1. Complete the following in regard to the Armspan and Height factors in the data set: Armspan 143.0 184.5 163.0 161.5 172.0 182.0 172.5 145.0 180.0 179.0 180.0 160.0 166.5 157.5 156.0 184.0 187.0 163.0 174.0 154.0 162.0 169.0 164.5 157.5 165.0 165.0 153.0 151.5 158.0 169.0 168.0 156.0 170.0 189.5 173.0 183.0 177.5 168.0 188.0 164.0 183.0 169.5 176.5 167.0 153.0 175.0 170.0 173.5 172.0 160.0 165.5 153.0 157.5 160.0 171.0 180.0 154.0 173.0 165.0 175.5 160.0 170.0 178.0 156.0 157.0 179.5 160.0 177.0 191.5 165.0 156.0 172.5 180.0 183.0 161.5 162.5 170.0 165.0 172.0 172.0 186.0 172.0 176.0 167.0 182.0 189.0 163.5 153.5 174.0 165.0 155.5 157.0   Height 145.0 180.0 161.5 160.0 170.0 183.0 172.5 142.0 180.0 172.5 182.5 159.0 168.0 159.0 160.0 179.0 189.0 162.5 181.0 160.0 157.5 169.0 162.5 152.5 163.0 167.5 157.5 149.5 157.5 165.0 170.0 167.0 170.0 188.0 177.0 185.5 179.5 170.0 187.0 163.0 181.0 166.5 178.0 167.0 150.0 177.5 169.0 172.5 169.0 160.0 170.0 155.0 155.0 162.5 170.0 183.0 154.0 172.5 160.0 172.5 159.0 168.0 170.5 160.0 161.5 175.0 156.0 175.0 184.0 161.0 160.0 164.0 178.0 186.0 161.0 162.5 169.0 165.0 180.0 176.0 183.0 171.0 175.5 165.0 175.0 188.0 167.5 167.5 171.0 167.5 154.0 158.0   a. In the region to the right, produce a scatterplot of the Armspan versus Height data (remember this means Height runs along the horizontal axis as the independent variable and Armspan along the vertical axis as the dependent variable.)  Adjust your horizontal and vertical scaling to keep the primary focus of the graph to the region in which the points lie.  Label the axes and make a descriptive title to the display.  For help, see Unit 3 Excel Guide (Part 2) and the video entitled:  Chapter 5:  How to Create and Analyze Scatterplots. Based upon your scatterplot, briefly discuss below your thoughts on whether the “visual” trend between the individuals’ armspan and height appears linear, curvilinear, or has no general trend at all.    b. Complete the following:   i. Include the graph's trend line and equation (least squares line) on the scatterplot created in part a.  Give the line's equation below and explain within this context what the "x" and "y" variables represent in the equation. ii.Below, explicitly state the slope of your trend line and discuss what the value of the slope signifies in terms of this context.     c. Determine the value of the correlation coefficient (r) for this paired data.  Explain what this value tells you regarding these two variables.  Next, determine the value of the coefficient of determination (r^2) for this paired data.  Explain what this value tells you regarding these two variables.   d. Using the least squares linear prediction equation from part bi. above, predict the armspan of an individual whose height is 195 cm.  Show work to the right.   e. Finally, critique the statement “since the correlation coefficient is strong positive correlation” then this means that “being taller causes one to have a longer armspan.”  Specifically address the issue of “causation” in relation to statistical correlation.

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1. Complete the following in regard to the Armspan and Height factors in the data set:

Armspan
143.0
184.5
163.0
161.5
172.0
182.0
172.5
145.0
180.0
179.0
180.0
160.0
166.5
157.5
156.0
184.0
187.0
163.0
174.0
154.0
162.0
169.0
164.5
157.5
165.0
165.0
153.0
151.5
158.0
169.0
168.0
156.0
170.0
189.5
173.0
183.0
177.5
168.0
188.0
164.0
183.0
169.5
176.5
167.0
153.0
175.0
170.0
173.5
172.0
160.0
165.5
153.0
157.5
160.0
171.0
180.0
154.0
173.0
165.0
175.5
160.0
170.0
178.0
156.0
157.0
179.5
160.0
177.0
191.5
165.0
156.0
172.5
180.0
183.0
161.5
162.5
170.0
165.0
172.0
172.0
186.0
172.0
176.0
167.0
182.0
189.0
163.5
153.5
174.0
165.0
155.5
157.0

 

Height
145.0
180.0
161.5
160.0
170.0
183.0
172.5
142.0
180.0
172.5
182.5
159.0
168.0
159.0
160.0
179.0
189.0
162.5
181.0
160.0
157.5
169.0
162.5
152.5
163.0
167.5
157.5
149.5
157.5
165.0
170.0
167.0
170.0
188.0
177.0
185.5
179.5
170.0
187.0
163.0
181.0
166.5
178.0
167.0
150.0
177.5
169.0
172.5
169.0
160.0
170.0
155.0
155.0
162.5
170.0
183.0
154.0
172.5
160.0
172.5
159.0
168.0
170.5
160.0
161.5
175.0
156.0
175.0
184.0
161.0
160.0
164.0
178.0
186.0
161.0
162.5
169.0
165.0
180.0
176.0
183.0
171.0
175.5
165.0
175.0
188.0
167.5
167.5
171.0
167.5
154.0
158.0

 

a. In the region to the right, produce a scatterplot of the Armspan versus Height data (remember this means Height runs along the horizontal axis as the independent variable and Armspan along the vertical axis as the dependent variable.)  Adjust your horizontal and vertical scaling to keep the primary focus of the graph to the region in which the points lie.  Label the axes and make a descriptive title to the display.  For help, see Unit 3 Excel Guide (Part 2) and the video entitled:  Chapter 5:  How to Create and Analyze Scatterplots.

Based upon your scatterplot, briefly discuss below your thoughts on whether the “visual” trend between the individuals’ armspan and height appears linear, curvilinear, or has no general trend at all. 

 

b. Complete the following:

 

i. Include the graph's trend line and equation (least squares line) on the scatterplot created in part a.  Give the line's equation below and explain within this context what the "x" and "y" variables represent in the equation.

ii.Below, explicitly state the slope of your trend line and discuss what the value of the slope signifies in terms of this context.  

 

c. Determine the value of the correlation coefficient (r) for this paired data.  Explain what this value tells you regarding these two variables.  Next, determine the value of the coefficient of determination (r^2) for this paired data.  Explain what this value tells you regarding these two variables.

 

d. Using the least squares linear prediction equation from part bi. above, predict the armspan of an individual whose height is 195 cm.  Show work to the right.

 

e. Finally, critique the statement “since the correlation coefficient is strong positive correlation” then this means that “being taller causes one to have a longer armspan.”  Specifically address the issue of “causation” in relation to statistical correlation.

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