04 Apr For this assignment, you must analyze a dataset and provide the results of your analysis. You should not interpret the output at this stage. Please refer to the data file in the wee
INSTRUCTIONS
For this assignment, you must analyze a dataset and provide the results of your analysis. You should not interpret the output at this stage. Please refer to the data file in the week 1 resources.
In the video game dataset provided, you can explore two categorical or grouping variables (independent variables), which include the type of player (police officer or thief) and advertising period (advertising period or no advertising period). You can explore the data to determine if the number of video game visits and/or the amount of visit time (dependent variables) are different for the levels of the two independent variables. If the data are normally distributed, you could use independent samples t-tests as your inferential model to compare the two levels of each independent variable (you would run two separate t-tests). If you analyze both independent variables simultaneously with their interaction term, you will use a two-way analysis of variance.
Your paper should consist of the following components:
- Describe the problem and state the hypotheses to be tested.
- Include the appropriate descriptive statistics and visuals in order to describe the characteristics of the data and include a written summary of the data.
- Address all relevant statistical assumptions and provide a written summary of the findings.
- Describe the results of the inferential analyses implemented to address each hypothesis.
Length: 5-7 pages, not including title and reference pages
References: Include a minimum of 5 scholarly resources.
TIM-7101_VideoGameData
| Date | Visits | VisitTime | TotalTime | Game | Advertising |
| Friday | 0 | 0 | 0 | Police | Yes |
| Saturday | 1 | 0.76 | 0.76 | Police | Yes |
| Sunday | 0 | 0 | 0 | Police | Yes |
| Monday | 0 | 0 | 0 | Police | No |
| Tuesday | 0 | 0 | 0 | Police | No |
| Wednesday | 0 | 0 | 0 | Police | No |
| Thursday | 0 | 0 | 0 | Police | No |
| Friday | 0 | 0 | 0 | Police | No |
| Saturday | 0 | 0 | 0 | Police | No |
| Sunday | 0 | 0 | 0 | Police | No |
| Monday | 6 | 1.33 | 7.95 | Police | Yes |
| Tuesday | 5 | 2.98 | 14.9 | Police | Yes |
| Wednesday | 0 | 0 | 0 | Police | Yes |
| Thursday | 7 | 2.4 | 16.83 | Police | Yes |
| Friday | 0 | 0 | 0 | Police | Yes |
| Saturday | 0 | 0 | 0 | Police | Yes |
| Sunday | 1 | 0.82 | 0.82 | Police | Yes |
| Monday | 8 | 1.93 | 15.45 | Police | Yes |
| Tuesday | 3 | 1.33 | 3.99 | Police | No |
| Wednesday | 0 | 0 | 0 | Police | No |
| Thursday | 0 | 0 | 0 | Police | No |
| Friday | 0 | 0 | 0 | Police | No |
| Friday | 1 | 1.68 | 1.68 | Thief | Yes |
| Saturday | 1 | 0.67 | 0.67 | Thief | Yes |
| Sunday | 0 | 0 | 0 | Thief | Yes |
| Monday | 1 | 1.16 | 1.16 | Thief | No |
| Tuesday | 0 | 0 | 0 | Thief | No |
| Wednesday | 1 | 2.88 | 2.88 | Thief | No |
| Thursday | 0 | 0 | 0 | Thief | No |
| Friday | 0 | 0 | 0 | Thief | No |
| Saturday | 0 | 0 | 0 | Thief | No |
| Sunday | 0 | 0 | 0 | Thief | No |
| Monday | 8 | 1 | 7.97 | Thief | Yes |
| Tuesday | 3 | 1.41 | 4.22 | Thief | Yes |
| Wednesday | 0 | 0 | 0 | Thief | Yes |
| Thursday | 10 | 2.85 | 28.45 | Thief | Yes |
| Friday | 0 | 0 | 0 | Thief | Yes |
| Saturday | 1 | 4.44 | 4.44 | Thief | Yes |
| Sunday | 1 | 1.23 | 1.23 | Thief | Yes |
| Monday | 6 | 2.15 | 12.89 | Thief | Yes |
| Tuesday | 0 | 0 | 0 | Thief | No |
| Wednesday | 0 | 0 | 0 | Thief | No |
| Thursday | 0 | 0 | 0 | Thief | No |
| Friday | 0 | 0 | 0 | Thief | No |
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