Report on Statistical Analysis

1.      Hypotheses Development

H1: There exists a positive correlation between visits and game.

H2: There exists a positive correlation between visit time and game.

H3: There exists a positive correlation between total time and game.

H4: There exists a positive correlation between advertising and game.

H5: Visits, visit time, total time and advertising have a positive correlation with the game.

2.      Descriptive Statistics

Summary of Statistical Analysis

The values of mean and standard deviation for the study variables are so much fluctuating. It doesn’t depict positive and unbiased responses of the respondents. Skewness and kurtosis values are also helping to determine the normality of the data set. In the above table, separate values for the total of study items are also helping to know about the sum of each item.

3.      Statistical Assumptions

Normality and linearity are some of the statistical assumptions.

·         Skewness and kurtosis helped to assess the normality,

·         Regression analysis is used to test the linearity.

·         The value range for skewness is +1 to -1 and for kurtosis is +3 to -3. In the above-said table, these values are also not up to the mark. For some of the variables, they are within specified range means within an acceptable range. While for others, they are not up to the mark.

4.      Regression Analysis

Linear Regression of Statistical Analysis

Multiple Linear Regression

5.      Overall Findings

For linear regression models, the value of R square provides a measure to goodness-of-fit. It provides with an indication to variance %age for dependent variable due to independent variables. For provided data, R square value for linear regression is 0%, which depicts that the model variable advertising has the least impact on the game. Adjusted R square value provides a comparison between models. A negative value of adjusted R square shows that out of total variation narrated by the regression line, the variation %age is not that significant. For linear regression, the p-value is greater than 0.05, which indicates that the relationship between advertising and game is not statistically significant.

For multiple linear regression, the regression test between multiple independent and a dependent variable narrates that R square value is 0% and adjusted R square value is also negative for multiple regression which means the variables are not related significantly.

For developed hypotheses, a test run helped to determine that p-value for all the variables is greater than 0.05, which means all the variables have an insignificant relationship with the dependent variable. So, the test results tend to reject all the hypotheses as is supported by the study of (Lewis). It is opposite to the discussion of (Activision) and (Yang),who narrated that most of the players believe that advertising is the best fit for the game.

Findings

   References of Statistical Analysis

Activision. "Activision and Nielsen entertainment release results of pioneering research on in-game advertising. Press Release." (2005).

Lewis, B & Porter, L. "In-Game Advertising Effects: Examining player perceptions of advertising schema congruity in a massively multiplayer online role-playing game." Journal of Interactive Advertising 10.2 (2010): 46-60.

Yang, M., Roskos-Ewoldsen, R.D., Dinu, L., & M.Arpan, L. "The Effectiveness of "in-Game" Advertising: Comparing college students' explicit and implicit memory for brand names." Journal of Advertising 35(4) (2006): 143-152.