Introduction: The following study may not be true: Studies show that men that are above average in weight are more likely to have a stroke than people with average weight. Steel structured buildings are less likely to fall then brick-structured buildings. How do we interpret this? Explanatory and Response Variables
The three Principles that guide Statistics:
Plot the data, and then add numerical summaries.
Look for overall patterns and deviations from those patterns. 3.
When there’s a regular overall pattern, use a simpler model to describe it. Two variables you must consider:
Response Variable: Measures an outcome of a study.
Example: Stroke Rate/Steel Buildings versus Brick Buildings Falling Rate Explanatory Variable: May help explain or influence changes in a response variable. Example: Weight/Location
Explanatory variables/ Response variables can be called independent/response variables. These variables can be explained very simple. A response variable is what happens after the explanatory variable takes place. Sometimes if values of each variable are not specified, that or bother variables may not exist. Example: A study shows three babies drinking 3 different doses of soda to see effects of hyperactivity. What is the response/explanatory variable?
Response Variable: Hyperactivity effects
Explanatory Variable: Dose amount of soda
Reasoning: The amount of soda (explanatory variable) can explain why the three babies (subjects) can experience more or less hyperactivity (response variable). More soda may correlate to more hyperactivity while less soda may correlate to less hyperactivity. Note: Sometimes the explanatory variable may not cause “direct” changes to the response variable. Just because you have a high math score on the SAT does not mean you have a high writing score. There is no cause-and-effect in that situation. Displaying Relationships: Scatterplots
What is the most useful graph for displaying the relationship between two...
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