Sport in the Community Conference - Research Paper Example

Part 1. Data: The data collected from the questionnaire 'Southwood: Sport in the Community Conference.' 2. Frequency Tables: The frequency distribution of all questions is shown in appendix II. The frequency table of question one concerning the hotel respondents stayed in indicated that 30% of all respondents stayed in the Metropolitan, while 26% stayed in the Royal, 21% stayed in the Grand, and 20% stayed in Crescent. The frequency table of question two showed that 68% of all respondents booked their hotels through the conference booking form, while the remaining 32% booked independently. The frequency table of question three A indicated that 42% of all respondents consider efficiency of check-in good, while 30% consider it poor, 17% consider it very good and the remaining 11% consider it very poor. The frequency table of question three B indicated that 42% of all respondents considered cleanliness of rooms to be very good, 40% considered it good, 12% considered poor and the remaining 6% considered it very poor. The frequency table of question three C indicated that 46% of respondents considered facilities in the room they stayed in to be good, while 33% considered it very good, 16% considered it poor, and the remaining 5% considered it very poor. The frequency table of question three D indicated that 42% of all respondents considered overall comfort to be good, 32% considered it very good, 14% considered it poor, and the remaining 11% considered it very poor. The frequency table of question four A indicated that 42% of all correspondents considered meeting rooms of the conference to be very good, while 39% considered it good, 14% considered it poor, and the remaining 5% considered it very poor. The frequency table of question four B indicated that the cleanliness of meeting rooms were considered very good by 37% of respondents, while 33% considered it good, 17% considered it poor, with the remaining 13% considered it very poor. The frequency table of question C indicated that the quality of audio-visual equipment is considered poor by 41%, good by 28%, very poor by 18%, and very good by 13%. The frequency table of answers to question number 5 A indicated that 17% of respondents considered the waiting time in the queue for lunch at the conference in Tuesday is 10 minutes, 14% considered it 8 minutes, 11% considered it 11minutes, 10% considered it 12 minutes, and the remaining respondents considered different waiting times. The frequency table of answers to question number five B indicated that 20% of all respondents considered the waiting time for lunch at the conference is 8 minutes, 19% considered it 9 minutes, 16% considered it 7 minutes. The frequency table of question five C indicated that 22% of all respondents considered the waiting time for lunch on Thursday to be 8 minutes, 17% considered it 9 minutes. The frequency table of answers to question 6 indicated that 51% of all respondents were females and 49% were male. The histogram of question one is shown in the following figure. The graph shows that most participants stayed in hotels number 2, 3, 4 and 1 and few stayed outside these hotels. Figure 1: Histogram showing frequecny distribution of Question one. 3. Descriptive Statistics: Analysis of the data collected in response to the question of the questionnaire has been conducted. Appendix one contain the descriptive statistics of these questions. The mode of the answer to question number one is 2. This means that most respondents stayed in hotel number two the Metropolitan. The mode of question number two is one. This means that more respondents booked through the conference rather than independently. The mean of answers to question number three A is 2.35. This means that the efficiency of check-in is good to poor. The confidence level of variable is from 1.23 to 2.54 meaning that 95% of all respondents rated check-in efficiency as very good to good. The mean of answers to question number three B is 1.82. This means that cleanliness of room nearest to good. The confidence level of variable is from 1.65 to 3.47 meaning that 95% of all respondents rated check-in efficiency as good to very poor. The mean of answers to question three C is 1.93. This means that facilities in room are nearest to good. The mean of answers to question number three D is 2.04. This means that overall comfort is good as indicated by respondents. The mean of answers to question number four A is 1.82. This means that comfort of meeting rooms is mostly good. The mean of answers to question number four B is 2.06. This means that cleanliness of meeting rooms is good. The mean of answers to question number four C is 2.64. This means that quality of audio-visual equipment is generally poor. The mean waiting time to queue for lunch at the conference in Tuesday, Wednesday, and Thursday are 9.6, 7.8, and 8 minutes. The mode of question number six is 2. This means that most of respondents are females. Cross Tabulated Frequency: The cross-tabulated frequency of question number one concerning the hotel the respondent has stayed in and in relation to efficiency of check in. The table is present in appendix III. The table shows that 42.9% of residents of hotel number one: The Grand, consider the efficiency of check-in as good, while 28.6% consider it poor. The table also shows that equal number of respondents of hotel number two; the Metropolitan consider the efficiency of check-in as very good, good and poor. The table also shows that equal number of respondents of hotel number three; the Royal consider the efficiency of check-in as good, poor and very poor. The table presents the opinion of respondents who stayed in hotel number four; the Crescent. Equal number of respondents considers the efficiency of check-in as very good, good and poor. Overall, 42% of all respondents consider the efficiency of check-in as good, while 30% consider it poor, 17% consider it very good and the remaining 11% consider it very poor. Part 2: Correlation between Total Number of Passengers and other variables: The correlation of variables is shown in appendix IV. The table shows the strongest positive correlation between total number of passengers going through a specific airport and the number of passenger using that airport to travel outside the EU of .925. The p-value of the correlation is less than .05 which indicates the significance of the correlation. The table then shows another strong positive correlation between total number of passengers traveling through a specific airport and the number of passengers using this airport to travel within the EU of .841. The p-value of this correlation is less than .05 indicating the significance of this correlation. Scatter Diagram: The following scatter diagram displays the layout and relationship between the total number of passengers going through a specific airport and the number of passengers using this airport to travel out of EU. Figure 2: Scatter Diagram of the number of passenger going through an airport and number of passenger using this airport to travel outside of EU The scatter diagram shows the presence of no outliers as most of the data lies closer to straight line going through most of the data. Regression Analysis: The regression analysis included the dependent variable of total number of passengers going through a specific airport. The independent variables were the number of passengers going through the airport to travel within EU, and the number of passengers going through this airport to travel out of EU. The results of the regression analysis are placed in appendix V. The results indicate that both independent variables are significantly significant as their p-values are less than .05 (selected alpha value). The adjust R-squared value of the regression analysis is .897 indicating that this regression model has the ability to predict 89.7% of the total number of passengers going through an airport given the number of passengers going through this airport to travel within the EU and the number of passengers going through this airport to travel outside of the EU. This leads to the following regression equation of the relationship between total passenger and intra EU and Extra EU. Total Transport = 6684 + .793 x intra EU27 + 1.155 x Extra EU27 Prediction of Total Transport: Example One: Given the following values: Number of passengers going through this airport to travel within the EU = 10347 Number of passengers going through this airport to travel outside of the EU = 5860 Total number of passengers = 6684 + .793 x 10347 + 1.155 x 5860 = 21654 Example Two: Given the following values: Number of passengers going through this airport to travel within the EU = 4627 Number of passengers going through this airport to travel outside of the EU = 1511 Total number of passengers = 6684 + .793 x 4627 + 1.155 x 1511 = 12097 Outlier Airport: The scatter diagram of the relationship between total number of people going through the airport and number of passengers going through the airport travelling within EU demonstrates the airports that are further from the best fit line. Figure 3: Scatter Diagram of the number of passenger going through an airport and number of passenger using this airport to travel outside of EU The Madrid/Barjas airport in Spain does not fit the general relationship pattern. This is because the value predicted by the regression model is the most further away from the real number of total passengers going through the airport. Barcelone, Spain also does not fit the regression model. Part 3: Time Series Analysis and Forecasting Time series forecasting (TSF) attempts to predict the future based on past data (Chase & Jacobs 2006). This report will utilize four different methods used to forecast time series: Simple Moving Average (SMA). Data: Consumer expenditure on recreation and culture, measure in ' million for the UK is measured in the following graph. This data was collected from the UK Statistics Authority (2008). The data is in current prices and is not seasonally adjusted. Figure 4: Graph of Consumer expenditure on recreation and culture in ' million for the UK. The above graph shows the general upward trend in consumer expenditure. This trend is accepted since the data is in current prices. The graph also shows the repeated increase in the fourth quarter of every year. Simple Moving Average Simple moving average is used to average a time series by removing the random fluctuation for forecasting future values. SMA do not account for seasonal changes. The duration of the moving average can best be determined according to the type of application data to forecast. Long time periods gives smoother response by removing random variations but react slower to changes in the data as it lags the trend. Short time periods produce more oscillation but closely follow the trend. SMA is calculated by averaging the most recent number of actual values. SMA is calculated by using the following equation (Chase & Jacobs 2006): Where Ft Forecast for coming period At-1 Actual value in the past At-2, At-3', Actual values two, three,' periods ago. N Number of periods to be averaged Since the moving average do not account for seasonal changes, the data for each quarter is grouped together to form a single column. The SMA of the last three values (SMA3) from the last quarters for each quarter calculations is shown in the Appendix VI. As shown in the appendix the following table includes the predicted values for the four coming quarters as follows: Table1: Predicted values for the next four Quarters using SMA3 2008 Quarters Predicted Value ' million Q1 22597 Q2 23888 Q3 23653 Q4 26529 The following graph shows the predicted four quarters as appended to the original time series data. Figure 5: Time series data plus four future predicted consumer expenditure on recreation and culture in ' million for the UK using SMA3. Figure 5 shows that the predicted values do not follow on with the upward general trend of consumer expenditure of the time series. The SMA is used applying last two values rather than three last values. The calculations of the predicted future four quarters are shown in appendix VII. The values of the predicted future quarters are shown in the following table. Table2: Predicted values for the next four Quarters using SMA2 2008 Quarters Predicted Value ' million Q1 22959 Q2 24415 Q3 24114 Q4 27054 The following graph shows the time series of consumer expenditure after including the values of the four future values predicted using SMA2. Figure 6: Time series data plus four future predicted consumer expenditure on recreation and culture in ' million for the UK using SMA2. Figure 6 shows that the predicted values still do not follow on with the upward general trend of consumer expenditure. However the predicted values using SMA2 are higher in value than those predicted using the SMA3. Thus SMA2 is better in predicting future quarter consumer expenditures than SMA3. References Chase, R. & Jacobs R. (2006). Operation Management for Competitive Advantage. McGraw Hill Eleventh Edition. UK Statistics Authority. (2008). Consumer expenditure on recreation and culture, measure in ' million for the UK, Obtained from http://www.statistics.gov.uk/statbase/TSDdownload1.asp Appendix 1: Descriptive Statistics of part 1 ' Qu 1 hotel Qu2 hotel booking Qu 3a hotel check in Qu 3b hotel cleanliness Qu3c hotel Facilities Qu3d hotel comfort Mean 2.54 1.32 2.35 1.82 1.93 2.04 Standard Error 0.112295 0.046883 0.089188 0.086899 0.083188 0.095261 Median 2 1 2 2 2 2 Mode 2 1 2 1 2 2 Standard Deviation 1.122947 0.468826 0.891883 0.868995 0.831878 0.952615 Sample Variance 1.26101 0.219798 0.795455 0.755152 0.69202 0.907475 Kurtosis -0.91602 -1.41476 -0.65097 0.253257 -0.01253 -0.33465 Skewness 0.2039 0.783546 0.20376 0.925972 0.670009 0.705996 Range 4 1 3 3 3 3 Minimum 1 1 1 1 1 1 Maximum 5 2 4 4 4 4 Sum 254 132 235 182 193 204 Count 100 100 100 100 100 100 Confidence Level(95.0%) 0.222817 0.093025 0.176969 0.172427 0.165063 0.189019 Qu4a conference comfort Qu 4b) conference cleanliness qu 4c) audio-visual equipment Q5 Tuesday Q5 Wednesday Q5 Thursday Qu 6 gender 1.82 2.06 2.64 9.59 7.76 8.03 1.51 0.085729 0.103299 0.092682 0.287832 0.204554 0.211992 0.050242 2 2 3 10 8 8 2 1 1 3 10 8 8 2 0.857292 1.032991 0.926817 2.878324 2.045542 2.119915 0.502418 0.734949 1.067071 0.85899 8.284747 4.184242 4.49404 0.252424 0.068029 -0.79211 -0.75128 -0.74259 0.26629 -0.16432 -2.03955 0.848592 0.607458 -0.22645 -0.08181 -0.2461 -0.26078 -0.04062 3 3 3 12 10 10 1 1 1 1 3 2 3 1 4 4 4 15 12 13 2 182 206 264 959 776 803 151 100 100 100 100 100 100 100 0.170105 0.204968 0.183901 0.571122 0.40588 0.420637 0.099691 Appendix 2: Frequency' Table of all questions Appendix III Appendix IV Appendix V: Regression Analysis of Part 2 Appendix VI: Simple Moving Average for each Quarter ' Q1 SMA3 Q2 ' Q3 ' Q4 ' 2002 18795 ' 19454 ' 18988 ' 21885 ' 2003 19347 ' 20463 ' 20463 ' 24113 ' 2004 20878 ' 22666 ' 22034 ' 25479 ' 2005 21872 19673.33 22835 20861 22730 20495 26682 23825.67 2006 22444 20699 24101 21988 23773 21742.33 27426 25424.67 2007 23475 21731.33 24730 23200.67 24456 22845.67 ' 26529 2008 ' 22597 ' 23888.67 ' 23653 ' 27054 Read More