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A statistical relationship is a mixture of the above two relationships. It’s a relationship that’s part deterministic, and part random. For example, there is a statistical relationship between calorie intake and weight gain. But the relationship isn’t exact: how much weight you gain depends on a lot of other factors and some element of ...
- Master’s Degrees vs. Box Office Revenue. If we collect data for the total number of Master’s degrees issued by universities each year and the total box office revenue generated by year, we would find that the two variables are highly correlated.
- Measles Cases vs. Marriage Rate. If we collect data for the total number of measles cases in the U.S. each year and the marriage rate each year, we would find that the two variables are highly correlated.
- High School Graduates vs. Donut Consumption. If we collect data for the total number of high school graduates and total donut consumption in the U.S. each year, we would find that the two variables are highly correlated.
- Video Game Sales vs. Nuclear Energy Production. If we collect data for the total video game sales each year around the world and the total energy produced by nuclear power plants, we would find that the two variables are highly correlated.
Dec 15, 2022 · This might suggest that observations from two populations, say males and females, were combined but the relationship between the two quantitative variables might be different for the two groups. Going back to Figure 6.1 it appears that there is a moderately strong linear relationship between Beers and BAC – not weak but with some variability ...
- Quadratic Relationships. One of the most common nonlinear relationships in the real world is a quadratic relationship between variables. When plotted on a scatterplot, this relationship typically exhibits a “U” shape.
- Cubic Relationships. Another common nonlinear relationship in the real world is a cubic relationship between variables. When plotted on a scatterplot, this relationship typically has two distinct curves.
- Exponential Relationships. Another common nonlinear relationship in the real world is an exponential relationship between variables. When plotted on a scatterplot, this relationship exhibits a single curve that becomes more pronounced as the variable on the x-axis increases.
- Logarithmic Relationships. Another common nonlinear relationship in the real world is a logarithmic relationship between variables. When plotted on a scatterplot, this relationship exhibits a single curve that becomes less pronounced as the variable on the x-axis increases.
Other examples of statistical relationships include: the positive relationship between height and weight. the positive relationship between alcohol consumed and blood alcohol content. the negative relationship between vital lung capacity and pack-years of smoking. the negative relationship between driving speed and gas mileage.
Jul 14, 2021 · Positive Correlation Examples. Example 1: Height vs. Weight. The correlation between the height of an individual and their weight tends to be positive. In other words, individuals who are taller also tend to weigh more. If we created a scatterplot of height vs. weight, it may look something like this: Example 2: Temperature vs. Ice Cream Sales.
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Mar 26, 2023 · The relationship between x x and y y is called a linear relationship because the points so plotted all lie on a single straight line. The number 95 95 in the equation y = 95x + 32 y = 95 x + 32 is the slope of the line, and measures its steepness. It describes how y changes in response to a change in x x: if x x increases by 1 1 unit then y y ...
