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Case Study: Do Tall People Have An Advantage In Stats II?

960 Words4 Pages

Do Tall People Have an Advantage in Stats II?

Corey Giles, Jacquelyn Spicher, Hope Spuck

In this project, we wanted to explore the correlation between height of Stats II students and their Stats II quarter one grades to determine whether or not tall people had an advantage in the classroom. We utilized a sample survey of twenty five Stats II students to represent the population of Stats II students. A sample survey is to survey some group of individuals by studying only some of its members. In the survey, we asked students to report their quarter one Stats II grade and their height. We hypothesize that as height increases, Stats II quarter one grades will increase with a strong positive correlation of .888. Population is the …show more content…

Our correlation was -0.438. This means that as height increases statistics two grade decreases which is a negative direction. The correlation of -0.438 means that the strength is moderate and negative therefore invalid. Coefficient of determination is the variation in the values of y that is explained by the least squares regression of y on x. Our coefficient of determination was .192. Variation is how the predicted data calculated from the regression equation can vary from the actual value. Our variation was 19.2% meaning that any prediction has a 19.2% variation from the regression line. Outliers are data points that lay outside of the path of the other points. Our data consists of two outliers. One outlier is the person with a height of 67 inches with a 100%. Since they are tall, you would expect them to have a lower grade. They other outlier is the person who has a height of 67.5 inches with a 86%. Since they have a lower grade in Stats II, you would expect them to be taller than they …show more content…

We predict if you are 65 inches tall, then you will have a Statistics II quarter one grade of 94.474% with a variation of 19.2 %. The coefficient of determination is the variation in the values of y that is explained by the least squares regression of y on x. Our r2 equals .192. This means that the variation in each prediction is 19.2% The variation is how the predicted data calculated from the regression equation can vary from the actual value. Our r2 value gave 19.2% variation which means our prediction is invalid. Any prediction has a 19.2% variation.
Lurking variables are any variables that have an important effect on the relationship among variables but are not one of the explanatory variables. One lurking variable of our survey is IQ. Intelligence can have a significant impact on a students grade. While this is not factored into our data it can still impact grades. This is an example of direct causation. Another lurking variable is age. Age has a significant effect on height because as you get older, you get taller. This could have affected our data because we did not ask age and it may have varied. This is an example of direct

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