Background of the Survey
Introduction
The first questionnaire generally talks about traveling. It aims at displaying information about travelling destination, age group and amount used during traveling. The research question that the questionnaire focuses on is to test whether amount used during traveling differed among the different age groups. The questionnaire containing 16 questions that were formulated and the data was collected using interviews. The sample size of the travel data is 2500 observations. A sample size of 250 observations was used to conduct the necessary analysis to answer the research question. Traveling agencies are concerned about ways to improve their services to satisfy their clients. Clients’ satisfaction is one fundamental goal for any organization. Clients’ satisfaction brings success to any business because the business will be receiving referrals and repeat clients. The agencies also want to get more information about their clients in terms of their spending, background, and age. This enables the traveling agencies to input the appropriate pricing for their clients.
Based on the statistical results obtained in Table 1.1, we can deduce that all the passengers spend almost the same similar amount during their traveling. This might be because all the basic needs are offered at the same rate (González-Rodríguez, Colubi & Gil, 2012; Hecke, 2012; Wobbrock et al. 2011; Altan, 2010). Thus I will recommend that the price of the basic needs, e.g. shelter, should be reduced because we can’t imagine that all the travelers come from the able background. Considering Fig 1.2, one can notice that most of the people who travel are of ages between 31 to 45 years. This age range is mostly family people. It is a common routine for families to travel during the holiday. This is an opportunity for travel agencies to create something unique that will attract more families to travel. Fig 1.2 suggests that most travelers travel to Adelaide. The place must be a cool place for travelers to love the place. It is also recommended that different destinations should include a unique feature or activities that can attract. This will prevent people from crowding in one particular area. Fig 1.3 supports the claim that was initially stated that the travelers spent almost a similar amount during their journey. The figure shows equal distribution of the Amount spent during the journey by the travelers. This is also important in determining the revenue added to the economy by the visitors who travel to certain regions.
Summary of Travel Survey Results
Introduction
The second questionnaire generally talks about drug addiction among Generation Y. It aims at displaying information about the people who are undergoing rehabilitation program of the drug addict. The research question on the questionnaire focuses on is determine what made the addict engage in addiction and whether they want help to get off the addiction.T he questionnaire containing nine questions, which were formulated and later, the data was collected using interviews. The sample size of the drug addiction data is 2000 rows. A sample size of 200 observations that were randomly sampled from the original data was used to conduct the necessary analysis to answer the research question. The government have been fighting drugs because drug adduction affects most of the citizens This reduces the productivity of the country. For this reason, the Government have initiated several programs for rehabilitation services. To achieve this, the government needs to get information on how the drug adduction engaged in using drugs. The information about the amount used to purchases the drugs is also important to the government
Fig 1.4 shows the distribution of those who are addicted to drugs, and it is shocking to see that the minor were among the majority of the drug addict (Huang, Cheng & Chiu, 2012; Saavedra, & Bustos, 2010; Hong & Chen, 2011). This shows the need to create awareness for people to avoid such predicaments. The majority of the drug adduct came from the middle class and the rich. This is reasonable since most of them can afford the drugs. Normally, drug addicts use a lot of money to purchase the drug despite their background. All the information gained from this should be taken seriously since a lot of the money that could have been used for one’s development is used to purchase the drugs. According to Fig 1.6, most of the drug addicts started using drugs due to curiosity. Several addicts were introduced into drugs by their friends and family members who were using the drugs. Fig 1.7 shows that majority of the addicts are willing to quit addiction and this shows how the rehabilitation program is a key element in helping the adduct get out of addiction. Fig 8 shows that the amount used for drugs was almost same among the addicts.
References
Altan, M. (2010). Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials & Design, 31(1), 599-604.
González-Rodríguez, G., Colubi, A., & Gil, M. Á. (2012). Fuzzy data treated as functional data: A one-way ANOVA test approach. Computational Statistics & Data Analysis, 56(4), 943-955.
Hecke, T. V. (2012). Power study of anova versus Kruskal-Wallis test. Journal of Statistics and Management Systems, 15(2-3), 241-247.
Hong, W., & Chen, T. S. (2011). Reversible data embedding for high quality images using interpolation and reference pixel distribution mechanism. Journal of Visual Communication and Image Representation, 22(2), 131-140.
Huang, S. C., Cheng, F. C., & Chiu, Y. S. (2012). Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE transactions on image processing, 22(3), 1032-1041.
Saavedra, J. M., & Bustos, B. (2010, September). An improved histogram of edge local orientations for sketch-based image retrieval. In Joint Pattern Recognition Symposium (pp. 432-441). Springer, Berlin, Heidelberg.
Wobbrock, J. O., Findlater, L., Gergle, D., & Higgins, J. J. (2011, May). The aligned rank transform for nonparametric factorial analyses using only anova procedures. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 143-146). ACM
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