| Logarithmic Regression Model Example 
				
					| Data: 
					 The 
					data below show the average growth rates of 12 Weeping Higan 
					cherry trees planted in Washington, D.C.  At the time 
					of planting, the trees were one year old and were all 6 feet 
					in height.                               
 |  
					| 
 |  | 
						
							
								| Age of Tree(in years)
 | Height(in feet)
 |  
								| 1 | 6 |  
								| 2 | 9.5 |  
								| 3 | 13 |  
								| 4 | 15 |  
								| 5 | 16.5 |  
								| 6 | 17.5 |  
								| 7 | 18.5 |  
								| 8 | 19 |  
								| 9 | 19.5 |  
								| 10 | 19.7 |  
								| 11 | 19.8 |    |  
					| 
						
							| Task: | a.) | Determine a 
							logarithmic 
							regression model equation to represent this data. |  
							|  | b.) | Graph the new equation. |  
							|  | c.) | Decide whether the new equation 
							is a "good fit" to represent this data. |  
							|  | d.) | Interpolate:  What was the 
							average height of the trees at one and one-half 
							years of age?  (to the nearest tenth of 
							a foot) |  
							|  | e.) | Extrapolate:  What is the predicted 
							average height of the trees at 20 years of age?Is 
							this prediction realistic?  (answer to the  nearest tenth of a foot) |  
							|  | f.) | Based upon 
							your observations of this data, what would you 
							predict to be the average height of a mature Higan 
							cherry tree, to the nearest foot? |  
							|  | g.) | If the average 
							height of the trees is 10 feet, what is the age of 
							the trees to the nearest tenth of a year? |  |  
				
						| Step 1.  
						Enter the data into the lists. For basic entry of data, see Basic 
						Commands.
 | 
 |  
						| Step 2. 
						 Create a scatter plot of the data. Go to STATPLOT (2nd Y=) 
						and choose the first plot.  Turn the plot
						ON, set the icon to Scatter 
						Plot (the first one), set Xlist 
						to L1 and Ylist to
						L2 (assuming that is where 
						you stored the data), and select a Mark of your choice.
 
    | 
 |  
						| Step 3.  
						Choose Logarithmic Regression Model. Press STAT, arrow right to
						CALC, and arrow down to
						9: LnReg.  Hit
						ENTER.  When
						LnReg appears on the home 
						screen, type the parameters L1, 
						L2, Y1.  The Y1 
						will put the equation into Y= 
						for you.
 (Y1 comes from VARS → YVARS, #Function, Y1)
             
				         |  The logarithmic regression equation is
 
  (answer to part a)
 |  
						| Step 4.  
						Graph the Exponential Regression Equation from
						Y1. 
 ZOOM #9 ZoomStat to see 
						the graph.
 |  (answer to part b)
 |  
						| Step 5.  
						Is this model a "good fit"? The correlation coefficient, r, is
 .9931293099 which places the correlation into the 
						"strong" category.  (0.8 or greater is a "strong" 
						correlation)
 The coefficient of determination, r 
						2, is
 .9863058261 which means 
						that 98.6% of the total variation in y can be 
						explained by the relationship between x and y.
 Yes, it is a very "good fit".
 (answer 
						to part c)
 | 
			           |  
						| Step 6.  
						Interpolate: (within the data set) What was the 
						average height of the trees at one and one-half years of 
						age?  (to the nearest tenth of a foot)
 
 The logarithmic regression equation is
 
  where x stands for time.   
						Substituting 1.5 for x gives an average height of 
						8.57558088 feet or 8.6 feet.
 (answer to part 
						d)
 | Step 7. Extrapolate 
						data: (beyond data set) What is the predicted 
						average height of the trees at 20 years of age?  Is 
						this prediction realistic?  
					(answer to the nearest tenth 
						of a foot)
 
 The logarithmic regression equation is
 
  where x stands for time.   
					  Substituting 20 for x gives an average height of 
						24.39693273 feet or 24.4 feet.
 Extrapolations far from the stated data are often 
						inaccurate and unreliable.  Nine years away from 
						the data set is a large span of time and the reading of 
						24.4 feet may be "high" based upon the observed leveling 
						nature of the last data entries.
 (answer to part e)
 |  
						| Step 8. Based upon your 
						observations of this data, what would you predict to be 
						the average height of a mature Higan cherry tree, to the 
						nearest foot?
 
 It can be seen from the graph that the growth 
						rate is slowing down (leveling off).  Such a 
						slowing could be interpreted that the trees are reaching 
						their mature height.  The twenty year prediction 
						shows a height of 24 feet.  Any answer from 20 feet 
						to 24 feet would be an acceptable approximation.  
						While extrapolations far from the stated data are often 
						inaccurate, a reading closer to 20 feet may be more 
						accurate.
 (In reality, Higan cherry trees, 
						depending upon the specific specie, reach a mature 
						height anywhere from 15 to 30 feet.)
 (answer to part f)
 | Step 9. 
					    If the average height of 
						the tree is 10 feet, what is the age of the tree to the 
						nearest tenth of a year?  Use your table to 
				        find the age: 
						   (answer to part g -- approx. 1.9 
						years of age)
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