function for use. The overall display of input values also offer references to facilitate learning of spreadsheet operations. Another identified advantage with spreadsheets is the ability to develop them within a short period and with significant ease. Users’ ability to design and develop spreadsheet also means that the data analysis tools are cheap. Spreadsheets are also flexible to handle both simple and complex data analysis and this makes them applicable to diversified users and professions. Programmed features of spreadsheets with “built-in capabilities, functions, and tools” also offer an advantage of spreadsheet and empower users in data analysis (Lau and Gugden 2011, p. 247). Most spreadsheets are also readily available with operation systems and from vendors and the accessibility promote training on the spreadsheet applications and familiarity with different spreadsheet features (Tennent and Friend 2011, n.p.). Spreadsheets also allows for multiple representation of data through “numerical, graphical, and algebraic representations” (Stacey, Chick and Kendal 2004, p. 107). One of the disadvantages of spreadsheet is difficulty in analyzing developed data from the spreadsheets and this is majorly because background information of the applications and statistical information may be necessary in interpretations (Etalle and Truszczynski 2006, p. 394; Rhodes 2010, p. 93). Differences between spreadsheet functions and formulas and the normally applicable algebraic formulas also complicate their use in data analysis (Stacey, Chick and Kendal 2004, p. 107). It is also difficult to modify or verify analysis results from spreadsheets (Lau and Gugden 2011, p. 248). References Etalle, S and Truszczynski, M 2006, Logic programming: 22nd international conference, ICLP 2006, Seattle, WA, USA, August 17-20, 2006, proceedings, Springer, Lexington. Lau, M and Gugden, S 2011, Applications of spreadsheets in education- The amazing power of a simple tool, Bentham Science Publishers, Sharja. Rhodes, J 2010, Setting budgets and managing cashflows, Straightforward co Ltd., Brington. Stacey, K Chick, H and Kendal, M 2004, The future of the teaching and learning of algebra: The 12th ICMI study, Springer, Norwell. Tennent, J and Friend, G 2011, Guide to business modeling, John Wiley & Sons, Hoboken. Read More