To better understand the scientific question in our case study, we had to first identify the variables of interest, the population, the sample, the parameters and statistics of the dataset. The variable of interest for this study is tip amounts, as that is what the owner of Sofrittos is analyzing. The population of the data set is tips received by Sofrittos waiters and waitresses, while the sample is the 80 tables of diners collected from the Bureau of Labor Statistics. The parameter of the study is average tip amounts at Sofrittos and the statistic is the tip amounts of the 80 tables found from the Bureau of Labor Statistics. The scientific question that we are addressing is what is the correlation between the amount of tips with variables:
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This is inferential statistics because the data is being used to draw a conclusion on whether or not the owner of Sofrittos should match the minimum server wage of Maryland’s at $3.63 as opposed to keeping it at Delaware’s $2.23. The types of data that we see in this study are mostly quantitative data, with variables: tips, bills, diners, and table minutes in that category. We also have the variable, day, which is nominal data. Our data is cross sectional because it corresponds to a specific time period and not a range of times. The data was not collected by Sofrittos’ owner so it is not primary data, but rather secondary data. This is because it was collected by the Bureau of Labor Statistics and made available for others to use. The data is from a reputable source and is reliable, however there are issues with the data. The data isn’t …show more content…
There is a strong positive correlation associated with minutes dined and the amount of tips given.
This bar chart shows the total amount of tips summed up from the weekend compared to the weekday. As we can see from the graph, the amount of tips received on the weekend is more than double of the tips received during the weekday.
This scatterplot describes the relationship between the two quantitative variables of tips and bills. Tips is the dependent variable while bills is the independent variable. The relationship shown in the graph is a positive correlation, where if the bill is more expensive, the tip becomes larger. It is appropriate to use a normal distribution to model our quantitative data because it is bell-shaped continuous data and because we are not modeling success or failure. The example of our graph modeling table minutes dined and tips is a normal
This is inferential statistics because the data is being used to draw a conclusion on whether or not the owner of Sofrittos should match the minimum server wage of Maryland’s at $3.63 as opposed to keeping it at Delaware’s $2.23. The types of data that we see in this study are mostly quantitative data, with variables: tips, bills, diners, and table minutes in that category. We also have the variable, day, which is nominal data. Our data is cross sectional because it corresponds to a specific time period and not a range of times. The data was not collected by Sofrittos’ owner so it is not primary data, but rather secondary data. This is because it was collected by the Bureau of Labor Statistics and made available for others to use. The data is from a reputable source and is reliable, however there are issues with the data. The data isn’t …show more content…
There is a strong positive correlation associated with minutes dined and the amount of tips given.
This bar chart shows the total amount of tips summed up from the weekend compared to the weekday. As we can see from the graph, the amount of tips received on the weekend is more than double of the tips received during the weekday.
This scatterplot describes the relationship between the two quantitative variables of tips and bills. Tips is the dependent variable while bills is the independent variable. The relationship shown in the graph is a positive correlation, where if the bill is more expensive, the tip becomes larger. It is appropriate to use a normal distribution to model our quantitative data because it is bell-shaped continuous data and because we are not modeling success or failure. The example of our graph modeling table minutes dined and tips is a normal