I am wondering what statistical analysis would be best practice to use if I am comparing data from state to state; for example, I am doing a humorous presentation on Big Foot Sighting, and I need to select which statistical analysis would be best to run the data. So there would be big foot sightings, there would be data collected on each state for the most sightings, and then the second data I would also be looking at is the bear population in each state. So my first part of the questions would be what would the independent variable be? How many dependant variables are there and which statistical analysis would be most appropriate to use in SPSS as well as which graph would be best to use in SPSS?
Good morning,
I am wondering what statistical analysis would be best practice to use if I am comparing data from state to state; for example, I am doing a humorous presentation on Big Foot Sighting, and I need to select which statistical analysis would be best to run the data. So there would be big foot sightings, there would be data collected on each state for the most sightings, and then the second data I would also be looking at is the bear population in each state.
So my first part of the questions would be what would the independent variable be? How many dependant variables are there and which statistical analysis would be most appropriate to use in SPSS as well as which graph would be best to use in SPSS?
ANSWER:
Comparing data from state to state can involve analysis of multiple variables and can be done using different statistical analyzes depending on the research questions and hypotheses. In the case of Big Foot sightings, the independent variable would likely be the state, and the dependent variable would be the number of Big Foot sightings and the bear population in each state.
To analyze the relationship between the number of Big Foot sightings and the bear population in each state, a correlation analysis would be appropriate. This type of analysis can determine whether there is a relationship between two variables, and the strength and direction of that relationship. A scatterplot can be used to graphically display the correlation between two variables, with the number of Big Foot sightings on the y-axis and the bear population on the x-axis. A scatterplot can help visually determine whether there is a positive or negative relationship, and whether there are any outliers that might affect the relationship.
Additionally, regression analysis can be performed to model the relationship between two variables and make predictions based on that model. This can help identify which variable is a better predictor of Big Foot sightings and to what degree. Multiple regression analysis can also be used to control for other variables that may affect the relationship, such as population size or geographic location.
To compare the number of Big Foot sightings and bear populations in different states, descriptive statistical analysis can be performed to calculate measures such as the mean, median, and standard deviation of the two variables for each state. These measures can help identify which states have the highest or lowest numbers of Bigfoot sightings and bear populations and whether there are significant differences between states. Box plots or bar graphs can be used to visualize the distribution of data across different states.
In summary, based on research questions and hypotheses, statistical analyzes such as correlation, regression, and descriptive statistics can be used to compare data from state to state regarding Big Foot sightings and bear populations. Appropriate statistical analysis and graphical presentation will depend on the nature of the data and the research questions being addressed.
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