Synthesize Overall Descriptive Trends

In the data set the experimental variable is games. While on the other hand, the secondary experimental variable used in this research is the advertisement level. Furthermore, outcomes variable is total visit number for a given day, total visit time, and average visit time per visit in a minute. Based on given data graphs are developed, and descriptive statistical analysis is made. The descriptive statistical analysis presents a continuously changing trend in the games and advertisement variable.

The above table shows that total time has the highest mean value, followed by visits and visit time. On the other hand, the game has the lowest mean value, while the mean value of advertising is 1. Additionally, the total time has the highest standard deviation, i.e. 5.18, visits have standard deviation as 2.35, and the standard deviation of the game is almost equal to its mean value. All of the variables have 66 values.

The graph mentioned above represents that trend of game and advertisement is not same both vary with a difference in the data set. The variable of the game has most of the values below one while; on the other hand, advertisement values also touched the maximum value of 2. Graphical representation of the hypothesis indicates that both variables have different responses, and only a few relates and match with the responses of the second experimental variable (Boslaugh and Watters). 

The graph mentioned above represents that the total time is greater than the total visit time. The common trend is positive in both variables. The presented above graph shows that the highest value of total time is 30 minutes. While on the other hand, the total number of visits or visit times has greatest value 10 with below 5-minute duration.    

Section 2: Formulation of Potential Hypothesis

         The answer to this question is consist of three key sections, which are the formulation of potential hypothesis, High degree of statistical conclusion Validity, and Information for Final Conclusion. Each section will provide detailed information regarding the concerning topic and statistical outcomes (Asadoorian and Kantarelis). 

Potential hypotheses formulated in the research study can be tested via inferential models which basis on the overall trends identified in the data set. Considering the data set and trend in data set the potential hypothesis are developed. The hypotheses are presented below:

H: Advertisement draw impact on the total number of visits for a game.

H: Advertisement influences over the total time spent in the games.

The high degree of statistical conclusion Validity

The high degree of statistical conclusion validity can be drawn through testing the variable response validity in the statistical software. The results of errors presented in the responses are also a great source of getting information about validity. Further, or, high values of variance can also indicate the possibility of invalidity in data sets. In current data sets, statistical testing is made according to which responses are valid and reliable for the generalization of the statistical outcomes and proven of hypotheses (Lacort).      

Information for Final Conclusion

          The conclusion represents the research findings; therefore, it has significant importance in the research study. While developing the conclusion, I would have to consider several factors. For instance, limitations of the data collection for research study and possible chances of errors in the responses or data set. Furthermore, I would also have to consider information collected from the statistical analysis results (Bagla). Descriptive findings, trends in data set, and other statistical testing can provide important information about the conclusion. Also, a statistical analysis such as standard deviation, mean, median, mode, correlation, variance testing, and regression analysis can provide important information about the conclusion o research study.

References of Synthesize Overall Descriptive Trends

Asadoorian, Malcolm O. and Demetrius Kantarelis. Essentials of Inferential Statistics. University Press of America, 2005.

Bagla, Vandana. Inferential Statistics and Numerical Methods. CreateSpace Independent Publishing Platform, 2018.

Boslaugh, Sarah and Paul Andrew Watters. Statistics in a Nutshell: A Desktop Quick Reference. O'Reilly Media, Inc., 2008.

Lacort, Mercedes Orús. Descriptive and Inferential Statistics - Summaries of theory and Exercises solved. Lulu.com, 2014.