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Compare and contrast Pearson and Spearman’s Correlation and illustrate an example (not used by previous students) of where each would be used.

Answer questions Minimum 100 words each and reference (questions #1-2) KEEP questions WITH ANSWER PLEASE ANSWER ALL THE QUESTIONS IN FULL DETAIL

1. Compare and contrast Pearson and Spearman’s Correlation and illustrate an example (not used by previous students) of where each would be used.

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2. Check out R^2. How does it relate to linear regression?)

A minimum of 75 words each question and References (IF NEEDED)(Response #1 – 7) KEEP RESPONSE WITH ANSWER

Make sure the Responses includes the Following: (a) an understanding of the weekly content as supported by a scholarly resource, (b) the provision of a probing question. (c) stay on topic

1. The standard error of estimate or (SEE) measures the distance of data from the regression line. The further away data points come from the regression line the less the regression line is in accuracy. The regression line is not as reliable when this occurs. A SEE is another version of standard deviation (Privitera, G.J., 2018).. 

2. The standard error of estimate, also known as the standard error of the regression, measures the accuracy of the predictions made with the regression line. The difference between the actual score and the predicted score (Y-Y’). It tells you the measurement of the distance the data point is from the regression line. Although similar to R^2 in providing a goodness-of-fits measurement, SEE provides a more exact number of measurement than R^2 providing a percentage. An example read was that r^2 can say that a car went 70% faster. This sounds fast, but we may not know the initial speed. For example, 70% increase of an initial speed of 20 MPH is 34MPH. SEE will give you a precise measurement of the car going exactly 65 MPH faster.

3. The Standard Error of Estimate is the measure of variation of an observation made around the computed regression line. The SEE is used to check the accuracy of predictions made with the regression line. (Privitera, G.J., 2018).

The regression line is the line that best fits the data. The overall distance from the line to the points (variable values) plotted on a graph is the smallest. The regression line is a line used to minimize the squared deviations of predictions.

The results are two variables which are used to check the relation between the two. It helps make future predictions and decisions. Standard of error for a regression line is a measure of how far in general points are (vertically) from the line. “Usually around two-thirds of the points will be within one standard error from the regression line. This also tells you the typical amount of error you would see when making forecasts using the regression line. An approximate formula is as follows: Standard error for regression line ~ SD of Y values x √1-r².”

4. according to Privitera (2018) A positive slope would be when one factor’s value increases and so does the second factor, whereas a negative slope is when one factor increases while the other decreases. Reading this leads me to believe that the above described table of data is in fact a negative slope. When reviewing this data and placing it on a graph one would see the slope going down whereas a positive one would be going up.

5. In reviewing a table of data in a study, that stated that the table data represents a linear relationship one notices that one is a factor of increase and second is of decrease.  It would be a description of having a linear relationship of as in a negative slope and not a increases.  When in using a straight line would be to being able to measure in one part of the factor imports on how another impacts.  So that the values of both factors would be impacted in the same way and manner, then the slope.  May be considered as a slope as the value in one factor of being positive on the increase of the values in factors.  As in the second being an decreases on this types of values that would be considered and viewed as a negative slope.This is how the linear relationship is generated as being with a negative outcome on a slope.

6.  This linear relationshiop would be described as having a negative relationship. With the relationship of y=mx+b, we can find that if both factors were to increase, the linear relationship would be an upward slope. However, if the relationship has a negative factor involved, the slope will be downward (in our case) signifying that the relationship is negative. In our case, we are learning that “m” is negative. This is because since y and x determine the axis points and we already know that as one increases, the other decreases, we know that if x is growing, y is lowering and vice versa. If we had a positive relationship, we would have a situation where as one of y or x grew, the other would do the same as well (an upward slope to the right of the graph).

7. This graph would have a negative correlation, meaning as one factor increases, the second factor decreases. For example, when X and Y go in opposite directions, the slope is negative. This linear relationship would be described as having a negative slope. If variables X and Y have a negative correlation (or are negatively correlated), as X increases in value, Y will decrease; similarly, if X decreases in value, Y will increase. According to Privitera (2018), the regression line will go downhill like a skier. graph would have a negative correlation, meaning as one factor increases, the second factor decreases. For example, when X and Y go in opposite directions, the slope is negative. This linear relationship would be described as having a negative slope. If variables X and Y have a negative correlation (or are negatively correlated), as X increases in value, Y will decrease; similarly, if X decreases in value, Y will increase. According to Privitera (2018), the regression line will go downhill like a skier.

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