Add main content and assignment for prob and stats
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@ -55,7 +55,7 @@ Graphically we can represent relationship between median and quartiles in a diag
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Here we also computer **inter-quartile range** IQR=Q3-Q1, and so-called **outliers** - values, that lie outside the boundaries [Q1-1.5*IQR,Q3+1.5*IQR].
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For finite distribution that contains small number of possible values, a good "typical" value is the one that appears the most frequently, which is called **mode**. It is often applied to categorical data, such as colors. Consider a situation when we have two groups of people - some that strongly prefer red, and others who prefer blue. If we code colors by numbers, the mean value for a favourite color would be somewhere in the orange-green spectrum, which does not indicate the actual preference on neither group. However, the mode would be either one of the colors, or both colors, if the number of people voting for them is equal (in this case we call the sample **multimodal**).
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For finite distribution that contains small number of possible values, a good "typical" value is the one that appears the most frequently, which is called **mode**. It is often applied to categorical data, such as colors. Consider a situation when we have two groups of people - some that strongly prefer red, and others who prefer blue. If we code colors by numbers, the mean value for a favorite color would be somewhere in the orange-green spectrum, which does not indicate the actual preference on neither group. However, the mode would be either one of the colors, or both colors, if the number of people voting for them is equal (in this case we call the sample **multimodal**).
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## Real-world Data
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When we analyze data from real life, they often are not random variables as such, in a sense that we do not perform experiments with unknown result. For example, consider a team of baseball players, and their body data, such as height, weight and age. Those numbers are not exactly random, but we can still apply the same mathematical concepts. For example, a sequence of people's weights can be considered to be a sequence of values drawn from some random variable. Below is the sequence of weights of actual baseball players from [Major League Baseball](http://mlb.mlb.com/index.jsp), taken from [this dataset](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) (for your convenience, only first 20 values are shown):
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@ -64,6 +64,8 @@ When we analyze data from real life, they often are not random variables as such
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[180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]
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```
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> **Note**: To see the example of working with this dataset, have a look at the [accompanying notebook](notebook.ipynb). There is also a number of challenges throughout this lesson, and you may complete them by adding some code to that notebook. If you are not sure how to operate on data, do not worry - we will come back to working with data using Python at a later time.
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Here is the box plot showing mean, median and quartiles for our data:
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![Weight Box Plot](images/weight-boxplot.png)
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@ -94,13 +96,23 @@ If we plot the histogram of the generated samples we will see the picture very s
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## Confidence Intervals
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When we talk about weights of baseball players, we assume that there is certain **random variable W** that corresponds to ideal probability distribution of weights of all baseball players. Our sequence of weights corresponds to a subset of all baseball players that we call **population**. An interesting question is, can we know the parameters of distribution of W, i.e. mean and variance?
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When we talk about weights of baseball players, we assume that there is certain **random variable W** that corresponds to ideal probability distribution of weights of all baseball players (so-called **population**). Our sequence of weights corresponds to a subset of all baseball players that we call **sample**. An interesting question is, can we know the parameters of distribution of W, i.e. mean and variance of the population?
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The easiest answer would be to calculate mean and variance of our sample. However, it could happen that our random sample does not accurately represent complete population. Thus it makes sense to talk about **confidence interval**.
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The easiest answer would be to calculate mean and variance of our sample. However, it could happen that our random sample does not accurately represent complete population. Thus it makes sense to talk about **confidence interval**.
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> **Confidence interval** is the estimation of true mean of the population given our sample, which is accurate is a certain probability (or **level of confidence**).
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Suppose we have a sample X<sub>1</sub>, ..., X<sub>n</sub> from our distribution. Each time we draw a sample from our distribution, we would end up with different mean value μ. Thus μ can be considered to be a random variable. A **confidence interval** with confidence p is a pair of values (L<sub>p</sub>,R<sub>p</sub>), such that **P**(L<sub>p</sub>≤μ≤R<sub>p</sub>) = p, i.e. a probability of measured mean value falling within the interval equals to p.
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It does beyond our short intro to discuss how those confidence intervals are calculated. Some more details can be found [on Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval). An example of calculating confidence interval for weights and heights is given in the [accompanying notebooks](notebook.ipynb).
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It does beyond our short intro to discuss in detail how those confidence intervals are calculated. Some more details can be found [on Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval). In short, we define the distribution of computed sample mean relative to the true mean of the population, which is called **student distribution**.
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> **Interesting fact**: Student distribution is named after mathematician William Sealy Gosset, who published his paper under pseudonym "Student". He worked in the Guinness brewery, and, according to one of the versions, his employer did not want general public to know that they were using statistical tests to determine the quality of raw materials.
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If we want to estimate the mean μ of our population with confidence p, we need to take *(1-p)/2-th percentile* of a Student distribution A, which can either be taken from tables, or computer using some built-in functions of statistical software (eg. Python, R, etc.). Then the interval for μ would be given by X±A*D/√n, where X is the obtained mean of the sample, D is the standard deviation.
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> **Note**: We also omit the discussion of an important concept of [degrees of freedom](https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)), which is important in relation to Student distribution. You can refer to more complete books on statistics to understand this concept deeper.
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An example of calculating confidence interval for weights and heights is given in the [accompanying notebooks](notebook.ipynb).
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| p | Weight mean |
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|-----|-----------|
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@ -163,11 +175,10 @@ In our case, p-value is very low, meaning that there is strong evidence supporti
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> **Challenge**: Use the sample code in the notebook to test other hypothesis that: (1) First basemen and older that second basemen; (2) First basemen and taller than third basemen; (3) Shortstops are taller than second basemen
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There are different types of hypothesis that we might want to test, for example:
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There are also different other types of hypothesis that we might want to test, for example:
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* To prove that a given sample follows some distribution. In our case we have assumed that heights are normally distributed, but that needs formal statistical verification.
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* To prove that a mean value of a sample corresponds to some predefined value
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* To prove that
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* To compare means of a number of samples (eg. what is the difference in happiness levels amond different age groups)
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## Law of Large Numbers and Central Limit Theorem
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> More examples of correlation and covariance can be found in [accompanying notebook](notebook.ipynb).
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## 🚀 Challenge
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@ -217,7 +225,12 @@ In our case, the value 0.53 indicates that there is some correlation between wei
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## Review & Self Study
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Probability and statistics is such a broad topic that it deserves its own course. If you are interested to go deeper into theory, you may want to continue reading some of the following books:
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1. [Carlos Fernanderz-Granda](https://cims.nyu.edu/~cfgranda/) from New York University has great lecture notes [Probability and Statistics for Data Science](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf) (available online)
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1. [Peter and Andrew Bruce. Practical Statistics for Data Scientists.](https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/) [[sample code in R](https://github.com/andrewgbruce/statistics-for-data-scientists)].
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1. [James D. Miller. Statistics for Data Science](https://www.packtpub.com/product/statistics-for-data-science/9781788290678) [[sample code in R](https://github.com/PacktPublishing/Statistics-for-Data-Science)]
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## Assignment
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[Assignment Title](assignment.md)
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[Small Diabetes Study](assignment.md)
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@ -0,0 +1,252 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"## Introduction to Probability and Statistics\r\n",
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"## Assignment\r\n",
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"\r\n",
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"In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"source": [
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"import pandas as pd\r\n",
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"import numpy as np\r\n",
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"\r\n",
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"df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
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"df.head()"
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],
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n",
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"0 59 2 32.1 101.0 157 93.2 38.0 4.0 4.8598 87 151\n",
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"1 48 1 21.6 87.0 183 103.2 70.0 3.0 3.8918 69 75\n",
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"2 72 2 30.5 93.0 156 93.6 41.0 4.0 4.6728 85 141\n",
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"3 24 1 25.3 84.0 198 131.4 40.0 5.0 4.8903 89 206\n",
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"4 50 1 23.0 101.0 192 125.4 52.0 4.0 4.2905 80 135"
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],
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>AGE</th>\n",
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" <th>SEX</th>\n",
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" <th>BMI</th>\n",
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" <th>BP</th>\n",
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" <th>S1</th>\n",
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" <th>S2</th>\n",
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" <th>S3</th>\n",
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" <th>S4</th>\n",
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" <th>S5</th>\n",
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" <th>S6</th>\n",
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" <th>Y</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>59</td>\n",
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" <td>32.1</td>\n",
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" <td>101.0</td>\n",
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" <td>157</td>\n",
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" <td>93.2</td>\n",
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" <td>38.0</td>\n",
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" <td>4.0</td>\n",
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" <td>4.8598</td>\n",
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" <td>87</td>\n",
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" <td>151</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>48</td>\n",
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" <td>1</td>\n",
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" <td>21.6</td>\n",
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" <td>87.0</td>\n",
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" <td>183</td>\n",
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" <td>103.2</td>\n",
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" <td>70.0</td>\n",
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" <td>3.0</td>\n",
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" <td>3.8918</td>\n",
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" <td>69</td>\n",
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" <td>75</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>72</td>\n",
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" <td>2</td>\n",
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" <td>30.5</td>\n",
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" <td>93.0</td>\n",
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" <td>156</td>\n",
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" <td>93.6</td>\n",
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" <td>41.0</td>\n",
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" <td>4.0</td>\n",
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" <td>4.6728</td>\n",
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" <td>85</td>\n",
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" <td>141</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>24</td>\n",
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" <td>1</td>\n",
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" <td>25.3</td>\n",
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" <td>84.0</td>\n",
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" <td>198</td>\n",
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" <td>131.4</td>\n",
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" <td>40.0</td>\n",
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" <td>5.0</td>\n",
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" <td>4.8903</td>\n",
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" <td>89</td>\n",
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" <td>206</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>50</td>\n",
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" <td>1</td>\n",
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" <td>23.0</td>\n",
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" <td>101.0</td>\n",
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" <td>192</td>\n",
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" <td>125.4</td>\n",
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" <td>52.0</td>\n",
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" <td>4.0</td>\n",
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" <td>4.2905</td>\n",
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" <td>80</td>\n",
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" <td>135</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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]
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},
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"metadata": {},
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"execution_count": 13
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}
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],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"\r\n",
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"In this dataset, columns as the following:\r\n",
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"* Age and sex are self-explanatory\r\n",
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"* BMI is body mass index\r\n",
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"* BP is average blood pressure\r\n",
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"* S1 through S6 are different blood measurements\r\n",
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"* Y is the qualitative measure of disease progression over one year\r\n",
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"\r\n",
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"Let's study this dataset using methods of probability and statistics.\r\n",
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"\r\n",
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"### Task 1: Compute mean values and variance for all values"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
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"\r\n",
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"> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
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],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
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],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {}
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}
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],
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"metadata": {
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"orig_nbformat": 4,
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"language_info": {
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"name": "python",
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"version": "3.8.8",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"pygments_lexer": "ipython3",
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.8.8 64-bit (conda)"
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},
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"interpreter": {
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"hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
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}
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"nbformat": 4,
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"nbformat_minor": 2
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}
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# Title
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# Small Diabetes Study
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In this assignment, we will work with a small dataset of diabetes patients taken from [here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).
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| | AGE | SEX | BMI | BP | S1 | S2 | S3 | S4 | S5 | S6 | Y |
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|---|-----|-----|-----|----|----|----|----|----|----|----|----|
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| 0 | 59 | 2 | 32.1 | 101. | 157 | 93.2 | 38.0 | 4. | 4.8598 | 87 | 151 |
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| 1 | 48 | 1 | 21.6 | 87.0 | 183 | 103.2 | 70. | 3. | 3.8918 | 69 | 75 |
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| 2 | 72 | 2 | 30.5 | 93.0 | 156 | 93.6 | 41.0 | 4.0 | 4. | 85 | 141 |
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| ... | ... | ... | ... | ...| ...| ...| ...| ...| ...| ...| ... |
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## Instructions
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* Open the [assignment notebook](assignment.ipynb) in a jupyter notebook environment
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* Complete all tasks listed in the notebook, namely:
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[ ] Compute mean values and variance for all values
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[ ] Plot boxplots for BMI, BP and Y depending on gender
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[ ] What is the the distribution of Age, Sex, BMI and Y variables?
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[ ] Test the correlation between different variables and disease progression (Y)
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[ ] Test the hypothesis that the degree of diabetes progression is different between men and women
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## Rubric
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Exemplary | Adequate | Needs Improvement
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--- | --- | -- |
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All required tasks are complete, graphically illustrated and explained | Most of the tasks are complete, explanations or takeaways from graphs and/or obtained values are missing | Only basic tasks such as computation of mean/variance and basic plots are complete, no conclusions are made from the data
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@ -0,0 +1,443 @@
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AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y
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59 2 32.1 101 157 93.2 38 4 4.8598 87 151
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48 1 21.6 87 183 103.2 70 3 3.8918 69 75
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72 2 30.5 93 156 93.6 41 4 4.6728 85 141
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24 1 25.3 84 198 131.4 40 5 4.8903 89 206
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50 1 23 101 192 125.4 52 4 4.2905 80 135
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23 1 22.6 89 139 64.8 61 2 4.1897 68 97
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36 2 22 90 160 99.6 50 3 3.9512 82 138
|
||||
66 2 26.2 114 255 185 56 4.55 4.2485 92 63
|
||||
60 2 32.1 83 179 119.4 42 4 4.4773 94 110
|
||||
29 1 30 85 180 93.4 43 4 5.3845 88 310
|
||||
22 1 18.6 97 114 57.6 46 2 3.9512 83 101
|
||||
56 2 28 85 184 144.8 32 6 3.5835 77 69
|
||||
53 1 23.7 92 186 109.2 62 3 4.3041 81 179
|
||||
50 2 26.2 97 186 105.4 49 4 5.0626 88 185
|
||||
61 1 24 91 202 115.4 72 3 4.2905 73 118
|
||||
34 2 24.7 118 254 184.2 39 7 5.037 81 171
|
||||
47 1 30.3 109 207 100.2 70 3 5.2149 98 166
|
||||
68 2 27.5 111 214 147 39 5 4.9416 91 144
|
||||
38 1 25.4 84 162 103 42 4 4.4427 87 97
|
||||
41 1 24.7 83 187 108.2 60 3 4.5433 78 168
|
||||
35 1 21.1 82 156 87.8 50 3 4.5109 95 68
|
||||
25 2 24.3 95 162 98.6 54 3 3.8501 87 49
|
||||
25 1 26 92 187 120.4 56 3 3.9703 88 68
|
||||
61 2 32 103.67 210 85.2 35 6 6.107 124 245
|
||||
31 1 29.7 88 167 103.4 48 4 4.3567 78 184
|
||||
30 2 25.2 83 178 118.4 34 5 4.852 83 202
|
||||
19 1 19.2 87 124 54 57 2 4.1744 90 137
|
||||
42 1 31.9 83 158 87.6 53 3 4.4659 101 85
|
||||
63 1 24.4 73 160 91.4 48 3 4.6347 78 131
|
||||
67 2 25.8 113 158 54.2 64 2 5.2933 104 283
|
||||
32 1 30.5 89 182 110.6 56 3 4.3438 89 129
|
||||
42 1 20.3 71 161 81.2 66 2 4.2341 81 59
|
||||
58 2 38 103 150 107.2 22 7 4.6444 98 341
|
||||
57 1 21.7 94 157 58 82 2 4.4427 92 87
|
||||
53 1 20.5 78 147 84.2 52 3 3.989 75 65
|
||||
62 2 23.5 80.33 225 112.8 86 2.62 4.8752 96 102
|
||||
52 1 28.5 110 195 97.2 60 3 5.2417 85 265
|
||||
46 1 27.4 78 171 88 58 3 4.8283 90 276
|
||||
48 2 33 123 253 163.6 44 6 5.425 97 252
|
||||
48 2 27.7 73 191 119.4 46 4 4.852 92 90
|
||||
50 2 25.6 101 229 162.2 43 5 4.7791 114 100
|
||||
21 1 20.1 63 135 69 54 3 4.0943 89 55
|
||||
32 2 25.4 90.33 153 100.4 34 4.5 4.5326 83 61
|
||||
54 1 24.2 74 204 109 82 2 4.1744 109 92
|
||||
61 2 32.7 97 177 118.4 29 6 4.9972 87 259
|
||||
56 2 23.1 104 181 116.4 47 4 4.4773 79 53
|
||||
33 1 25.3 85 155 85 51 3 4.5539 70 190
|
||||
27 1 19.6 78 128 68 43 3 4.4427 71 142
|
||||
67 2 22.5 98 191 119.2 61 3 3.989 86 75
|
||||
37 2 27.7 93 180 119.4 30 6 5.0304 88 142
|
||||
58 1 25.7 99 157 91.6 49 3 4.4067 93 155
|
||||
65 2 27.9 103 159 96.8 42 4 4.6151 86 225
|
||||
34 1 25.5 93 218 144 57 4 4.4427 88 59
|
||||
46 1 24.9 115 198 129.6 54 4 4.2767 103 104
|
||||
35 1 28.7 97 204 126.8 64 3 4.1897 93 182
|
||||
37 1 21.8 84 184 101 73 3 3.912 93 128
|
||||
37 1 30.2 87 166 96 40 4.15 5.0106 87 52
|
||||
41 1 20.5 80 124 48.8 64 2 4.0254 75 37
|
||||
60 1 20.4 105 198 78.4 99 2 4.6347 79 170
|
||||
66 2 24 98 236 146.4 58 4 5.0626 96 170
|
||||
29 1 26 83 141 65.2 64 2 4.0775 83 61
|
||||
37 2 26.8 79 157 98 28 6 5.0434 96 144
|
||||
41 2 25.7 83 181 106.6 66 3 3.7377 85 52
|
||||
39 1 22.9 77 204 143.2 46 4 4.3041 74 128
|
||||
67 2 24 83 143 77.2 49 3 4.4308 94 71
|
||||
36 2 24.1 112 193 125 35 6 5.1059 95 163
|
||||
46 2 24.7 85 174 123.2 30 6 4.6444 96 150
|
||||
60 2 25 89.67 185 120.8 46 4.02 4.5109 92 97
|
||||
59 2 23.6 83 165 100 47 4 4.4998 92 160
|
||||
53 1 22.1 93 134 76.2 46 3 4.0775 96 178
|
||||
48 1 19.9 91 189 109.6 69 3 3.9512 101 48
|
||||
48 1 29.5 131 207 132.2 47 4 4.9345 106 270
|
||||
66 2 26 91 264 146.6 65 4 5.5683 87 202
|
||||
52 2 24.5 94 217 149.4 48 5 4.585 89 111
|
||||
52 2 26.6 111 209 126.4 61 3 4.6821 109 85
|
||||
46 2 23.5 87 181 114.8 44 4 4.7095 98 42
|
||||
40 2 29 115 97 47.2 35 2.77 4.3041 95 170
|
||||
22 1 23 73 161 97.8 54 3 3.8286 91 200
|
||||
50 1 21 88 140 71.8 35 4 5.112 71 252
|
||||
20 1 22.9 87 191 128.2 53 4 3.8918 85 113
|
||||
68 1 27.5 107 241 149.6 64 4 4.92 90 143
|
||||
52 2 24.3 86 197 133.6 44 5 4.5747 91 51
|
||||
44 1 23.1 87 213 126.4 77 3 3.8712 72 52
|
||||
38 1 27.3 81 146 81.6 47 3 4.4659 81 210
|
||||
49 1 22.7 65.33 168 96.2 62 2.71 3.8918 60 65
|
||||
61 1 33 95 182 114.8 54 3 4.1897 74 141
|
||||
29 2 19.4 83 152 105.8 39 4 3.5835 83 55
|
||||
61 1 25.8 98 235 125.8 76 3 5.112 82 134
|
||||
34 2 22.6 75 166 91.8 60 3 4.2627 108 42
|
||||
36 1 21.9 89 189 105.2 68 3 4.3694 96 111
|
||||
52 1 24 83 167 86.6 71 2 3.8501 94 98
|
||||
61 1 31.2 79 235 156.8 47 5 5.0499 96 164
|
||||
43 1 26.8 123 193 102.2 67 3 4.7791 94 48
|
||||
35 1 20.4 65 187 105.6 67 2.79 4.2767 78 96
|
||||
27 1 24.8 91 189 106.8 69 3 4.1897 69 90
|
||||
29 1 21 71 156 97 38 4 4.654 90 162
|
||||
64 2 27.3 109 186 107.6 38 5 5.3083 99 150
|
||||
41 1 34.6 87.33 205 142.6 41 5 4.6728 110 279
|
||||
49 2 25.9 91 178 106.6 52 3 4.5747 75 92
|
||||
48 1 20.4 98 209 139.4 46 5 4.7707 78 83
|
||||
53 1 28 88 233 143.8 58 4 5.0499 91 128
|
||||
53 2 22.2 113 197 115.2 67 3 4.3041 100 102
|
||||
23 1 29 90 216 131.4 65 3 4.585 91 302
|
||||
65 2 30.2 98 219 160.6 40 5 4.5218 84 198
|
||||
41 1 32.4 94 171 104.4 56 3 3.9703 76 95
|
||||
55 2 23.4 83 166 101.6 46 4 4.5218 96 53
|
||||
22 1 19.3 82 156 93.2 52 3 3.989 71 134
|
||||
56 1 31 78.67 187 141.4 34 5.5 4.0604 90 144
|
||||
54 2 30.6 103.33 144 79.8 30 4.8 5.1417 101 232
|
||||
59 2 25.5 95.33 190 139.4 35 5.43 4.3567 117 81
|
||||
60 2 23.4 88 153 89.8 58 3 3.2581 95 104
|
||||
54 1 26.8 87 206 122 68 3 4.382 80 59
|
||||
25 1 28.3 87 193 128 49 4 4.382 92 246
|
||||
54 2 27.7 113 200 128.4 37 5 5.1533 113 297
|
||||
55 1 36.6 113 199 94.4 43 4.63 5.7301 97 258
|
||||
40 2 26.5 93 236 147 37 7 5.5607 92 229
|
||||
62 2 31.8 115 199 128.6 44 5 4.8828 98 275
|
||||
65 1 24.4 120 222 135.6 37 6 5.5094 124 281
|
||||
33 2 25.4 102 206 141 39 5 4.8675 105 179
|
||||
53 1 22 94 175 88 59 3 4.9416 98 200
|
||||
35 1 26.8 98 162 103.6 45 4 4.2047 86 200
|
||||
66 1 28 101 195 129.2 40 5 4.8598 94 173
|
||||
62 2 33.9 101 221 156.4 35 6 4.9972 103 180
|
||||
50 2 29.6 94.33 300 242.4 33 9.09 4.8122 109 84
|
||||
47 1 28.6 97 164 90.6 56 3 4.4659 88 121
|
||||
47 2 25.6 94 165 74.8 40 4 5.5255 93 161
|
||||
24 1 20.7 87 149 80.6 61 2 3.6109 78 99
|
||||
58 2 26.2 91 217 124.2 71 3 4.6913 68 109
|
||||
34 1 20.6 87 185 112.2 58 3 4.3041 74 115
|
||||
51 1 27.9 96 196 122.2 42 5 5.0689 120 268
|
||||
31 2 35.3 125 187 112.4 48 4 4.8903 109 274
|
||||
22 1 19.9 75 175 108.6 54 3 4.1271 72 158
|
||||
53 2 24.4 92 214 146 50 4 4.4998 97 107
|
||||
37 2 21.4 83 128 69.6 49 3 3.8501 84 83
|
||||
28 1 30.4 85 198 115.6 67 3 4.3438 80 103
|
||||
47 1 31.6 84 154 88 30 5.1 5.1985 105 272
|
||||
23 1 18.8 78 145 72 63 2 3.912 86 85
|
||||
50 1 31 123 178 105 48 4 4.8283 88 280
|
||||
58 2 36.7 117 166 93.8 44 4 4.9488 109 336
|
||||
55 1 32.1 110 164 84.2 42 4 5.2417 90 281
|
||||
60 2 27.7 107 167 114.6 38 4 4.2767 95 118
|
||||
41 1 30.8 81 214 152 28 7.6 5.1358 123 317
|
||||
60 2 27.5 106 229 143.8 51 4 5.1417 91 235
|
||||
40 1 26.9 92 203 119.8 70 3 4.1897 81 60
|
||||
57 2 30.7 90 204 147.8 34 6 4.7095 93 174
|
||||
37 1 38.3 113 165 94.6 53 3 4.4659 79 259
|
||||
40 2 31.9 95 198 135.6 38 5 4.804 93 178
|
||||
33 1 35 89 200 130.4 42 4.76 4.9273 101 128
|
||||
32 2 27.8 89 216 146.2 55 4 4.3041 91 96
|
||||
35 2 25.9 81 174 102.4 31 6 5.3132 82 126
|
||||
55 1 32.9 102 164 106.2 41 4 4.4308 89 288
|
||||
49 1 26 93 183 100.2 64 3 4.5433 88 88
|
||||
39 2 26.3 115 218 158.2 32 7 4.9345 109 292
|
||||
60 2 22.3 113 186 125.8 46 4 4.2627 94 71
|
||||
67 2 28.3 93 204 132.2 49 4 4.7362 92 197
|
||||
41 2 32 109 251 170.6 49 5 5.0562 103 186
|
||||
44 1 25.4 95 162 92.6 53 3 4.4067 83 25
|
||||
48 2 23.3 89.33 212 142.8 46 4.61 4.7536 98 84
|
||||
45 1 20.3 74.33 190 126.2 49 3.88 4.3041 79 96
|
||||
47 1 30.4 120 199 120 46 4 5.1059 87 195
|
||||
46 1 20.6 73 172 107 51 3 4.2485 80 53
|
||||
36 2 32.3 115 286 199.4 39 7 5.4723 112 217
|
||||
34 1 29.2 73 172 108.2 49 4 4.3041 91 172
|
||||
53 2 33.1 117 183 119 48 4 4.382 106 131
|
||||
61 1 24.6 101 209 106.8 77 3 4.8363 88 214
|
||||
37 1 20.2 81 162 87.8 63 3 4.0254 88 59
|
||||
33 2 20.8 84 125 70.2 46 3 3.7842 66 70
|
||||
68 1 32.8 105.67 205 116.4 40 5.13 5.4931 117 220
|
||||
49 2 31.9 94 234 155.8 34 7 5.3982 122 268
|
||||
48 1 23.9 109 232 105.2 37 6 6.107 96 152
|
||||
55 2 24.5 84 179 105.8 66 3 3.5835 87 47
|
||||
43 1 22.1 66 134 77.2 45 3 4.0775 80 74
|
||||
60 2 33 97 217 125.6 45 5 5.4467 112 295
|
||||
31 2 19 93 137 73 47 3 4.4427 78 101
|
||||
53 2 27.3 82 119 55 39 3 4.8283 93 151
|
||||
67 1 22.8 87 166 98.6 52 3 4.3438 92 127
|
||||
61 2 28.2 106 204 132 52 4 4.6052 96 237
|
||||
62 1 28.9 87.33 206 127.2 33 6.24 5.4337 99 225
|
||||
60 1 25.6 87 207 125.8 69 3 4.1109 84 81
|
||||
42 1 24.9 91 204 141.8 38 5 4.7958 89 151
|
||||
38 2 26.8 105 181 119.2 37 5 4.8203 91 107
|
||||
62 1 22.4 79 222 147.4 59 4 4.3567 76 64
|
||||
61 2 26.9 111 236 172.4 39 6 4.8122 89 138
|
||||
61 2 23.1 113 186 114.4 47 4 4.8122 105 185
|
||||
53 1 28.6 88 171 98.8 41 4 5.0499 99 265
|
||||
28 2 24.7 97 175 99.6 32 5 5.3799 87 101
|
||||
26 2 30.3 89 218 152.2 31 7 5.1591 82 137
|
||||
30 1 21.3 87 134 63 63 2 3.6889 66 143
|
||||
50 1 26.1 109 243 160.6 62 4 4.625 89 141
|
||||
48 1 20.2 95 187 117.4 53 4 4.4188 85 79
|
||||
51 1 25.2 103 176 112.2 37 5 4.8978 90 292
|
||||
47 2 22.5 82 131 66.8 41 3 4.7536 89 178
|
||||
64 2 23.5 97 203 129 59 3 4.3175 77 91
|
||||
51 2 25.9 76 240 169 39 6 5.0752 96 116
|
||||
30 1 20.9 104 152 83.8 47 3 4.6634 97 86
|
||||
56 2 28.7 99 208 146.4 39 5 4.7274 97 122
|
||||
42 1 22.1 85 213 138.6 60 4 4.2767 94 72
|
||||
62 2 26.7 115 183 124 35 5 4.7875 100 129
|
||||
34 1 31.4 87 149 93.8 46 3 3.8286 77 142
|
||||
60 1 22.2 104.67 221 105.4 60 3.68 5.6276 93 90
|
||||
64 1 21 92.33 227 146.8 65 3.49 4.3307 102 158
|
||||
39 2 21.2 90 182 110.4 60 3 4.0604 98 39
|
||||
71 2 26.5 105 281 173.6 55 5 5.5683 84 196
|
||||
48 2 29.2 110 218 151.6 39 6 4.92 98 222
|
||||
79 2 27 103 169 110.8 37 5 4.6634 110 277
|
||||
40 1 30.7 99 177 85.4 50 4 5.3375 85 99
|
||||
49 2 28.8 92 207 140 44 5 4.7449 92 196
|
||||
51 1 30.6 103 198 106.6 57 3 5.1475 100 202
|
||||
57 1 30.1 117 202 139.6 42 5 4.625 120 155
|
||||
59 2 24.7 114 152 104.8 29 5 4.5109 88 77
|
||||
51 1 27.7 99 229 145.6 69 3 4.2767 77 191
|
||||
74 1 29.8 101 171 104.8 50 3 4.3944 86 70
|
||||
67 1 26.7 105 225 135.4 69 3 4.6347 96 73
|
||||
49 1 19.8 88 188 114.8 57 3 4.3944 93 49
|
||||
57 1 23.3 88 155 63.6 78 2 4.2047 78 65
|
||||
56 2 35.1 123 164 95 38 4 5.0434 117 263
|
||||
52 2 29.7 109 228 162.8 31 8 5.1417 103 248
|
||||
69 1 29.3 124 223 139 54 4 5.0106 102 296
|
||||
37 1 20.3 83 185 124.6 38 5 4.7185 88 214
|
||||
24 1 22.5 89 141 68 52 3 4.654 84 185
|
||||
55 2 22.7 93 154 94.2 53 3 3.5264 75 78
|
||||
36 1 22.8 87 178 116 41 4 4.654 82 93
|
||||
42 2 24 107 150 85 44 3 4.654 96 252
|
||||
21 1 24.2 76 147 77 53 3 4.4427 79 150
|
||||
41 1 20.2 62 153 89 50 3 4.2485 89 77
|
||||
57 2 29.4 109 160 87.6 31 5 5.3327 92 208
|
||||
20 2 22.1 87 171 99.6 58 3 4.2047 78 77
|
||||
67 2 23.6 111.33 189 105.4 70 2.7 4.2195 93 108
|
||||
34 1 25.2 77 189 120.6 53 4 4.3438 79 160
|
||||
41 2 24.9 86 192 115 61 3 4.382 94 53
|
||||
38 2 33 78 301 215 50 6.02 5.193 108 220
|
||||
51 1 23.5 101 195 121 51 4 4.7449 94 154
|
||||
52 2 26.4 91.33 218 152 39 5.59 4.9053 99 259
|
||||
67 1 29.8 80 172 93.4 63 3 4.3567 82 90
|
||||
61 1 30 108 194 100 52 3.73 5.3471 105 246
|
||||
67 2 25 111.67 146 93.4 33 4.42 4.585 103 124
|
||||
56 1 27 105 247 160.6 54 5 5.0876 94 67
|
||||
64 1 20 74.67 189 114.8 62 3.05 4.1109 91 72
|
||||
58 2 25.5 112 163 110.6 29 6 4.7622 86 257
|
||||
55 1 28.2 91 250 140.2 67 4 5.366 103 262
|
||||
62 2 33.3 114 182 114 38 5 5.0106 96 275
|
||||
57 2 25.6 96 200 133 52 3.85 4.3175 105 177
|
||||
20 2 24.2 88 126 72.2 45 3 3.7842 74 71
|
||||
53 2 22.1 98 165 105.2 47 4 4.1589 81 47
|
||||
32 2 31.4 89 153 84.2 56 3 4.1589 90 187
|
||||
41 1 23.1 86 148 78 58 3 4.0943 60 125
|
||||
60 1 23.4 76.67 247 148 65 3.8 5.1358 77 78
|
||||
26 1 18.8 83 191 103.6 69 3 4.5218 69 51
|
||||
37 1 30.8 112 282 197.2 43 7 5.3423 101 258
|
||||
45 1 32 110 224 134.2 45 5 5.4116 93 215
|
||||
67 1 31.6 116 179 90.4 41 4 5.4723 100 303
|
||||
34 2 35.5 120 233 146.6 34 7 5.5683 101 243
|
||||
50 1 31.9 78.33 207 149.2 38 5.45 4.5951 84 91
|
||||
71 1 29.5 97 227 151.6 45 5 5.0239 108 150
|
||||
57 2 31.6 117 225 107.6 40 6 5.9584 113 310
|
||||
49 1 20.3 93 184 103 61 3 4.6052 93 153
|
||||
35 1 41.3 81 168 102.8 37 5 4.9488 94 346
|
||||
41 2 21.2 102 184 100.4 64 3 4.585 79 63
|
||||
70 2 24.1 82.33 194 149.2 31 6.26 4.2341 105 89
|
||||
52 1 23 107 179 123.7 42.5 4.21 4.1589 93 50
|
||||
60 1 25.6 78 195 95.4 91 2 3.7612 87 39
|
||||
62 1 22.5 125 215 99 98 2 4.4998 95 103
|
||||
44 2 38.2 123 201 126.6 44 5 5.0239 92 308
|
||||
28 2 19.2 81 155 94.6 51 3 3.8501 87 116
|
||||
58 2 29 85 156 109.2 36 4 3.989 86 145
|
||||
39 2 24 89.67 190 113.6 52 3.65 4.804 101 74
|
||||
34 2 20.6 98 183 92 83 2 3.6889 92 45
|
||||
65 1 26.3 70 244 166.2 51 5 4.8978 98 115
|
||||
66 2 34.6 115 204 139.4 36 6 4.9628 109 264
|
||||
51 1 23.4 87 220 108.8 93 2 4.5109 82 87
|
||||
50 2 29.2 119 162 85.2 54 3 4.7362 95 202
|
||||
59 2 27.2 107 158 102 39 4 4.4427 93 127
|
||||
52 1 27 78.33 134 73 44 3.05 4.4427 69 182
|
||||
69 2 24.5 108 243 136.4 40 6 5.8081 100 241
|
||||
53 1 24.1 105 184 113.4 46 4 4.8122 95 66
|
||||
47 2 25.3 98 173 105.6 44 4 4.7622 108 94
|
||||
52 1 28.8 113 280 174 67 4 5.273 86 283
|
||||
39 1 20.9 95 150 65.6 68 2 4.4067 95 64
|
||||
67 2 23 70 184 128 35 5 4.654 99 102
|
||||
59 2 24.1 96 170 98.6 54 3 4.4659 85 200
|
||||
51 2 28.1 106 202 122.2 55 4 4.8203 87 265
|
||||
23 2 18 78 171 96 48 4 4.9053 92 94
|
||||
68 1 25.9 93 253 181.2 53 5 4.5433 98 230
|
||||
44 1 21.5 85 157 92.2 55 3 3.8918 84 181
|
||||
60 2 24.3 103 141 86.6 33 4 4.6728 78 156
|
||||
52 1 24.5 90 198 129 29 7 5.2983 86 233
|
||||
38 1 21.3 72 165 60.2 88 2 4.4308 90 60
|
||||
61 1 25.8 90 280 195.4 55 5 4.9972 90 219
|
||||
68 2 24.8 101 221 151.4 60 4 3.8712 87 80
|
||||
28 2 31.5 83 228 149.4 38 6 5.3132 83 68
|
||||
65 2 33.5 102 190 126.2 35 5 4.9698 102 332
|
||||
69 1 28.1 113 234 142.8 52 4 5.2781 77 248
|
||||
51 1 24.3 85.33 153 71.6 71 2.15 3.9512 82 84
|
||||
29 1 35 98.33 204 142.6 50 4.08 4.0431 91 200
|
||||
55 2 23.5 93 177 126.8 41 4 3.8286 83 55
|
||||
34 2 30 83 185 107.2 53 3 4.8203 92 85
|
||||
67 1 20.7 83 170 99.8 59 3 4.0254 77 89
|
||||
49 1 25.6 76 161 99.8 51 3 3.9318 78 31
|
||||
55 2 22.9 81 123 67.2 41 3 4.3041 88 129
|
||||
59 2 25.1 90 163 101.4 46 4 4.3567 91 83
|
||||
53 1 33.2 82.67 186 106.8 46 4.04 5.112 102 275
|
||||
48 2 24.1 110 209 134.6 58 4 4.4067 100 65
|
||||
52 1 29.5 104.33 211 132.8 49 4.31 4.9836 98 198
|
||||
69 1 29.6 122 231 128.4 56 4 5.451 86 236
|
||||
60 2 22.8 110 245 189.8 39 6 4.3944 88 253
|
||||
46 2 22.7 83 183 125.8 32 6 4.8363 75 124
|
||||
51 2 26.2 101 161 99.6 48 3 4.2047 88 44
|
||||
67 2 23.5 96 207 138.2 42 5 4.8978 111 172
|
||||
49 1 22.1 85 136 63.4 62 2.19 3.9703 72 114
|
||||
46 2 26.5 94 247 160.2 59 4 4.9345 111 142
|
||||
47 1 32.4 105 188 125 46 4.09 4.4427 99 109
|
||||
75 1 30.1 78 222 154.2 44 5.05 4.7791 97 180
|
||||
28 1 24.2 93 174 106.4 54 3 4.2195 84 144
|
||||
65 2 31.3 110 213 128 47 5 5.247 91 163
|
||||
42 1 30.1 91 182 114.8 49 4 4.5109 82 147
|
||||
51 1 24.5 79 212 128.6 65 3 4.5218 91 97
|
||||
53 2 27.7 95 190 101.8 41 5 5.4638 101 220
|
||||
54 1 23.2 110.67 238 162.8 48 4.96 4.9127 108 190
|
||||
73 1 27 102 211 121 67 3 4.7449 99 109
|
||||
54 1 26.8 108 176 80.6 67 3 4.9558 106 191
|
||||
42 1 29.2 93 249 174.2 45 6 5.0039 92 122
|
||||
75 1 31.2 117.67 229 138.8 29 7.9 5.7236 106 230
|
||||
55 2 32.1 112.67 207 92.4 25 8.28 6.1048 111 242
|
||||
68 2 25.7 109 233 112.6 35 7 6.0568 105 248
|
||||
57 1 26.9 98 246 165.2 38 7 5.366 96 249
|
||||
48 1 31.4 75.33 242 151.6 38 6.37 5.5683 103 192
|
||||
61 2 25.6 85 184 116.2 39 5 4.9698 98 131
|
||||
69 1 37 103 207 131.4 55 4 4.6347 90 237
|
||||
38 1 32.6 77 168 100.6 47 4 4.625 96 78
|
||||
45 2 21.2 94 169 96.8 55 3 4.4543 102 135
|
||||
51 2 29.2 107 187 139 32 6 4.382 95 244
|
||||
71 2 24 84 138 85.8 39 4 4.1897 90 199
|
||||
57 1 36.1 117 181 108.2 34 5 5.2679 100 270
|
||||
56 2 25.8 103 177 114.4 34 5 4.9628 99 164
|
||||
32 2 22 88 137 78.6 48 3 3.9512 78 72
|
||||
50 1 21.9 91 190 111.2 67 3 4.0775 77 96
|
||||
43 1 34.3 84 256 172.6 33 8 5.5294 104 306
|
||||
54 2 25.2 115 181 120 39 5 4.7005 92 91
|
||||
31 1 23.3 85 190 130.8 43 4 4.3944 77 214
|
||||
56 1 25.7 80 244 151.6 59 4 5.118 95 95
|
||||
44 1 25.1 133 182 113 55 3 4.2485 84 216
|
||||
57 2 31.9 111 173 116.2 41 4 4.3694 87 263
|
||||
64 2 28.4 111 184 127 41 4 4.382 97 178
|
||||
43 1 28.1 121 192 121 60 3 4.0073 93 113
|
||||
19 1 25.3 83 225 156.6 46 5 4.7185 84 200
|
||||
71 2 26.1 85 220 152.4 47 5 4.6347 91 139
|
||||
50 2 28 104 282 196.8 44 6 5.3279 95 139
|
||||
59 2 23.6 73 180 107.4 51 4 4.6821 84 88
|
||||
57 1 24.5 93 186 96.6 71 3 4.5218 91 148
|
||||
49 2 21 82 119 85.4 23 5 3.9703 74 88
|
||||
41 2 32 126 198 104.2 49 4 5.4116 124 243
|
||||
25 2 22.6 85 130 71 48 3 4.0073 81 71
|
||||
52 2 19.7 81 152 53.4 82 2 4.4188 82 77
|
||||
34 1 21.2 84 254 113.4 52 5 6.0936 92 109
|
||||
42 2 30.6 101 269 172.2 50 5 5.4553 106 272
|
||||
28 2 25.5 99 162 101.6 46 4 4.2767 94 60
|
||||
47 2 23.3 90 195 125.8 54 4 4.3307 73 54
|
||||
32 2 31 100 177 96.2 45 4 5.1874 77 221
|
||||
43 1 18.5 87 163 93.6 61 2.67 3.7377 80 90
|
||||
59 2 26.9 104 194 126.6 43 5 4.804 106 311
|
||||
53 1 28.3 101 179 107 48 4 4.7875 101 281
|
||||
60 1 25.7 103 158 84.6 64 2 3.8501 97 182
|
||||
54 2 36.1 115 163 98.4 43 4 4.6821 101 321
|
||||
35 2 24.1 94.67 155 97.4 32 4.84 4.852 94 58
|
||||
49 2 25.8 89 182 118.6 39 5 4.804 115 262
|
||||
58 1 22.8 91 196 118.8 48 4 4.9836 115 206
|
||||
36 2 39.1 90 219 135.8 38 6 5.4205 103 233
|
||||
46 2 42.2 99 211 137 44 5 5.0106 99 242
|
||||
44 2 26.6 99 205 109 43 5 5.5797 111 123
|
||||
46 1 29.9 83 171 113 38 4.5 4.585 98 167
|
||||
54 1 21 78 188 107.4 70 3 3.9703 73 63
|
||||
63 2 25.5 109 226 103.2 46 5 5.9506 87 197
|
||||
41 2 24.2 90 199 123.6 57 4 4.5218 86 71
|
||||
28 1 25.4 93 141 79 49 3 4.1744 91 168
|
||||
19 1 23.2 75 143 70.4 52 3 4.6347 72 140
|
||||
61 2 26.1 126 215 129.8 57 4 4.9488 96 217
|
||||
48 1 32.7 93 276 198.6 43 6.42 5.1475 91 121
|
||||
54 2 27.3 100 200 144 33 6 4.7449 76 235
|
||||
53 2 26.6 93 185 122.4 36 5 4.8903 82 245
|
||||
48 1 22.8 101 110 41.6 56 2 4.1271 97 40
|
||||
53 1 28.8 111.67 145 87.2 46 3.15 4.0775 85 52
|
||||
29 2 18.1 73 158 99 41 4 4.4998 78 104
|
||||
62 1 32 88 172 69 38 4 5.7838 100 132
|
||||
50 2 23.7 92 166 97 52 3 4.4427 93 88
|
||||
58 2 23.6 96 257 171 59 4 4.9053 82 69
|
||||
55 2 24.6 109 143 76.4 51 3 4.3567 88 219
|
||||
54 1 22.6 90 183 104.2 64 3 4.3041 92 72
|
||||
36 1 27.8 73 153 104.4 42 4 3.4965 73 201
|
||||
63 2 24.1 111 184 112.2 44 4 4.9345 82 110
|
||||
47 2 26.5 70 181 104.8 63 3 4.1897 70 51
|
||||
51 2 32.8 112 202 100.6 37 5 5.7746 109 277
|
||||
42 1 19.9 76 146 83.2 55 3 3.6636 79 63
|
||||
37 2 23.6 94 205 138.8 53 4 4.1897 107 118
|
||||
28 1 22.1 82 168 100.6 54 3 4.2047 86 69
|
||||
58 1 28.1 111 198 80.6 31 6 6.0684 93 273
|
||||
32 1 26.5 86 184 101.6 53 4 4.9904 78 258
|
||||
25 2 23.5 88 143 80.8 55 3 3.5835 83 43
|
||||
63 1 26 85.67 155 78.2 46 3.37 5.037 97 198
|
||||
52 1 27.8 85 219 136 49 4 5.1358 75 242
|
||||
65 2 28.5 109 201 123 46 4 5.0752 96 232
|
||||
42 1 30.6 121 176 92.8 69 3 4.2627 89 175
|
||||
53 1 22.2 78 164 81 70 2 4.1744 101 93
|
||||
79 2 23.3 88 186 128.4 33 6 4.8122 102 168
|
||||
43 1 35.4 93 185 100.2 44 4 5.3181 101 275
|
||||
44 1 31.4 115 165 97.6 52 3 4.3438 89 293
|
||||
62 2 37.8 119 113 51 31 4 5.0434 84 281
|
||||
33 1 18.9 70 162 91.8 59 3 4.0254 58 72
|
||||
56 1 35 79.33 195 140.8 42 4.64 4.1109 96 140
|
||||
66 1 21.7 126 212 127.8 45 4.71 5.2781 101 189
|
||||
34 2 25.3 111 230 162 39 6 4.9767 90 181
|
||||
46 2 23.8 97 224 139.2 42 5 5.366 81 209
|
||||
50 1 31.8 82 136 69.2 55 2 4.0775 85 136
|
||||
69 1 34.3 113 200 123.8 54 4 4.7095 112 261
|
||||
34 1 26.3 87 197 120 63 3 4.2485 96 113
|
||||
71 2 27 93.33 269 190.2 41 6.56 5.2417 93 131
|
||||
47 1 27.2 80 208 145.6 38 6 4.804 92 174
|
||||
41 1 33.8 123.33 187 127 45 4.16 4.3175 100 257
|
||||
34 1 33 73 178 114.6 51 3.49 4.1271 92 55
|
||||
51 1 24.1 87 261 175.6 69 4 4.4067 93 84
|
||||
43 1 21.3 79 141 78.8 53 3 3.8286 90 42
|
||||
55 1 23 94.67 190 137.6 38 5 4.2767 106 146
|
||||
59 2 27.9 101 218 144.2 38 6 5.1874 95 212
|
||||
27 2 33.6 110 246 156.6 57 4 5.0876 89 233
|
||||
51 2 22.7 103 217 162.4 30 7 4.8122 80 91
|
||||
49 2 27.4 89 177 113 37 5 4.9053 97 111
|
||||
27 1 22.6 71 116 43.4 56 2 4.4188 79 152
|
||||
57 2 23.2 107.33 231 159.4 41 5.63 5.0304 112 120
|
||||
39 2 26.9 93 136 75.4 48 3 4.1431 99 67
|
||||
62 2 34.6 120 215 129.2 43 5 5.366 123 310
|
||||
37 1 23.3 88 223 142 65 3.4 4.3567 82 94
|
||||
46 1 21.1 80 205 144.4 42 5 4.5326 87 183
|
||||
68 2 23.5 101 162 85.4 59 3 4.4773 91 66
|
||||
51 1 31.5 93 231 144 49 4.7 5.2523 117 173
|
||||
41 1 20.8 86 223 128.2 83 3 4.0775 89 72
|
||||
53 1 26.5 97 193 122.4 58 3 4.1431 99 49
|
||||
45 1 24.2 83 177 118.4 45 4 4.2195 82 64
|
||||
33 1 19.5 80 171 85.4 75 2 3.9703 80 48
|
||||
60 2 28.2 112 185 113.8 42 4 4.9836 93 178
|
||||
47 2 24.9 75 225 166 42 5 4.4427 102 104
|
||||
60 2 24.9 99.67 162 106.6 43 3.77 4.1271 95 132
|
||||
36 1 30 95 201 125.2 42 4.79 5.1299 85 220
|
||||
36 1 19.6 71 250 133.2 97 3 4.5951 92 57
|
|
Loading…
Reference in New Issue