Understanding Research in Education - Assignment Example

Student’s name Course code+name Professor’s name University name City, State Date of submission Table of Content Page Number 1.0 Introduction 3 1.1 Definitions 3 1.3 Conceptual differences between RPI and CPI 4 1.4 Methodological differences 4 2.1 RPI inflation graphs from 1987-2011 5 2.2 Index calculation 6 2.3 Inflation graph from 1987 of Tobacco 8 3.1 How they are being interpreted 9 4.1 Correlation and other statistical measures 10 4.3 Statistical interpretation and manipulation 12 Bibliography 15 Appendices 16 1.0 Introduction 1.1 Business and financial analysis Whenever one is sick he needs to seek doctors assistance but not all doctors can assist you diagnose your problem. Each and every doctor has specific area of specialization hence you will be only able to be assisted with a specific doctor. Likewise in business one needs to seek the assistance from the right person like an accountant, financial analyst or market and product analyst. For this purpose to be achieved there is need for accurate and reliable data to be collected before the required analysis to be done. The quality of a good data includes; Timely-up-to-date, Accurate and easy to understand, Fit for the purpose intended for and can be verifiable. Qualitative research is more details and more details compared to quantitative research work is merely comparison. 1.2 Definitions Index can be defined as statistical measure of change in an economy or a securities market. In the point of market, an index can be defined as imaginary portfolio of the securities representing a particular market or a portion of it. Different market has different index calculation methodology. 1.3 Conceptual differences between RPI and CPI The history of RPI began as compensation index and later developed to safe guard ordinary worker from increase in price was resulted from the world war. Only in later days RPI was used as the main elements for measuring the level of domestic inflation within the country. CPI was launched in the late 1996 and its main intention was to compare the measure of inflation using the internationally laid methodologist and structures. In most cases CPI is the target by the government on the inflation rate. 1.4 Methodological differences RPI CPI It covers both the actual goods and services in their indices It covers most of the cost which are excluded in the CPI, some include; Road fund licenses TV licenses Council licenses housing depreciation, Building insurance. mortgage interest payment The RPI includes a price index for cars which is based entirely on used car prices It normally covers some of the charges which are excluded in RPI and these include; University accommodation fees Stock brokers fees Unit trust fees Foreign student tuition The index for the purchase of new cars in the CPI is quality adjusted and based on actual published prices for new cars The population is based on the expenditure which are covered by indexes and some sources of data from the expenditure Normally represent the majority of the household who are private and excluded people with higher earning and pension household within the country and mostly depends on the state benefits. Expenditure data (or ‘weights’) used to represent this population are derived from a number of sources but mainly from ONS’s Living Costs and Food Survey It represents all the house hold and also institutional household. Expenditure data (or ‘weights’) used to represent this population are derived from National Accounts data and can therefore differ in magnitude from the RPI weights for similar components Index calculation formula In its initial state, RPI is calculated using arithmetic mean. Two different methods are applied to two different items. Eg. If one price increased by 25% from the base period (which=100) and another decreased by 20% their new index values would be 125 and 80 respectively. The AM of these is (125 +80)/2= 102.5. this show an average price increase of 2.5% In the same point, CPI normally uses geometrical mean, which takes the value of the adjacent, thus it is calculated as; 125*80= square root of1000= 100, indicating that there has been no change in prices. An advantageous property of the geometric mean is that it can better reflect changes in consumer spending patterns elative to changes in the price of goods and services 2.0 PARTII 2.1 RPI inflation graphs from 1987-2011 Fig.1.0 2.2 Index calculation Index calculation Indices year cash percentage increase 25% Percentages decrease 20% 1 100 125 80 102.5 2.5 2 97.5 122.5 77.5 100 2.5 3 95 120 75 97.5 2.5 Fig.1.1 In the next three year I will need$ (2.5*3) in order to adjust for the inflation. 2.3 Inflation graph from 1987 of Tobacco Fig.1.2 3.0 Topic three Moving average usually is an indicator which shows the normal average value of the share prices over a given period of time. When you are calculating the moving average of given shares in analysis you take the average divided by the given period of time. With changes in prices, the average prices also continuously changes either upwards or downward. The moving averages are five in number, they include; arithmetic exponential, variable, triangular and weighted moving averages. The most significant different between the above moving averages is the weight assigned to them at different time intervals. 3.1 How they are being interpreted One of the easiest and simple ways to interpret the moving average of the share stock is by comparing the moving average prices by the share prices themselves. The relationship will give an investor hint on whether it should be sold or bought. Example, when security or share prices is higher than the moving average then it signals a buy, that is when investor should buy the share at that point but when the share prices fall below the moving average then it signalizes a sell,, that is the security should be sold out (Lovell and Lawson, 1970). 4.0 Topic four 4.1 Correlation and other statistical measures Correlation is also used heavily in the business analysis more so in financial sectors where the management would like to know how different factors influences their income, if we take an example where different people borrow loans from financial institution, some do default in paying back the loans while other pay back, so the correlation can give an hint on how these factors influences the loan performance. Regression is also very useful statistical instrument in the analysis of business performance. 4.2 Correlation analysis Correlations heiht01 Shoes size2 Height Pearson Correlation 1 .640 Sig. (2-tailed) . .171 N 6 6 Shoes size Pearson Correlation .640 1 Sig. (2-tailed) .171 . N 6 6 Fig.1.4 Scattered graphs Fig. 1.5 From the above figure, it can be seen that, with increase in advertisement, sales increases rapidly, it can be concluded that any slight increase in advertisement it positively affect the sales of the product hence direct relationship can be derived from the above analysis. Business should be encouraged to increase their advertisement if they want to increase their sales at any given point. Because their direct proportionality between the advertisement expense and the sale then it is true to say 15m increase will be (15*410)/10.1= 680.9802 4.3 Class exercise HEIGHT SHOES_SIZE  Mean  19.04348  5.782609  Median  18.00000  6.000000  Maximum  37.00000  9.000000  Minimum  12.00000  2.000000  Std. Dev.  6.071380  2.173320  Skewness  1.662836  0.013608  Kurtosis  5.671822  1.802221  Jarque-Bera  17.44045  1.375607  Probability  0.000163  0.502679  Sum  438.0000  133.0000  Sum Sq. Dev.  810.9565  103.9130  Observations  23  23 Fig 1.6 Correlations SHOESIZE HEIGHT SHOESIZE Pearson Correlation 1 -.041 Sig. (2-tailed) . .854 N 23 23 HEIGHT Pearson Correlation -.041 1 Sig. (2-tailed) .854 . N 23 23 Fig 1.7 There is relationship between the height and the shoes size, this is brought by the fact that the significance level is 1 and negative 0.041 while in height it is the vise versa. Scattered graphs Fig 1.8 Question: a) . Using the information you have generated, suggest an approximation for height for someone with shoe size 11 1/2, and 6 1/2. Using the correlation line graph above, the shoe size of 11.5 will approximately have a height of 180cm while the one with 6.5 will have 120cm. b) . What can you say about the data split between genders as well? Usually female have shoes of smaller sizes compared to male partners this can be attributed only to the height factor in it. c) If you were stocking shoes for a shop selling clothes to women over 175cms what size of shoes would you tend to stock? I would stock shoes of size between of 6.5 to 8.5 mostly because their height is evenly distributed along that size. d) Use the data to write a short description of your observations. Descritive Statistics N Std. Deviation Kurtosis Statistic Statistic Statistic Statistic Std. Error Statistic Statistic Statistic Std. Error Statistic Std. Error SHOESIZE 23 2.00 9.00 5.7826 .4532 2.17332 4.723 .015 .481 -1.191 .935 HEIGHT 23 12.00 37.00 19.0435 1.2660 6.07138 36.862 1.781 .481 3.673 .935 MALE 22 .00 68.00 7.7273 2.9048 13.62484 185.636 4.506 .491 20.817 .953 FEMALE 21 .00 9.00 3.6667 .6912 3.16754 10.033 .557 .501 -1.010 .972 Task Relationship between income, level of crime rate and the age group Descriptive statistics AGE_0_17_2005 AGE_65_PLUS_2005 CRIME_RATE_100_000_POPUL PER_CAPITA_INCOME_2005 UNINSURED_2005  Mean  32089.05  14199.51  6779.716  21420.46  17508.98  Median  11318.00  7862.000  5142.000  18659.00  7543.000  Maximum  254354.0  95466.00  18716.00  40626.00  113696.0  Minimum  756.0000  633.0000  26.00000  12528.00  685.0000  Std. Dev.  50978.09  18511.49  5083.504  7374.813  23327.55  Skewness  3.228200  3.220591  0.735551  1.504185  2.744455  Kurtosis  14.02520  14.31348  2.281448  4.390565  11.33521  Jarque-Bera  550.9358  572.0073  9.046540  37.07086  336.1628  Probability  0.000000  0.000000  0.010853  0.000000  0.000000  Sum  2599213.  1150160.  549157.0  1735057.  1418227.  Sum Sq. Dev.  2.08E+11  2.74E+10  2.07E+09  4.35E+09  4.35E+10 The descriptive data analysis gives the superficial relationship of the factors in the study. The standard deviation explains the variation between the crime rate and the level of income at different age groups and the population of a given area. It can be seen from the data above that crime rates are high among younger people with low income and highly densely populated area. The skewness shows how the different in variation is so wide. Correlation AGE_0_17_2005 AGE_65_PLUS_2005 CRIME_RATE_100_000_POPUL PER_CAPITA_INCOME_2005 UNINSURED_2005 AGE_0_17_2005 1 0.980144939924286 0.005153303453032496 0.5751310731872737 0.9876147954050899 AGE_65_PLUS_2005 0.980144939924286 1 0.05959595055279493 0.551049119651416 0.9873955788741031 CRIME_RATE_100_000_POPUL 0.005153303453032496 0.05959595055279493 1 -0.2072233192135845 0.08882193632814925 PER_CAPITA_INCOME_2005 0.5751310731872737 0.551049119651416 -0.2072233192135845 1 0.5361953453828868 UNINSURED_2005 0.9876147954050899 0.9873955788741031 0.08882193632814925 0.5361953453828868 1 Correlation simply explains the relationship between the age and the crime rate in relationship e is with age factor. The crime rate is inversely proportional to the level of income and age group. Low income encourages crimes and places which are highly populated are characterized by high crime rates. 4.3 Statistical interpretation and manipulation Statistical data can be manipulated in many different ways using different statistical software which includes, spss, eviews, SigmaXL, and much other software’s to satisfy any given condition. One can calculate descriptive data and inferential data from the given sample of the data.( Lovell and Lawson, 1970). Conclusion Using statistical data one is in a position to analyze the business performance determining its weakness, strength, opportunity and the threats the business might face. Using financial state met, the financial ratios explain the performance of the business in terms of the financial management and the investors can actually asses whether the investment opportunity is viable or not. Bibliography Lovell, K and Lawson, K.(1970). Understanding Research in Education. Edinsburth: University of London Press Appendix I Tobacco Prices 1990-2012 RRP £ per 20 Tax Burden £ per 20 Tax Incidence 1990 1.65 1.20 73 1991 1.80 1.31 73 1992 2.08 1.55 75 1993 2.27 1.70 75 1994 2.52 1.93 77 1995 2.70 2.09 77 1996 2.89 2.26 78 1997 3.08 2.42 79 1998 3.36 2.65 79 1999 3.64 2.88 79 2000 3.88 3.08 79 2001 4.22 3.37 80 2002 4.39 3.46 79 2003 4.51 3.55 79 2004 4.65 3.65 78 2005 4.82 3.77 78 2006 5.05 3.91 77 2007 5.33 4.07 76 2008 5.44 4.18 77 2009 5.67 4.34 77 2010 6.29 4.83 77 2011 6.63 5.08 77 2012 7.09 5.45 77 Appendix II sales and advertise relationship Sales £m Advertising Expenditure £000 8.5 210 9.2 250 7.9 290 8.6 330 9.4 370 10.1 410 Appendixes III economic relationship Age 0-17 2005 Age 65 Plus 2005 Crime rate/100,000 population (2005) Uninsured 2005 Per Capita Income 2005 10,220 8,486 2,382 7,722 18793 26,328 12,965 6,542 16,360 39459 26,328 12,965 6,542 16,360 39459 34,284 18,069 6,233 23,062 40557 34,284 18,069 6,233 23,062 40557 13,700 8,874 3,736 8,293 22944 8,816 5,793 2,850 6,995 14101 15,315 7,571 2,047 12,958 17600 20,601 11,068 5,323 11,080 30250 20,601 11,068 5,323 11,080 30250 56,634 21,094 26 27,174 23374 3,172 2,474 1,598 2,890 12780 9,911 4,776 1,224 5,573 20442 10,497 9,048 10,770 7,543 15929 1,414 1,106 18,716 899 17848 254,354 95,466 2,231 113,696 40626 254,354 95,466 2,231 113,696 40626 4,455 2,777 804 3,068 15340 14,679 9,531 2,709 8,782 22345 10,585 7,862 2,024 7,030 20861 4,862 2,778 10,500 2,988 18659 8,545 4,432 1,777 4,670 21289 1,543 1,309 12,722 1,025 17285 8,705 4,905 3,606 4,964 22025 4,740 3,394 4,045 4,441 13231 34,770 16,190 8,593 20,039 22008 8,026 3,751 7,060 6,605 14228 6,013 3,257 14,141 3,505 17912 903 927 1,380 693 20936 2,120 3,447 2,207 1,831 21826 5,856 4,336 2,023 4,743 14778 73,681 14,645 3,491 26,293 36739 1,050 1,134 3,467 920 13000 3,505 3,011 3,193 2,315 20050 5,118 3,954 2,194 3,609 17742 15,009 10,659 9,759 9,529 18198 11,318 2,546 9,044 4,589 26238 6,680 5,572 2,876 5,128 16927 3,325 3,160 12,089 2,477 16251 23,752 11,620 2,994 10,982 28515 2,986 2,722 3,901 2,282 15710 52,337 18,934 10,942 27,247 19202 52,337 18,934 10,942 27,247 19202 61,722 23,462 10,990 38,113 17687 61,722 23,462 10,990 38,113 17687 61,722 23,462 10,990 38,113 17687 61,722 23,462 10,990 38,113 17687 61,722 23,462 10,990 38,113 17687 3,705 2,842 666 2,488 19203 756 633 10,190 685 13140 4,976 3,836 3,423 4,326 12528 8,370 5,164 17,339 5,413 16196 8,370 5,164 17,339 5,413 16196 25,764 13,052 13,163 14,990 18311 6,950 5,708 2,641 5,082 20011 254,354 95,466 2,231 113,696 40626 8,842 7,313 3,427 7,276 15283 45,442 25,702 15,888 30,683 18448 45,442 25,702 15,888 30,683 18448 45,442 25,702 15,888 30,683 18448 45,442 25,702 15,888 30,683 18448 45,442 25,702 15,888 30,683 18448 45,442 25,702 15,888 30,683 18448 45,442 25,702 15,888 30,683 18448 21,257 14,864 11,497 14,242 19285 10,585 7,862 2,024 7,030 20861 4,878 4,137 6,024 3,101 21768 7,987 6,593 2,028 5,719 16891 6,615 6,230 2,781 5,146 17780 3,761 3,480 4,042 3,051 16888 20,668 8,672 7,226 11,560 19207 2,243 1,813 2,036 1,547 20215 8,842 7,313 3,427 7,276 15283 117,919 42,482 7,483 58,144 24514 117,919 42,482 7,483 58,144 24514 15577 7343 3689 7694 29132 11594 10173 5142 6787 31112 16132 7845 4694 8579 22794 102654 20857 5798 40669 31083 8188 6855 2382 5179 20681 5890 4612 2326 4199 17892 Read More