Part 1: Using the Q Test to Determine Outlier Replicate measurements are encouraged when conducting an experiment and an average value is used rather than just one measurement taken. Sometimes one of those measurements is not consistent with the rest. In such a case, the inconsistent data should be tested to determine whether it should be included in computation or should be discarded. Using the following information, determine if one of the measurements is an outlier and should be discarded. Given that six students conducted the same experiment (separately) to determine the percent by mass of magnesium in magnesium oxide and obtained the following: 60.52%, 60.15%, 59.97%, 68.40%, 59.33%, and 60.98% of magnesium in magnesium oxide. 1. Calculate the average mass of the magnesium obtained by the six students. (Assume you have 100.00 g of each sample.) 2. Examine the data set for a possible outlier. Use the Q test to determine (at the 90% confidence level) whether the measurement that you chose is actually an outlier. 3. If there is an outlier, remove that measurement from the set and then re-compute the average with the remaining five measurements. 4. Calculate the deviation from the mean, the RMD, and the standard deviation for the set of measurements before removing any outlier and after removing an outlier. 5. Calculate the theoretical mass percent of magnesium in magnesium oxide (MgO), and compare it with the averages in Step 1 and Step 3 (if an outlier was identified). 6. Calculate the percent error in the averages in Step 1 and Step 3. Part II: Graphing with Excel In this part of the experiment, you will make two Excel graphs. For the first graph, you will use all the data points shown in Table 1.2 below. The graph must be properly labeled, include a trendline, and have the trendline equation displayed on the graph along with the R value. For the second graph, you will identify and remove any outlier (in the y values) before adding the best line of fit. Instructions for graphing in Excel are given in Appendix 2. Part I: Using the Q Test to Determine Outlier i) Before the Removal of Any Outlier Mass of Mg (g) Deviation (g) Square of Deviation (g) Mean: Number of Measurements (N): Standard Deviation from the Mean, : Show calculation here RMD: Show calculation here Possible Outlier: Q Test: Calculation of Qdata and comparison with Qcritical ii) After the Removal of Any Outlier Mass of Mg (g) Deviation (g) Square of Deviation (g) Mean (without outlier): Number of Measurements (N): Standard Deviation from the Mean, : Show calculation here RMD: Show calculation here Experiment 1: GRAPHING AND ERROR ANALYSIS iii) Calculation of % Error Before and After Removal of Outlier Theoretical % of Mg in MgO Show calculation of theoretical % Mg in MgO Error in mean before removal of outlier: Show calculation of error in mean % Error before removal of outlier: Show calculation of % error Error in mean after removal of outlier: Show calculation of error in mean % Error after removal of outlier: Show calculation of % error Part II: Graphing with Excel The coordinates of the point that is likely to be an outlier: R value before removal of outlier: R value after removal of outlier: Explain what the R values suggest about the data. (See Appendix 1). Attach your graphs to your report.