Data for Part 1 - Period using a Stopwatch Length Period Period Period Period Period (m) Trial 1(s) Trial 2 (s) Trial 3 (s) Trial 4 (s) Trial 5 (s) 0.10 0.41 0.31 0.35 0.32 0.35 0.20 ).43 ).40 0.38 .30 0.37 0.30 0/63 0.72 0.62 0.71 0.68 0.40 1.01 0.86 0.98 0.90 1.12 0.50 1.30 1.09 1.13 1.00 1.35 0.60 1.23 1.19 1.26 1.17 1.15 0.70 1.24 1.33 1.34 1.39 1.29 0.80 N/A N/A N/A N/A N/A Calculations for Part 1 - Period using a Stopwatch Length (m) OT Avg Period Period Trials (s) Avg Period2, T2 (s2) Trial (s) 0.10 0.034871192 0.35 0.121104 0.20 0.043174066 0.38 0.141376 0.30 0.040693980 0.67 0.451584 0.40 0.090686272 0.97 0.948676 0.50 0.131240238 1.17 1.378276 0.60 0.040000000 1.20 1.440000 0.70 0.050358713 1.32 1.737124 0.80 N/A N/A N/APeriod2 vs Length 2.000000 1.800000 y = 2.99x - 0.3077 R2 = 0.9606 1.600000 1.400000 1.200000 1.000000 Avg Period2, T2 (s'] Avg Period* 2, T^ 2 (s* 2) 0.800 000 ......*: Linear (Avg Period^2, T*2 (s^2) ) 0.600000 0.40000 0 0.200000 0.000000 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0. 200000 Length (m) Value of Slope Value of Y- Show full calculation of Show full calculation from Intercept from gexp Calculation Percent Error Graph of Period vs Graph of Period2 Length vs Length 2.99 0.3077 slope - 407 .1. error = gexp - gact y 100 gact = 9.81 m/s gexp Jack gexp . 2.99 = 402 . Cup .1. error = 13.203 - 9.81 x 100 9exp 9.81 294 9up = 4712 = 3.393 x 100 9.81 249 2. 99 ./. error = 34. 591 gexp= 13. 203Data for Part 2 - Period using a SMART TIMER Length Period Period Period (m) Period Period Trial 1(s) Trial 2 (s) Trial 3 (s) Trial 4 (s) Trial 5 (s) 0.10 0.6113 0.6266 0.6135 0.6243 0.6233 0.20 0.8675 0.8687 0.8680 0.8668 8680 0.30 1.1321 1.1315 1.1342 1.1338 1.1339 0.40 1.2884 1.2908 1.2903 1.2899 1.2900 0.50 1.4098 1.4108 1.4114 1.4116 1.4107 0.60 1.5438 1.5455 1.5446 1.5453 1.5455 0.70 1.6468 1.6479 1.6477 1..6467 1.6499 0.80 N/A N/A N/A N/A N/A Calculations for Part 2 - Period using a SMART TIMER Length (m) OT Avg Period Avg Period2, T2 (s2) Period Trials (s) Trial (s) 0.10 0.006175435 0.6198 0.38415204 0.20 0.000629285 0.8678 0.75307684 0.30 0.001086278 1.1331 1.28391561 0.40 0.000803492 1.28988 1.663790414 0.50 00631189 1.41086 1.99052594 0.60 0.00065909 1.54494 2.386839604 0.70 0.001152389 1.6478 2.71524484 0.80 N/A N/A N/APeriod2 vs Length W y = 3.9169x + 0.03... 2.5 RZ = 0.9956.." N Avg Period?, T2(s2) 1.5 9....... Series 1 .......". Linear (Series1) 05 U 0.2 0.4 0.6 0.8 Length (m) Value of Slope Value of Y- Show full calculation of Show full calculation from Intercept Sexp Calculation Percent Error Graph of Period2 from Graph vs Length of Period2 vs Length 3.9169 0.03 slope = 4712 gexp 1. error= gexp - gact X LOO gack gexp. 3. 9169= 412 . . gexp . 1 . error = 10.079- 9.81 X 10O Jexp 9.81 3.9164 gup= 417 2 = . 269 X 100 9.81 3.9164 3.9169 /. error = 2.741 gexp = 10.079Analysis Questions 1. Which method was more precise, the stopwatch or the SMART TIMER? What data supports your claim? Smart timer was a more precise method. 2. Discuss your standard deviations. Did you find that the standard deviation was better for short lengths, longer lengths, or were they the same? Did you find that the stopwatch standard deviations were smaller, larger or the same as the SMART TIMER? Do these results agree with the expectation that the Smart Timer should be more precise? 3. Which method was more accurate, the stopwatch or the photogate? What data supports this claim? 4. Analyze your first graph above to determine if there is random error in your data. What features of the graph illustrate this. 5. Taking the second graph above to represent the 'actual' i.e., expected, results, compare your first and second graphs, as well as the corresponding slopes and y-intercepts to determine if there is systematic error in your data. What type(s) exist and how can you tell