Time	Distance	Distance	Distance	Distance	Distance	Average	Standard	Precision	Time Squared
	Trial 1	Trial 2	Trial 3	Trial 4	Trial 5	Distance	Deviation	Stdev/Ave	(s*s)
(s)	(m)	(m)	(m)	(m)	(m)	(m)	(m)	%
0	0	0	0	0	0	0	0	0	0
0.5	1.31	1.19	1.27	1.3	1.15	1.24	0.078	0.063	0.25
0.75	2.94	2.59	3	2.82	2.08	2.69	0.37	0.138	0.5625
1	5.21	4.41	4.81	5.18	4.41	4.8	0.39	0.081	1
1.25	8.24	6.57	8.38	8.78	7.77	7.95	0.85	0.107	1.5625
1.5	11.68	10.51	11.72	12.4	9.59	11.18	1.1188	0.1	2.25

1. Compute the data reduction in Table 2 including the average of each data values, the standard deviation. Calculate the precision for each y value by taking its corresponding standard deviation and dividing it by its average value. Also compute the value of the time squared (t^2) and place this in the Data Table. This will be used for graphical analysis.
2. Make a plot of the "Average y" versus "Time". This graph should be a scatter plot. Y is the dependent variable (y-axis) and Time is the independent variable (x-axis). Make sure you include title, axis labels and units on your graph. This graph is showing how the position changes in time. The change of position in time is the velocity.
3. Plot a graph of "Average y" versus "t^2". Since we know that the equation of motion for an object in free fall starting from rest is y = gt², where g is the acceleration due to gravity, plotting Average y vs t^2 should give us a straight line with slope g/2. (Average y vs t^2 is the equation of a parabola, which has the general form y = ax²).
4. Determine the slope of this by using the trendline function. The trendline is an Excel function that can extract a linear relationship from a set of data using the least-squares fit method. Please select "add trendline equation" so it shows on your plot from step #3. Remember, to use the proper significant figures for this slope. Since our data (y values) have 2 significant figures (while time only has 1, we are assuming that is accurate to greater the 2 significant figures), we are allowed to use the minimum of the significant figures for the data used in our reduction algorithm. (However, for some statistical functional analysis, we can add one more significant figure.)
5. From the slope determined from the trendline, calculate the percent error of the value of g, the gravitational constant. (Use the accepted value of 'g' as 9.8 m/s²).

[Percent Error = [(Experimental Determined Value - Theoretical Value)/ Theoretical Value ] * 100] (This is the same equation listed in the instructions)

1. Calculate the precision of your data by taking the standard deviation at each time step and divide it by the average for that time step. Multiply the result by 100 to get a percentage.