The paper performs an inferential statistical test on the given dataset and draws statistical conclusion validity of the research. Game is the first experimental variable in this research; there are kinds of games, i.e. police and thief. Advertising level (none, partial or full) is a secondary experimental variable, and both games have the same advertising level. Furthermore, total visit number for a given day, total visit time, and average visit time per visit in a minute are included in outcome variables.

Exploratory Statistics of Descriptive Statistics and Data Visualization

            Exploratory statistics or exploratory data analysis (EDA) is a critical process that performs initial data investigation so that its patterns could be discovered; anomalies could be spotted, hypothesis and assumptions could be checked with the help of graphical representation and summary statistics (Koller and Friedman).

Correlation on Descriptive Statistics and Data Visualization

Summary of Descriptive Statistics and Data Visualization

Characteristics of Data of Descriptive Statistics and Data Visualization

Level of Measurement of Descriptive Statistics and Data Visualization

·         Visit has quantitative ratio measurement level

·          VisitTime has quantitative ratio measurement level

·         VisitTotalTime has quantitative ratio measurement level

·         Game has qualitative ordinal measurement level

·         Advertising has qualitative measurement level

Distributional Parameters and Visualization of Descriptive Statistics and Data Visualization

·         Best distributional parameter for visit is range and variance

·          Best distributional parameter for VisitTime is range and variance

·         Best distributional parameter for VisitTotalTime is range and variance

·         Best distributional parameter for game is median and mode

·         Best distributional parameter for advertising is median mode

Justification for Statistical Techniques of Descriptive Statistics and Data Visualization

Since the variables are assigned numerical values, the best statistical technique to be performed is summary statistics because it provides all of the relevant distributional parameters such as median, mode, range, and variance (Iliinsky and Steele).

Data Quality Concerns of Descriptive Statistics and Data Visualization

        The number of values in the dataset is not too large, and it has covered a small sample size that might affect the data quality. The large dataset improves data quality (Stirrup, Nandeshwar and Ohmann).

References of Descriptive Statistics and Data Visualization

Iliinsky, Noah and Julie Steele. Designing Data Visualizations: Representing Informational Relationships. "O'Reilly Media, Inc.", 2011.

Koller, Daphne and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.

Stirrup, Jen, Ashutosh Nandeshwar and Ashley Ohmann. Tableau: Creating Interactive Data Visualizations. Packt Publishing Ltd, 2016.