Project work of SAS VIYA and STATA -- 2
Budget: £20 – £250 GBP
Introduction
➢ Provide a brief explanation for the methodology, such as data, the definition of dependent,
independent, and control variables, the objective of the analyses, and the baseline model (as
explained in the Main Regression Analysis section).
o The app revenue and app price should be used in the natural-logarithm-transformed
version in all analyses.
Descriptive Analysis
➢ Provide a two-way table that depicts the summary statistics of the variables for subsamples of
game and non-game apps, as well as the full sample in one table. Briefly discuss the results.
➢ Apply an appropriate test to evaluate if there is any statistically significant difference (at 0.05
significance level) across categories regarding the app revenue (logged). Briefly discuss the
results.
➢ Apply an appropriate test to evaluate if there is any statistically significant difference (at 0.05
significance level) between games and non-game apps regarding the app revenue (logged).
Briefly discuss the results.
➢ Provide the correlation matrix of the main variables. Briefly discuss the results.
Exploratory Analysis
➢ Inspect the data graphically, such as visual summary statistics, check the distribution/skewness
of variables, pre-check the possibility of outliers, and pre-check the relationship between the
dependent and independent variables, etc. The details and types of graphs are your decision—
the objective is to provide a concise yet informative inspection of the data before running the
regression. You may pick up a few of the above-mentioned list of potential graphs (or other
graphs), which describe various aspects of the data efficiently.
Main Regression Analysis:
➢ Conduct an OLS regression to estimate the effect of app rating, app price (logged), monetization
strategies, and age target on the app revenue (logged) while controlling the number of available
languages and app main category. This will be the baseline model. Carefully interpret and
discuss the results (e.g., R-squared, the statistical significance of coefficients, and the effect
size).
o Looking at the regression results, discuss which monetization strategy is the best for
revenue generation. Use a proper graph to enhance your discussion.
o Looking at the regression results, discuss whether it is better to target all ages (a broader
segment) or better to focus on specific age levels. Use a proper graph to enhance your
discussion.
➢ Modify the baseline model to evaluate the differential effect of app rating on app revenue
(logged) for different monetization strategies. Discuss the results and explain which
monetization strategy is the best for high-quality apps. Which one is the worst? You can use a
proper graph to enhance your discussion.
Diagnostics and Robustness Analysis:
➢ Apply diagnostic analyses on the baseline model to check the potential heteroskedasticity and
apply an appropriate remedy if needed. Briefly compare the new results with the original results
of the baseline.
➢ Investigate the possibility of a quadratic effect of the app price (logged) on the app revenue
(logged) and discuss the result. You can use graphical illustrations to enhance your discussion.
According to the results, for revenue generation, is it better to set a low price (to increase app
installs), a high price (to boost revenue per install), or somewhere in the middle?
➢ Explain potential endogeneity problems in the baseline model. Discuss specifically related to
the model (i.e., not a generic explanation of endogeneity).
o How can you use other available variables in the dataset to improve your model?
Discuss their relevance.
o If you were able to collect panel data (meaning, multiple periods of data) for these apps,
discuss how it could mitigate the endogeneity problems and enhance causal inference
of results
The deadline for this project is more than 2 weeks, providing ample time for the freelancer to complete the tasks.
➢ Provide a brief explanation for the methodology, such as data, the definition of dependent,
independent, and control variables, the objective of the analyses, and the baseline model (as
explained in the Main Regression Analysis section).
o The app revenue and app price should be used in the natural-logarithm-transformed
version in all analyses.
Descriptive Analysis
➢ Provide a two-way table that depicts the summary statistics of the variables for subsamples of
game and non-game apps, as well as the full sample in one table. Briefly discuss the results.
➢ Apply an appropriate test to evaluate if there is any statistically significant difference (at 0.05
significance level) across categories regarding the app revenue (logged). Briefly discuss the
results.
➢ Apply an appropriate test to evaluate if there is any statistically significant difference (at 0.05
significance level) between games and non-game apps regarding the app revenue (logged).
Briefly discuss the results.
➢ Provide the correlation matrix of the main variables. Briefly discuss the results.
Exploratory Analysis
➢ Inspect the data graphically, such as visual summary statistics, check the distribution/skewness
of variables, pre-check the possibility of outliers, and pre-check the relationship between the
dependent and independent variables, etc. The details and types of graphs are your decision—
the objective is to provide a concise yet informative inspection of the data before running the
regression. You may pick up a few of the above-mentioned list of potential graphs (or other
graphs), which describe various aspects of the data efficiently.
Main Regression Analysis:
➢ Conduct an OLS regression to estimate the effect of app rating, app price (logged), monetization
strategies, and age target on the app revenue (logged) while controlling the number of available
languages and app main category. This will be the baseline model. Carefully interpret and
discuss the results (e.g., R-squared, the statistical significance of coefficients, and the effect
size).
o Looking at the regression results, discuss which monetization strategy is the best for
revenue generation. Use a proper graph to enhance your discussion.
o Looking at the regression results, discuss whether it is better to target all ages (a broader
segment) or better to focus on specific age levels. Use a proper graph to enhance your
discussion.
➢ Modify the baseline model to evaluate the differential effect of app rating on app revenue
(logged) for different monetization strategies. Discuss the results and explain which
monetization strategy is the best for high-quality apps. Which one is the worst? You can use a
proper graph to enhance your discussion.
Diagnostics and Robustness Analysis:
➢ Apply diagnostic analyses on the baseline model to check the potential heteroskedasticity and
apply an appropriate remedy if needed. Briefly compare the new results with the original results
of the baseline.
➢ Investigate the possibility of a quadratic effect of the app price (logged) on the app revenue
(logged) and discuss the result. You can use graphical illustrations to enhance your discussion.
According to the results, for revenue generation, is it better to set a low price (to increase app
installs), a high price (to boost revenue per install), or somewhere in the middle?
➢ Explain potential endogeneity problems in the baseline model. Discuss specifically related to
the model (i.e., not a generic explanation of endogeneity).
o How can you use other available variables in the dataset to improve your model?
Discuss their relevance.
o If you were able to collect panel data (meaning, multiple periods of data) for these apps,
discuss how it could mitigate the endogeneity problems and enhance causal inference
of results
The deadline for this project is more than 2 weeks, providing ample time for the freelancer to complete the tasks.