Description
MET CS 555 Assignment 1
The data in the table below give the duration in days of hospital stays of patients admitted to the hospital with C. Difficile. Use the data on the following page to:
(1) Save the data to a excel or CSV file and read into R for analysis. (2 points)
(2) Make a histogram of the duration of days of hospital stays. Ensure the histogram is labelled appropriately. Use a width of 1 day. Describe the shape center and spread of the data. Are there any outliers? (7 points)
(3) Find the mean, median, standard deviation, first and third quartiles, minimum and maximum of the durations of hospital stay in the sample. Summarize these values in a table that you create in EXCEL or WORD. In other words, do *not* simply copy and paste R output. Given the shape of the distribution, what is the best single number summary of the center of the distribution? What is the best single number summary of the spread of the distribution? (6 points)
(4) Assume that the literature on this topic suggests that the distribution of days of hospital stay are normally distributed with a mean of 5 and a standard deviation of 3. Use R to determine the probabilities below based on the normal distribution:
(a) What percentage of patients are in the hospital for less than a week? (2 points)
(b) Recent publications have indicated that hypervirulent strains of C. Difficile are on the rise. Such strains are associated with poor outcomes, including extended hospital stays. An investigator is interested in showing that the average hospital stay durations have increased versus published literature. He has a sample of 10 patients from his hospital. If the published data are consistent with the truth, what is the probability that the sample mean in his sample will be greater than 7 days? (3 points)
Data is on the next page
| 7 | 3 | 5 | 3 | 1 | 5 | 10 | 3 | 4 | 4 |
| 7 | 5 | 8 | 3 | 4 | 1 | 15 | 4 | 5 | 8 |
| 5 | 3 | 2 | 3 | 5 | 9 | 4 | 5 | 6 | 9 |
| 5 | 3 | 6 | 3 | 2 | 6 | 4 | 5 | 5 | 4 |
| 5 | 8 | 4 | 6 | 14 | 4 | 6 | 3 | 2 | 3 |
| 2 | 4 | 6 | 6 | 6 | 8 | 6 | 3 | 4 | 4 |
| 5 | 10 | 4 | 6 | 3 | 9 | 3 | 9 | 4 | 7 |
| 10 | 13 | 4 | 6 | 5 | 10 | 4 | 4 | 9 | 4 |
| 4 | 3 | 6 | 8 | 5 | 7 | 6 | 1 | 3 | 12 |
| 15 | 5 | 2 | 1 | 4 | 4 | 5 | 6 | 4 | 12 |
MET CS 555 Assignment 2
An experiment was conducted to determine the effect of children participating in a given meal preparation on calorie intake for that meal. Data are recorded below. Save the data to a format that can be read into R. Read the data in for analysis. Use R to calculate the quantities and generate the visual summaries requested below.
(1) Summarize the data by whether children participated in the meal preparation or not. Use an appropriately labelled table to show the results. Also include a graphical presentation that shows the distribution of calories for participants vs. non-participants. Describe the shape of each distribution and comment on the similarity (or lack thereof) between the distributions in each population.
(2) Does the mean calorie consumption for those who participated in the meal preparation differ from 425? Formally test at the level using the 5 steps outlined in the module.
(3) Calculate a 90% confidence interval for the mean calorie intake for participants in the meal preparation. Interpret the confidence interval.
(4) Formally test whether or not participants consumed more calories than non-participants at the level using the 5 steps outlined in the module.
(5) Are the assumptions of the test used in (4) met? How do you know?
Calorie Intake for participants
| 435.16 |
| 338.99 |
| 488.73 |
| 590.28 |
| 582.59 |
| 635.21 |
| 249.86 |
| 441.66 |
| 572.43 |
| 357.78 |
| 396.79 |
| 298.38 |
| 282.99 |
| 368.51 |
| 388.59 |
| 256.32 |
| 408.82 |
| 424.94 |
| 477.96 |
| 428.74 |
| 432.52 |
| 428.27 |
| 596.79 |
| 456.30 |
| 446.38 |
Calorie intake for non-participants
| 414.61 |
| 503.46 |
| 425.22 |
| 288.77 |
| 184.00 |
| 299.73 |
| 350.65 |
| 394.94 |
| 261.55 |
| 295.28 |
| 139.69 |
| 462.78 |
| 179.59 |
| 301.75 |
| 436.58 |
| 371.39 |
| 469.02 |
| 378.09 |
| 287.31 |
| 448.55 |
| 332.64 |
| 403.98 |
MET CS 555 Assignment 3
The data in this document gives the number of meals eaten that contain fish (per week) and mercury levels in head hair for 100 fisherman. Save the data to a format that can be read into R. Read the data in for analysis. Use R to calculate the quantities and generate the visual summaries requested below.
(1) Save the data to a file (excel or CSV file) and read it into R memory for analysis. (Q1 – 2 points)
(2) To get a sense of the data, generate a scatterplot (using an appropriate window, label the axes, and title the graph). Consciously decide which variable should be on the -axis and which should be on the y-axis. Using the scatterplot, describe the form, direction, and strength of the association between the variables. (Q2 – 3 points)
(3) Calculate the correlation coefficient. What does the correlation tell us? (Q3 – 2 points)
(4) Find the equation of the least squares regression equation, and write out the equation. Add the regression line to the scatterplot you generated above. (Q4 – 4 points)
(5) What is the estimate for beta_1 ? How can we interpret this value? What is the estimate for beta_0 ? What is the interpretation of this value? (Q5 – 4 points)
(6) Calculate the ANOVA table and the table which gives the standard error of (hat beta 1) . Formally test the hypothesis that beta_1 = 0 using either the F-test or the t-test at the alpha level a=0.10. Either way, present your results using the 5 step procedure as in the course notes. Within your conclusion, calculate the R2 (R squared) value and interpret this.
Also, calculate and interpret the 90% confidence interval for beta_1 . (Q6 – 5 points)
| Number of meals with fish | Total Mercury in mg/g |
| 14 | 4.484 |
| 7 | 4.789 |
| 5 | 3.856 |
| 8 | 4.888 |
| 21 | 10.849 |
| 18 | 6.457 |
| 22 | 11.222 |
| 6 | 4.908 |
| 19 | 10.116 |
| 7 | 3.567 |
| 16 | 6.092 |
| 17 | 3.799 |
| 20 | 6.781 |
| 5 | 5.995 |
| 7 | 1.717 |
| 14 | 4.615 |
| 1 | 3.362 |
| 6 | 3.928 |
| 9 | 1.833 |
| 10 | 5.668 |
| 13 | 4.7 |
| 9 | 2.272 |
| 16 | 4.812 |
| 5 | 1.342 |
| 18 | 6.123 |
| 7 | 4.622 |
| 8 | 7.805 |
| 7 | 2.643 |
| 8 | 6.111 |
| 7 | 2.476 |
| 10 | 4.317 |
| 4 | 1.789 |
| 4 | 2.484 |
| 7 | 1.757 |
| 6 | 1.239 |
| 5 | 5.311 |
| 19 | 6.103 |
| 3 | 1.984 |
| 4 | 2.697 |
| 7 | 0.692 |
| 7 | 2.404 |
| 9 | 1.503 |
| 17 | 8.231 |
| 14 | 5.321 |
| 7 | 3.81 |
| 21 | 1.765 |
| 4 | 0.408 |
| 7 | 3.901 |
| 10 | 0.48 |
| 11 | 3.826 |
| 7 | 3.451 |
| 9 | 2.32 |
| 2 | 4.086 |
| 7 | 2.272 |
| 3 | 2.564 |
| 7 | 7.998 |
| 11 | 5.081 |
| 8 | 0.366 |
| 7 | 2.477 |
| 4 | 5.288 |
| 7 | 5.676 |
| 7 | 2.296 |
| 21 | 6.11 |
| 4 | 1.502 |
| 7 | 3.71 |
| 3 | 2.752 |
| 3 | 0.987 |
| 19 | 10.14 |
| 7 | 1.616 |
| 12 | 4.65 |
| 13 | 7.241 |
| 18 | 9.36 |
| 7 | 3.753 |
| 13 | 4.008 |
| 21 | 5.345 |
| 1 | 2.455 |
| 0 | 0.941 |
| 1 | 2.478 |
| 1 | 3.212 |
| 10 | 5.214 |
| 0 | 1.12 |
| 0 | 0.745 |
| 2 | 4.645 |
| 2 | 4.981 |
| 1 | 2.812 |
| 0 | 0.846 |
| 2 | 5.142 |
| 0 | 1.111 |
| 0 | 1.094 |
| 2 | 2.978 |
| 2 | 3.942 |
| 0 | 1.131 |
| 0 | 0.979 |
| 0 | 0.844 |
| 1 | 2.411 |
| 1 | 2.497 |
| 10 | 3.764 |
| 20 | 8.178 |
| 19 | 7.664 |
| 22 | 9.716 |
MET CS 555 Assignment 4
The data on the next two pages is from a Canadian 1970 census which collected information about specific occupations. Data collected was used to develop a regression model to predict prestige for all occupations. Use R to calculate the quantities and generate the visual summaries requested below.
(1) Save the data to excel or CSV file and read into R for analysis. (1 point)
(2) To get a sense of the data, generate a scatterplot to examine the association between prestige score and years of education. Briefly describe the form, direction, and strength of the association between the variables. Calculate the correlation. (3 points)
(3) Perform a simple linear regression. Generate a residual plot. Assess whether the model assumptions are met. Are there any outliers or influence points? If so, identify them by ID and comment on the effect of each on the regression. (4 points)
(4) Calculate the least squares regression equation that predicts prestige from education, income and percentage of women. Formally test whether the set of these predictors are associated with prestige at the = 0.05 level. (4 points)
(5) If the overall model was significant, summarize the information about the contribution of each variable separately at the same significance level as used for the overall model (no need to do a formal 5-step procedure for each one, just comment on the results of the tests). Provide interpretations for any estimates that were significant. Calculate 95% confidence intervals where appropriate. (4 points)
(6) Generate a residual plot showing the fitted values from the regression against the residuals. Is the fit of the model reasonable? (2 points)
(7) Are there any outliers or influence points? (2 points)
| Occupational Title | Education Level (years) | Income ($) | Percent of Workforce that are Women | Prestige Score |
| GOV_ADMINISTRATORS | 13.11 | 12351 | 11.16 | 68.8 |
| GENERAL_MANAGERS | 12.26 | 25879 | 4.02 | 69.1 |
| ACCOUNTANTS | 12.77 | 9271 | 15.7 | 63.4 |
| PURCHASING_OFFICERS | 11.42 | 8865 | 9.11 | 56.8 |
| CHEMISTS | 14.62 | 8403 | 11.68 | 73.5 |
| PHYSICISTS | 15.64 | 11030 | 5.13 | 77.6 |
| BIOLOGISTS | 15.09 | 8258 | 25.65 | 72.6 |
| ARCHITECTS | 15.44 | 14163 | 2.69 | 78.1 |
| CIVIL_ENGINEERS | 14.52 | 11377 | 1.03 | 73.1 |
| MINING_ENGINEERS | 14.64 | 11023 | 0.94 | 68.8 |
| SURVEYORS | 12.39 | 5902 | 1.91 | 62 |
| DRAUGHTSMEN | 12.3 | 7059 | 7.83 | 60 |
| COMPUTER_PROGRAMERS | 13.83 | 8425 | 15.33 | 53.8 |
| ECONOMISTS | 14.44 | 8049 | 57.31 | 62.2 |
| PSYCHOLOGISTS | 14.36 | 7405 | 48.28 | 74.9 |
| SOCIAL_WORKERS | 14.21 | 6336 | 54.77 | 55.1 |
| LAWYERS | 15.77 | 19263 | 5.13 | 82.3 |
| LIBRARIANS | 14.15 | 6112 | 77.1 | 58.1 |
| VOCATIONAL_COUNSELLORS | 15.22 | 9593 | 34.89 | 58.3 |
| MINISTERS | 14.5 | 4686 | 4.14 | 72.8 |
| UNIVERSITY_TEACHERS | 15.97 | 12480 | 19.59 | 84.6 |
| PRIMARY_SCHOOL_TEACHERS | 13.62 | 5648 | 83.78 | 59.6 |
| SECONDARY_SCHOOL_TEACHERS | 15.08 | 8034 | 46.8 | 66.1 |
| PHYSICIANS | 15.96 | 25308 | 10.56 | 87.2 |
| VETERINARIANS | 15.94 | 14558 | 4.32 | 66.7 |
| OSTEOPATHS_CHIROPRACTORS | 14.71 | 17498 | 6.91 | 68.4 |
| NURSES | 12.46 | 4614 | 96.12 | 64.7 |
| NURSING_AIDES | 9.45 | 3485 | 76.14 | 34.9 |
| PHYSIO_THERAPSTS | 13.62 | 5092 | 82.66 | 72.1 |
| PHARMACISTS | 15.21 | 10432 | 24.71 | 69.3 |
| MEDICAL_TECHNICIANS | 12.79 | 5180 | 76.04 | 67.5 |
| COMMERCIAL_ARTISTS | 11.09 | 6197 | 21.03 | 57.2 |
| RADIO_TV_ANNOUNCERS | 12.71 | 7562 | 11.15 | 57.6 |
| ATHLETES | 11.44 | 8206 | 8.13 | 54.1 |
| SECRETARIES | 11.59 | 4036 | 97.51 | 46 |
| TYPISTS | 11.49 | 3148 | 95.97 | 41.9 |
| BOOKKEEPERS | 11.32 | 4348 | 68.24 | 49.4 |
| TELLERS_CASHIERS | 10.64 | 2448 | 91.76 | 42.3 |
| COMPUTER_OPERATORS | 11.36 | 4330 | 75.92 | 47.7 |
| SHIPPING_CLERKS | 9.17 | 4761 | 11.37 | 30.9 |
| FILE_CLERKS | 12.09 | 3016 | 83.19 | 32.7 |
| RECEPTIONSTS | 11.04 | 2901 | 92.86 | 38.7 |
| MAIL_CARRIERS | 9.22 | 5511 | 7.62 | 36.1 |
| POSTAL_CLERKS | 10.07 | 3739 | 52.27 | 37.2 |
| TELEPHONE_OPERATORS | 10.51 | 3161 | 96.14 | 38.1 |
| COLLECTORS | 11.2 | 4741 | 47.06 | 29.4 |
| CLAIM_ADJUSTORS | 11.13 | 5052 | 56.1 | 51.1 |
| TRAVEL_CLERKS | 11.43 | 6259 | 39.17 | 35.7 |
| OFFICE_CLERKS | 11 | 4075 | 63.23 | 35.6 |
| SALES_SUPERVISORS | 9.84 | 7482 | 17.04 | 41.5 |
| COMMERCIAL_TRAVELLERS | 11.13 | 8780 | 3.16 | 40.2 |
| SALES_CLERKS | 10.05 | 2594 | 67.82 | 26.5 |
| NEWSBOYS | 9.62 | 918 | 7 | 14.8 |
| SERVICE_STATION_ATTENDANT | 9.93 | 2370 | 3.69 | 23.3 |
| INSURANCE__AGENTS | 11.6 | 8131 | 13.09 | 47.3 |
| REAL_ESTATE_SALESMEN | 11.09 | 6992 | 24.44 | 47.1 |
| BUYERS | 11.03 | 7956 | 23.88 | 51.1 |
| FIREFIGHTERS | 9.47 | 8895 | 0 | 43.5 |
| POLICEMEN | 10.93 | 8891 | 1.65 | 51.6 |
| COOKS | 7.74 | 3116 | 52 | 29.7 |
| BARTENDERS | 8.5 | 3930 | 15.51 | 20.2 |
| FUNERAL_DIRECTORS | 10.57 | 7869 | 6.01 | 54.9 |
| BABYSITTERS | 9.46 | 611 | 96.53 | 25.9 |
| LAUNDERERS | 7.33 | 3000 | 69.31 | 20.8 |
| JANITORS | 7.11 | 3472 | 33.57 | 17.3 |
| ELEVATOR_OPERATORS | 7.58 | 3582 | 30.08 | 20.1 |
| FARMERS | 6.84 | 3643 | 3.6 | 44.1 |
| FARM_WORKERS | 8.6 | 1656 | 27.75 | 21.5 |
| ROTARY_WELL_DRILLERS | 8.88 | 6860 | 0 | 35.3 |
| BAKERS | 7.54 | 4199 | 33.3 | 38.9 |
| SLAUGHTERERS_1 | 7.64 | 5134 | 17.26 | 25.2 |
| SLAUGHTERERS_2 | 7.64 | 5134 | 17.26 | 34.8 |
| CANNERS | 7.42 | 1890 | 72.24 | 23.2 |
| TEXTILE_WEAVERS | 6.69 | 4443 | 31.36 | 33.3 |
| TEXTILE_LABOURERS | 6.74 | 3485 | 39.48 | 28.8 |
| TOOL_DIE_MAKERS | 10.09 | 8043 | 1.5 | 42.5 |
| MACHINISTS | 8.81 | 6686 | 4.28 | 44.2 |
| SHEET_METAL_WORKERS | 8.4 | 6565 | 2.3 | 35.9 |
| WELDERS | 7.92 | 6477 | 5.17 | 41.8 |
| AUTO_WORKERS | 8.43 | 5811 | 13.62 | 35.9 |
| AIRCRAFT_WORKERS | 8.78 | 6573 | 5.78 | 43.7 |
| ELECTRONIC_WORKERS | 8.76 | 3942 | 74.54 | 50.8 |
| RADIO_TV_REPAIRMEN | 10.29 | 5449 | 2.92 | 37.2 |
| SEWING_MACH_OPERATORS | 6.38 | 2847 | 90.67 | 28.2 |
| AUTO_REPAIRMEN | 8.1 | 5795 | 0.81 | 38.1 |
| AIRCRAFT_REPAIRMEN | 10.1 | 7716 | 0.78 | 50.3 |
| RAILWAY_SECTIONMEN | 6.67 | 4696 | 0 | 27.3 |
| ELECTRICAL_LINEMEN | 9.05 | 8316 | 1.34 | 40.9 |
| ELECTRICIANS | 9.93 | 7147 | 0.99 | 50.2 |
| CONSTRUCTION_FOREMEN | 8.24 | 8880 | 0.65 | 51.1 |
| CARPENTERS | 6.92 | 5299 | 0.56 | 38.9 |
| MASONS | 6.6 | 5959 | 0.52 | 36.2 |
| HOUSE_PAINTERS | 7.81 | 4549 | 2.46 | 29.9 |
| PLUMBERS | 8.33 | 6928 | 0.61 | 42.9 |
| CONSTRUCTION_LABOURERS | 7.52 | 3910 | 1.09 | 26.5 |
| PILOTS | 12.27 | 14032 | 0.58 | 66.1 |
| TRAIN_ENGINEERS | 8.49 | 8845 | 0 | 48.9 |
| BUS_DRIVERS | 7.58 | 5562 | 9.47 | 35.9 |
| TAXI_DRIVERS | 7.93 | 4224 | 3.59 | 25.1 |
| LONGSHOREMEN | 8.37 | 4753 | 0 | 26.1 |
| TYPESETTERS | 10 | 6462 | 13.58 | 42.2 |
| BOOKBINDERS | 8.55 | 3617 | 70.87 | 35.2 |
MET CS 555 Assignment 5
The data in this document is from 3 groups of students (math, chemistry, and physics) on an IQ related test. Save the data to CSV/Excel file and read the data into R. Use this data to address the following questions:
- How many students are in each group? Summarize the data relating to both test score and age by the student group (separately). Use appropriate numerical and/or graphical summaries. (3 points)
- Do the test scores vary by student group? Perform a one way ANOVA using the aov or Anova function in R to assess. Summarize the results using the 5 step procedure. If the results of the overall model are significant, perform the appropriate pairwise comparisons using Tukey’s procedure to adjust for multiple comparisons and summarize these results. (7 points)
- Create an appropriate number of dummy variables for student group and re-run the one-way ANOVA using the lm function with the newly created dummy variables. Set chemistry students as the reference group. Confirm if the results are the same. What is the interpretation of the beta estimates from the regression model? (4 points)
- Re-do the one-way ANOVA adjusting for age. Focus on the output relating to the comparisons of test score by student type. Explain how this analysis differs from the analysis in step 2 above (not the results but how does this analysis differ in terms of the questions it answers as opposed to the one above). Did you obtain different results? Summarize briefly (no need to go through the 5 –step procedure here). Present the least square means and interpret these. (6 points)
| group | iq | age |
| Physics student | 34 | 15 |
| Physics student | 33 | 17 |
| Physics student | 32 | 15 |
| Physics student | 25 | 14 |
| Physics student | 36 | 19 |
| Physics student | 30 | 18 |
| Physics student | 31 | 16 |
| Physics student | 34 | 17 |
| Physics student | 29 | 16 |
| Physics student | 34 | 17 |
| Physics student | 39 | 16 |
| Physics student | 33 | 18 |
| Physics student | 39 | 19 |
| Physics student | 42 | 20 |
| Physics student | 41 | 20 |
| Math student | 36 | 20 |
| Math student | 38 | 28 |
| Math student | 37 | 22 |
| Math student | 35 | 18 |
| Math student | 41 | 19 |
| Math student | 40 | 23 |
| Math student | 36 | 19 |
| Math student | 38 | 16 |
| Math student | 24 | 18 |
| Math student | 39 | 20 |
| Math student | 29 | 19 |
| Math student | 38 | 20 |
| Math student | 45 | 23 |
| Math student | 44 | 24 |
| Math student | 44 | 22 |
| Chemistry student | 52 | 46 |
| Chemistry student | 46 | 38 |
| Chemistry student | 51 | 41 |
| Chemistry student | 52 | 39 |
| Chemistry student | 45 | 44 |
| Chemistry student | 49 | 33 |
| Chemistry student | 47 | 41 |
| Chemistry student | 46 | 36 |
| Chemistry student | 41 | 40 |
| Chemistry student | 47 | 44 |
| Chemistry student | 46 | 46 |
| Chemistry student | 42 | 38 |
| Chemistry student | 43 | 32 |
| Chemistry student | 47 | 41 |
| Chemistry student | 40 | 42 |
MET CS 555 Assignment 6
The data in this document consists of body temperature measurements and heart rate measurements for 65 men and 65 women. Save the data to excel and read the data into R. Use this data to address the following questions.
(1) We are interested in whether the proportion of men and women with body temperatures greater than or equal to 98.6 degrees Fahrenheit are equal. Therefore, we need to dichotomize the body temperature variable. Create a new variable, called “temp_level” in which temp_level = 1 if body temperature >= 98.6 and temp_level=0 if body temperature < 98.6. (1 point)
(2) Summarize the data relating to body temperature level by sex. (2 points)
(3) Calculate the risk difference. Formally test (at the α=.05 level) whether the proportion of people with higher body temperatures (greater than or equal to 98.6) is the same across men and women, based on this effect measure. Do females have higher body temperatures than males? (4.5 points)
(4) Perform a logistic regression with sex as the only explanatory variable. Formally test (at the α=.05 level) if the odds of having a temperature greater than or equal to 98.6 is the same between males and females. Include the odds ratio for sex and the associated 95% confidence interval based on the model in your summary and interpret this value. What is the c-statistic for this model? (5.5 points)
(5) Perform a multiple logistic regression predicting body temperature level from sex and heart rate. Summarize briefly the output from this model. Give the odds ratio for sex and heart rate (for a 10 beat increase). What is the c-statistic of this model? (5 points)
(6) Which model fit the data better? Support your response with evidence from your output. Present the ROC curve for the model you choose. (2 points)
Data (1=males, 2 =females)
| temp | sex | Heart rate |
| 96.3 | 1 | 70 |
| 96.7 | 1 | 71 |
| 96.9 | 1 | 74 |
| 97 | 1 | 80 |
| 97.1 | 1 | 73 |
| 97.1 | 1 | 75 |
| 97.1 | 1 | 82 |
| 97.2 | 1 | 64 |
| 97.3 | 1 | 69 |
| 97.4 | 1 | 70 |
| 97.4 | 1 | 68 |
| 97.4 | 1 | 72 |
| 97.4 | 1 | 78 |
| 97.5 | 1 | 70 |
| 97.5 | 1 | 75 |
| 97.6 | 1 | 74 |
| 97.6 | 1 | 69 |
| 97.6 | 1 | 73 |
| 97.7 | 1 | 77 |
| 97.8 | 1 | 58 |
| 97.8 | 1 | 73 |
| 97.8 | 1 | 65 |
| 97.8 | 1 | 74 |
| 97.9 | 1 | 76 |
| 97.9 | 1 | 72 |
| 98 | 1 | 78 |
| 98 | 1 | 71 |
| 98 | 1 | 74 |
| 98 | 1 | 67 |
| 98 | 1 | 64 |
| 98 | 1 | 78 |
| 98.1 | 1 | 73 |
| 98.1 | 1 | 67 |
| 98.2 | 1 | 66 |
| 98.2 | 1 | 64 |
| 98.2 | 1 | 71 |
| 98.2 | 1 | 72 |
| 98.3 | 1 | 86 |
| 98.3 | 1 | 72 |
| 98.4 | 1 | 68 |
| 98.4 | 1 | 70 |
| 98.4 | 1 | 82 |
| 98.4 | 1 | 84 |
| 98.5 | 1 | 68 |
| 98.5 | 1 | 71 |
| 98.6 | 2 | 77 |
| 98.6 | 1 | 78 |
| 98.6 | 1 | 83 |
| 98.6 | 2 | 66 |
| 98.6 | 1 | 70 |
| 98.6 | 1 | 82 |
| 98.7 | 2 | 73 |
| 98.7 | 1 | 78 |
| 98.8 | 1 | 78 |
| 98.8 | 1 | 81 |
| 98.8 | 2 | 78 |
| 98.9 | 1 | 80 |
| 99 | 2 | 75 |
| 99 | 2 | 79 |
| 99 | 1 | 81 |
| 99.1 | 1 | 71 |
| 99.2 | 1 | 83 |
| 99.3 | 1 | 63 |
| 99.4 | 1 | 70 |
| 99.5 | 1 | 75 |
| 96.4 | 2 | 69 |
| 96.7 | 2 | 62 |
| 96.8 | 1 | 75 |
| 97.2 | 1 | 66 |
| 97.2 | 2 | 68 |
| 97.4 | 2 | 57 |
| 97.6 | 1 | 61 |
| 97.7 | 2 | 84 |
| 97.7 | 1 | 61 |
| 97.8 | 2 | 77 |
| 97.8 | 2 | 62 |
| 97.8 | 2 | 71 |
| 97.9 | 1 | 68 |
| 97.9 | 2 | 69 |
| 97.9 | 2 | 79 |
| 98 | 2 | 76 |
| 98 | 1 | 87 |
| 98 | 2 | 78 |
| 98 | 2 | 73 |
| 98 | 2 | 89 |
| 98.1 | 2 | 81 |
| 98.2 | 2 | 73 |
| 98.2 | 2 | 64 |
| 98.2 | 2 | 65 |
| 98.2 | 2 | 73 |
| 98.2 | 2 | 69 |
| 98.2 | 2 | 57 |
| 98.3 | 2 | 79 |
| 98.3 | 2 | 78 |
| 98.3 | 2 | 80 |
| 98.4 | 2 | 79 |
| 98.4 | 2 | 81 |
| 98.4 | 2 | 73 |
| 98.4 | 2 | 74 |
| 98.4 | 2 | 84 |
| 98.5 | 2 | 83 |
| 98.6 | 2 | 82 |
| 98.6 | 2 | 85 |
| 98.6 | 2 | 86 |
| 98.6 | 2 | 77 |
| 98.7 | 2 | 72 |
| 98.7 | 2 | 79 |
| 98.7 | 2 | 59 |
| 98.7 | 2 | 64 |
| 98.7 | 2 | 65 |
| 98.7 | 2 | 82 |
| 98.8 | 2 | 64 |
| 98.8 | 2 | 70 |
| 98.8 | 2 | 83 |
| 98.8 | 2 | 89 |
| 98.8 | 2 | 69 |
| 98.8 | 2 | 73 |
| 98.8 | 2 | 84 |
| 98.9 | 2 | 76 |
| 99 | 2 | 79 |
| 99 | 2 | 81 |
| 99.1 | 2 | 80 |
| 99.1 | 2 | 74 |
| 99.2 | 2 | 77 |
| 99.2 | 2 | 66 |
| 99.3 | 2 | 68 |
| 99.4 | 2 | 77 |
| 99.9 | 2 | 79 |
| 100 | 2 | 78 |
| 100.8 | 2 | 77 |