Gym Survey a Brief Analysis

Question: Discuss about the Gym Survey a Brief Analysis. Answer: Introduction A gym has conducted a survey based on its customers to understand their underlying behaviour. Further, a brief analysis of the existing literature on the subject is also carried out in order to identify the likely bivariate and other trends. Also, the given exercise also carries out a bivariate analysis based on the various categorical and numerical variables identified in the survey. Based on this analysis, the report offers managerial advice. Additionally, the report also focuses on the limitations of quantitative research coupled with potential abuse of statistics. Literature Review There is empirical evidence to support the idea that preferences with regards to gym and underlying behaviour is closely linked with the gender. This is particularly visible with regards to the motive with the two sexes approach the gym with the females looking to lose weight and the males focusing on enhancing the overall muscle and strength. Besides, females also exhibit a more balance exercise schedule which is apparent in their behaviour as it is part of a fitness regime for them (SMH, 2012). Hence, females tend to do exercises outside the gym also using alternative therapies of fitness such as yoga. Further, females in general tend to be more sensitive about their bodies and hence more embarrassed if it is out of shape (Sorgen, nd). Bivariate Analysis The given section tends to focus on bivariate analysis. In this regards the following three cases need to be explored. Case 1: Two categorical variables (Sample Size = 100) The two categorical variables of interest based on the result of the given survey and the associated literature review are the gender of the customer and reasons for going to the gym. The relevant two way table based on the given sample data is indicated below. Particulars Reason to go to the GYM Stress Relief Lose Weight Gain Strength Other Total Male 4 9 42 6 61 Female 3 27 3 6 39 Total 7 36 45 12 100 The proportion of the males and females belonging to the different category is shown below. Particulars Reason to go to the GYM Stress Relief Lose Weight Gain Strength Other Male 0.066 0.148 0.689 0.098 Female 0.077 0.692 0.077 0.154 It is apparent from the above that there seems to a stark difference in the reasons with which males and females go to the gym which seems to validate the existing literature review. For males, the primary reason to the gym is to gain strength which is referred to by almost 69% of the male customers. This is in sharp contrast with females where losing weight is the primary priority with almost 69% female customers vote. Case 2: One categorical variable and one numerical variable (Sample Size = 100) The categorical variable in the given case is assumed to be gender while the numerical variable is taken as the BMI. The relevant mean and standard deviation of the two groups is captured in a tabular manner shown below. Particulars Mean BMI Standard Deviation Male 26.7 3.2 Female 25.8 2.7 From the above, it is apparent that in general males tend to have a higher average BMI in comparison with the females. Also, the BMI of males comparatively have a higher range in comparison with females which is apparent from the respective values of standard deviation. The distribution of each of the variables can be derived from the respective graphs of the BMI for the two genders which is indicated below. It is apparent from the above graph that the distribution is non-normal and the data is skewed towards the left. Also, the mode, median and mean for the above data does not coincide 9 Eriksson and Kovalainen, 2015). It is apparent from the above graph that the distribution seems to be approximately normal as there is only one peak and the graph seems symmetric about the centre. Also, a bell shape curve is the result. However, the given data has some skew which is primarily because of approximated normal distribution (Hair et. al., 2015). Case 3: Two numerical variables (Sample Size = 100) The two numerical variables selected for this task are BMI and minutes on weight machine. The requisite scatterplot of the above mentioned two variables based on the given gym survey is presented below It is apparent from the above that there seems to be no definite relationship between BMI and the time that the given individual depends on doing weight exercises. Thus, it reflects that there are other variables which are a more accurate representation of the time spent on various exercises such as cardio, weights which would be driven by the gender and also the aim with which the given customer goes to the gym. The mean and standard deviation of the given variables is indicated below. Particulars Mean Standard deviation BMI 26.3 3.1 Minutes on weight machine 25 16 It is apparent from the above table that with regards to BMI the range of values is comparatively lower when compared to the minutes on weight machine. This is on expected lines considering the stark differences in preferences of gender and underlying objective. Managerial Advice Based on the above bivariate analysis and also literature review, it is imperative that the management should be sensitive to be gender preferences as these may be starkly different. Additionally, proper coaching and mentoring should be provided to individuals based upon their goal. Also, in view of the given data where 44% of the customers support the idea of a unisex gym, it is requisite that dedicated timings must be recommended especially females as these form a majority of these 44% customers. Besides, attention needs to paid on the equipment variety as about 68% of the customers consider it a significant parameter. Proposed Change Analysis Confidence interval estimation Customers supported the changes proposed = 748 Total customers participating in survey = 1000 Thus, p = 748/1000 =0.748 Standard Error (SE) = 0.748*(1-0.748)/1000 = 0.01373 The relevant z value for 95% confidence level = 1.96 (Hillier, 2006) 95% confidence interval lower level = 0.748 1.96*0.01373 = 0.721 95% confidence interval lower level = 0.748 + 1.96*0.01373 = 0.774 Thus, it may be concluded with 95% confidence that proportion of customers rendering support to the changes proposed would be in the interval (0.721, 0.774) (Hastie, Tibshirani and Friedman, 2001). Hypothesis Testing Ho: p = 0.5 H1: p 0.5 The level of significance is assumed to be 5% Z statistic (calculated) = = (0.748-0.5)/ 0.01373= 18.06 At 95% confidence level, critical value of z comes out to be 1.645 and -1.645. However, The z statistic as computed above does not fall within the critical interval which leads to rejection of null hypothesis. Hence, acceptance of alternate hypothesis takes place (Eriksson and Kovalainen, 2015). Thus, it may be concluded that proposed changes have support of the majority of the customers based on the gym survey results. Conclusion Based on the discussion in the above sections, it is noteworthy that gender preferences for gym customers are significant. Additionally, a critical factor which drives the exercise schedule is the underlying goal to be achieved. However, BMI does not act as a significant driver of the underlying exercise composition of individuals which is more driven from their end goal. Also, there seems to be majority support for the proposed changes which the gym management intends to bring. It is essential that these must incorporate the customer feedback obtained particularly with regards to emphasis on equipment and other behavioural preferences. Abuses of Statistics With regards to the questionnaires, possible abuse could have been possible based on how the same was filled by the respondent. For instance, if the questionnaire was filled in a group setting, the responses to certain questions may be modified such as one that dealt with whether the respondent is ashamed of the body. Also, the responses of the respondent in presence of peer group or instructor may be biased (Hair et. al., 2015). Further, it is likely that the data set may be biased owing to representation of only two nationals (i.e. US and Germany) and also because the demographics of the respondents may be significantly different from the population. Also, with regards to the responses to the proposed changes, there may be sample bias to include those who are in favour so as to project a positive image (Hillier, 2006). Besides, factors such as BMI could potentially lead to misleading conclusions. Limitations of Quantitative Research Quantitative research may not lead to general theories and relationships especially when the behaviour is being studied. One instance is the relationship between BMI and weight exercise duration which does not lead to any conclusive result. Besides, when there are categorical variables, quantitative research is limited. Also, the methods of data collection used for quantitative research may lead to biased result as has been pointed in the earlier section also (Eriksson and Kovalainen, 2015). Qualitative research is applied when the researcher needs to explore the underlying motivations behind the relationships explored. In the given case, if the gym wants to understand the prime motivations between the gender differences then qualitative research using interviews as the data collection method would be preferred (Taylor, Bogdan and DeVault, 2015). References Eriksson, P. and Kovalainen, A. 2015, Quantitative methods in business research, London: Sage Publications Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P. and Page, M. J. 2015,Essentials of business research methods, New York: Routledge. Hastie, T., Tibshirani, R. and Friedman, J. 2001, The Elements of Statistical Learning, New York: Springer Publications, Hillier, F. 2006, Introduction to Operations Research, New York: McGraw Hill Publications, Sorgen, C. nd, His and Hers Fitness, [Online] Available at https://www.webmd.com/fitness-exercise/features/his-hers-fitness#1 (Accessed January 25, 2017) SMH 2012, Gender and the gym, [Online] Available at https://www.smh.com.au/lifestyle/diet-and-fitness/chew-on-this/gender-and-the-gym-20120827-24w32.html (Accessed January 25, 2017) Taylor, S.J., Bogdan, R. and DeVault, M. 2015, Introduction to qualitative research methods: A guidebook and resource, New York: John Wiley Sons