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canvas.cwu.edu Born Primitive Body e Preview Page > of 6 - ZOOM+ answer a-e, and 1 pt. for explaining each answer) )Does there appear to be a positive, negative, or no relationship? How can you tell? b) Is there any evidence of a non-linear relationship? Why or why not, and what effect will this have on the correlation coefficient (in other words, why does this matter)? c) Is there any evidence of issues such as heteroscedasticity or outliers? Why or why not, and what effect will they likely have if they're there? d) Roughly, what range would you expect r to be in? Why? (You should explain what r least your first answer). is on at e) Given all of your answers above, how accurate would you expect to be in predicting future scores based on X (i.e., would you expect the Standard Error of Estimation to be high, low, or moderate)? If you have said that there is no relationship, what would the Standard Error of Estimation be equal to (don't need to calculate a value here - recall that there is an upper limit to the SEoE)Explanation / Answer
a) Does there appear to be a positive, negative, or no relationship? How can you tell?
It has negative relationship. The direction of the slope if downwards. Median house prices are decreasing with increasing percent lower status.
b) Is there any evidence of a non-linear relationship? Why or why not, and what effect will this have on the correlation coefficient (in other words, why does this matter)?
The association cannot be described by a simple straight line because of the higher values of the median house prices and dip of the plot at around 20 median house prices. So it can be best described by either an exponential or a quadratic graph which can be decided one we have values of data points.
The non-linear graph will increase the value of correlation coefficient because most of the data could be described the fit. The linear model won't do justice to most of the data points and thus correlation coefficient will small and the association will be weak however non linear model will better explain and increase correlation.
c) Is there any evidence of issues such as heteroscedasticity or outliers? Why or why not, and what effect will they likely have if they're there?
Most of the data is tightly bounded other than few dispersed points at higher percent lower status where data points are dispersed and few data points at higher median house prices. But mostly there are no outliers
The word “heteroscedasticity” comes from the Greek, and quite literally means data with a different (hetero) dispersion (skedasis). If your graph has a rough cone shape, you’re probably dealing with heteroscedasticity.
However, our graph does not have a cone shape, so we are not dealing with heteroscedasticity
d) Roughly, what range would you expect r2 to be in? Why? (You should explain what r2 is on at least your first answer).
The range of the r^2 could be between 0.7-0.9.
The coefficient of determination, r2 is the square of the correlation coefficient, r. The coefficient of determination is equal to the percent of the variation in one variable that is accounted for (predicted) by the other variable. The greater the proportion of explained variation, the closer are the y values and predicted y values, hence the stronger the relationship.
e) Given all of your answers above, how accurate would you expect to be in predicting future ? scores based on X (i.e., would you expect the Standard Error of Estimation to be high, low, or moderate)? If you have said that there is no relationship, what would the Standard Error of Estimation be equal to (don't need to calculate a value here - recall that there is an upper limit to the SEoE)
The standard error of the estimate is a measure of the accuracy of predictions made with a regression line or curve.
Standard Error of Estimation would be low. As the predicted data will be the good fit of the data. So the difference between predicted and Y value will be less.
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