Data Science_W3

 

Initially summarize the questions attempted

Q1: ISLR Textbook chapter review questions

Chapter 3 – Linear Regression

Review the following sections in Python

• Load Datasets
• 3.1 Simple Linear Regression
• 3.2 Multiple Linear Regression
• 3.3 Other Considerations in the Regression Model

Q2 Textbook Theory Questions 3.7 Exercises

1. Describe the null hypotheses to which the p-values given in Table 3.4 correspond. Explain what conclusions you can draw based on these p-values. Your explanation should be phrased in terms of sales, TV, radio, and newspaper, rather than in terms of the coefficients of the linear model.

Q3 Applied Textbook Questions with Python 3.7 Exercises

Hint – several github sites have the complete solution in python e.g.

https://github.com/mscaudill/IntroStatLearn

https://botlnec.github.io/islp/

8. This question involves the use of simple linear regression on the Auto data set. (a) Perform a simple linear regression with mpg as the response and horsepower as the predictor. Print the results. Comment on the output. For example:

i. Is there a relationship between the predictor and the response?

ii. How strong is the relationship between the predictor and the response?

iii. Is the relationship between the predictor and the response positive or negative?

iv. Predictions

1. What is the predicted mpg associated with a horsepower of 98? What are the associated 95 % confidence and prediction intervals?
2. Plot the response and the predictor, display the least squares regression line.
3. Produce diagnostic plots of the least squares regression fit. Comment on any problems you see with the fit. 

9. This question involves the use of multiple linear regression on the Auto data set.

(a) Produce a scatterplot matrix which includes all of the variables in the data set.

(b) Compute the matrix of correlations between the variables.

(c) Perform a multiple linear regression with mpg as the response and all other variables except name as the predictors and print the results. Comment on the output. For instance:

i. Is there a relationship between the predictors and the response?

ii. Which predictors appear to have a statistically significant relationship to the response?

iii. What does the coefficient for the year variable suggest?

(d) Produce diagnostic plots of the linear regression fit. Comment on any problems you see with the fit. Do the residual plots suggest any unusually large outliers? Does the leverage plot identify any observations with unusually high leverage?

(e) Fit linear regression models with interaction effects. Do any interactions appear to be statistically significant?

(f) Try a few different transformations of the variables, such as log(X), √ X, X2. Comment on your findings.

Opional

15. This problem involves the Boston data set, which we saw in the lab for this chapter. We will now try to predict per capita crime rate using the other variables in this data set. In other words, per capita crime rate is the response, and the other variables are the predictors.

(a) For each predictor, fit a simple linear regression model to predict the response. Describe your results. In which of the models is there a statistically significant association between the predictor and the response? Create some plots to back up your assertions.

(b) Fit a multiple regression model to predict the response using all of the predictors. Describe your results. For which predictors can we reject the null hypothesis H0 : βj = 0?

(c) How do your results from (a) compare to your results from (b)? Create a plot displaying the univariate regression coefficients from (a) on the x-axis, and the multiple regression coefficients from (b) on the y-axis. That is, each predictor is displayed as a single point in the plot. Its coefficient in a simple linear regression model is shown on the x-axis, and its coefficient estimate in the multiple linear regression model is shown on the y-axis.

(d) Is there evidence of non-linear association between any of the predictors and the response? To answer this question, for each predictor X, fit a model of the form Y = β0 + β1X + β2X2 + β3X3 + .

,TV,radio,newspaper,sales
1,230.1,37.8,69.2,22.1
2,44.5,39.3,45.1,10.4
3,17.2,45.9,69.3,9.3
4,151.5,41.3,58.5,18.5
5,180.8,10.8,58.4,12.9
6,8.7,48.9,75,7.2
7,57.5,32.8,23.5,11.8
8,120.2,19.6,11.6,13.2
9,8.6,2.1,1,4.8
10,199.8,2.6,21.2,10.6
11,66.1,5.8,24.2,8.6
12,214.7,24,4,17.4
13,23.8,35.1,65.9,9.2
14,97.5,7.6,7.2,9.7
15,204.1,32.9,46,19
16,195.4,47.7,52.9,22.4
17,67.8,36.6,114,12.5
18,281.4,39.6,55.8,24.4
19,69.2,20.5,18.3,11.3
20,147.3,23.9,19.1,14.6
21,218.4,27.7,53.4,18
22,237.4,5.1,23.5,12.5
23,13.2,15.9,49.6,5.6
24,228.3,16.9,26.2,15.5
25,62.3,12.6,18.3,9.7
26,262.9,3.5,19.5,12
27,142.9,29.3,12.6,15
28,240.1,16.7,22.9,15.9
29,248.8,27.1,22.9,18.9
30,70.6,16,40.8,10.5
31,292.9,28.3,43.2,21.4
32,112.9,17.4,38.6,11.9
33,97.2,1.5,30,9.6
34,265.6,20,0.3,17.4
35,95.7,1.4,7.4,9.5
36,290.7,4.1,8.5,12.8
37,266.9,43.8,5,25.4
38,74.7,49.4,45.7,14.7
39,43.1,26.7,35.1,10.1
40,228,37.7,32,21.5
41,202.5,22.3,31.6,16.6
42,177,33.4,38.7,17.1
43,293.6,27.7,1.8,20.7
44,206.9,8.4,26.4,12.9
45,25.1,25.7,43.3,8.5
46,175.1,22.5,31.5,14.9
47,89.7,9.9,35.7,10.6
48,239.9,41.5,18.5,23.2
49,227.2,15.8,49.9,14.8
50,66.9,11.7,36.8,9.7
51,199.8,3.1,34.6,11.4
52,100.4,9.6,3.6,10.7
53,216.4,41.7,39.6,22.6
54,182.6,46.2,58.7,21.2
55,262.7,28.8,15.9,20.2
56,198.9,49.4,60,23.7
57,7.3,28.1,41.4,5.5
58,136.2,19.2,16.6,13.2
59,210.8,49.6,37.7,23.8
60,210.7,29.5,9.3,18.4
61,53.5,2,21.4,8.1
62,261.3,42.7,54.7,24.2
63,239.3,15.5,27.3,15.7
64,102.7,29.6,8.4,14
65,131.1,42.8,28.9,18
66,69,9.3,0.9,9.3
67,31.5,24.6,2.2,9.5
68,139.3,14.5,10.2,13.4
69,237.4,27.5,11,18.9
70,216.8,43.9,27.2,22.3
71,199.1,30.6,38.7,18.3
72,109.8,14.3,31.7,12.4
73,26.8,33,19.3,8.8
74,129.4,5.7,31.3,11
75,213.4,24.6,13.1,17
76,16.9,43.7,89.4,8.7
77,27.5,1.6,20.7,6.9
78,120.5,28.5,14.2,14.2
79,5.4,29.9,9.4,5.3
80,116,7.7,23.1,11
81,76.4,26.7,22.3,11.8
82,239.8,4.1,36.9,12.3
83,75.3,20.3,32.5,11.3
84,68.4,44.5,35.6,13.6
85,213.5,43,33.8,21.7
86,193.2,18.4,65.7,15.2
87,76.3,27.5,16,12
88,110.7,40.6,63.2,16
89,88.3,25.5,73.4,12.9
90,109.8,47.8,51.4,16.7
91,134.3,4.9,9.3,11.2
92,28.6,1.5,33,7.3
93,217.7,33.5,59,19.4
94,250.9,36.5,72.3,22.2
95,107.4,14,10.9,11.5
96,163.3,31.6,52.9,16.9
97,197.6,3.5,5.9,11.7
98,184.9,21,22,15.5
99,289.7,42.3,51.2,25.4
100,135.2,41.7,45.9,17.2
101,222.4,4.3,49.8,11.7
102,296.4,36.3,100.9,23.8
103,280.2,10.1,21.4,14.8
104,187.9,17.2,17.9,14.7
105,238.2,34.3,5.3,20.7
106,137.9,46.4,59,19.2
107,25,11,29.7,7.2
108,90.4,0.3,23.2,8.7
109,13.1,0.4,25.6,5.3
110,255.4,26.9,5.5,19.8
111,225.8,8.2,56.5,13.4
112,241.7,38,23.2,21.8
113,175.7,15.4,2.4,14.1
114,209.6,20.6,10.7,15.9
115,78.2,46.8,34.5,14.6
116,75.1,35,52.7,12.6
117,139.2,14.3,25.6,12.2
118,76.4,0.8,14.8,9.4
119,125.7,36.9,79.2,15.9
120,19.4,16,22.3,6.6
121,141.3,26.8,46.2,15.5
122,18.8,21.7,50.4,7
123,224,2.4,15.6,11.6
124,123.1,34.6,12.4,15.2
125,229.5,32.3,74.2,19.7
126,87.2,11.8,25.9,10.6
127,7.8,38.9,50.6,6.6
128,80.2,0,9.2,8.8
129,220.3,49,3.2,24.7
130,59.6,12,43.1,9.7
131,0.7,39.6,8.7,1.6
132,265.2,2.9,43,12.7
133,8.4,27.2,2.1,5.7
134,219.8,33.5,45.1,19.6
135,36.9,38.6,65.6,10.8
136,48.3,47,8.5,11.6
137,25.6,39,9.3,9.5
138,273.7,28.9,59.7,20.8
139,43,25.9,20.5,9.6
140,184.9,43.9,1.7,20.7
141,73.4,17,12.9,10.9
142,193.7,35.4,75.6,19.2
143,220.5,33.2,37.9,20.1
144,104.6,5.7,34.4,10.4
145,96.2,14.8,38.9,11.4
146,140.3,1.9,9,10.3
147,240.1,7.3,8.7,13.2
148,243.2,49,44.3,25.4
149,38,40.3,11.9,10.9
150,44.7,25.8,20.6,10.1
151,280.7,13.9,37,16.1
152,121,8.4,48.7,11.6
153,197.6,23.3,14.2,16.6
154,171.3,39.7,37.7,19
155,187.8,21.1,9.5,15.6
156,4.1,11.6,5.7,3.2
157,93.9,43.5,50.5,15.3
158,149.8,1.3,24.3,10.1
159,11.7,36.9,45.2,7.3
160,131.7,18.4,34.6,12.9
161,172.5,18.1,30.7,14.4
162,85.7,35.8,49.3,13.3
163,188.4,18.1,25.6,14.9
164,163.5,36.8,7.4,18
165,117.2,14.7,5.4,11.9
166,234.5,3.4,84.8,11.9
167,17.9,37.6,21.6,8
168,206.8,5.2,19.4,12.2
169,215.4,23.6,57.6,17.1
170,284.3,10.6,6.4,15
171,50,11.6,18.4,8.4
172,164.5,20.9,47.4,14.5
173,19.6,20.1,17,7.6
174,168.4,7.1,12.8,11.7
175,222.4,3.4,13.1,11.5
176,276.9,48.9,41.8,27
177,248.4,30.2,20.3,20.2
178,170.2,7.8,35.2,11.7
179,276.7,2.3,23.7,11.8
180,165.6,10,17.6,12.6
181,156.6,2.6,8.3,10.5
182,218.5,5.4,27.4,12.2
183,56.2,5.7,29.7,8.7
184,287.6,43,71.8,26.2
185,253.8,21.3,30,17.6
186,205,45.1,19.6,22.6
187,139.5,2.1,26.6,10.3
188,191.1,28.7,18.2,17.3
189,286,13.9,3.7,15.9
190,18.7,12.1,23.4,6.7
191,39.5,41.1,5.8,10.8
192,75.5,10.8,6,9.9
193,17.2,4.1,31.6,5.9
194,166.8,42,3.6,19.6
195,149.7,35.6,6,17.3
196,38.2,3.7,13.8,7.6
197,94.2,4.9,8.1,9.7
198,177,9.3,6.4,12.8
199,283.6,42,66.2,25.5
200,232.1,8.6,8.7,13.4

Name ID Homework

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· Share the code and the plots

· Put your name and id number

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HW03 Cover Sheet

Identify all questions that you attempted in this template

Q

1

: ISLR Textbook chapter review

Summarize the textbook notes using for chapter 3:

https://github.com/JWarmenhoven/ISLR-python

clicking on the link

Chapter 3 – Linear Regression

Review the following sections in Python

·

Load Datasets

·

3.1 Simple Linear Regression

·

3.2 Multiple Linear Regression

·

3.3 Other Considerations in the Regression Model

Q2 Textbook Theory Questions 3.7 Exercises

1. Describe the null hypotheses to which the p-values given in Table 3.4 correspond. Explain what conclusions you can draw based on these p-values. Your explanation should be phrased in terms of sales, TV, radio, and newspaper, rather than in terms of the coefficients of the linear model.

Q3 Applied Textbook Questions with Python 3.7 Exercises

Hint – several github sites have the complete solution in python e.g.

https://github.com/mscaudill/IntroStatLearn

https://botlnec.github.io/islp/

8. This question involves the use of simple linear regression on the Auto data set. (a) Perform a simple linear regression with mpg as the response and horsepower as the predictor. Print the results. Comment on the output. For example:

i. Is there a relationship between the predictor and the response?

ii. How strong is the relationship between the predictor and the response?

iii. Is the relationship between the predictor and the response positive or negative?

iv. Predictions

a) What is the predicted mpg associated with a horsepower of 98? What are the associated 95 % confidence and prediction intervals?

b) Plot the response and the predictor, display the least squares regression line.

c) Produce diagnostic plots of the least squares regression fit. Comment on any problems you see with the fit.

9. This question involves the use of multiple linear regression on the Auto data set.

(a) Produce a scatterplot matrix which includes all of the variables in the data set.

(b) Compute the matrix of correlations between the variables.

(c) Perform a multiple linear regression with mpg as the response and all other variables except name as the predictors and print the results. Comment on the output. For instance:

i. Is there a relationship between the predictors and the response?

ii. Which predictors appear to have a statistically significant relationship to the response?

iii. What does the coefficient for the year variable suggest?

(d) Produce diagnostic plots of the linear regression fit. Comment on any problems you see with the fit. Do the residual plots suggest any unusually large outliers? Does the leverage plot identify any observations with unusually high leverage?

(e) Fit linear regression models with interaction effects. Do any interactions appear to be statistically significant?

(f) Try a few different transformations of the variables, such as log(X), √ X, X2. Comment on your findings.

15. This problem involves the Boston data set, which we saw in the lab for this chapter. We will now try to predict per capita crime rate using the other variables in this data set. In other words, per capita crime rate is the response, and the other variables are the predictors.

(a) For each predictor, fit a simple linear regression model to predict the response. Describe your results. In which of the models is there a statistically significant association between the predictor and the response? Create some plots to back up your assertions.

(b) Fit a multiple regression model to predict the response using all of the predictors. Describe your results. For which predictors can we reject the null hypothesis H0 : βj = 0?

(c) How do your results from (a) compare to your results from (b)? Create a plot displaying the univariate regression coefficients from (a) on the x-axis, and the multiple regression coefficients from (b) on the y-axis. That is, each predictor is displayed as a single point in the plot. Its coefficient in a simple linear regression model is shown on the x-axis, and its coefficient estimate in the multiple linear regression model is shown on the y-axis.

(d) Is there evidence of non-linear association between any of the predictors and the response? To answer this question, for each predictor X, fit a model of the form Y = β0 + β1X + β2X2 + β3X3 + .

1

5/19/19

“”,”crim”,”zn”,”indus”,”chas”,”nox”,”rm”,”age”,”dis”,”rad”,”tax”,”ptratio”,”black”,”lstat”,”medv”
“1”,0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
“2”,0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
“3”,0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
“4”,0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
“5”,0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
“6”,0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
“7”,0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9
“8”,0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1
“9”,0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5
“10”,0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9
“11”,0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15
“12”,0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9
“13”,0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7
“14”,0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4
“15”,0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2
“16”,0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9
“17”,1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1
“18”,0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5
“19”,0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2
“20”,0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2
“21”,1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6
“22”,0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6
“23”,1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2
“24”,0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5
“25”,0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6
“26”,0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9
“27”,0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6
“28”,0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8
“29”,0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4
“30”,1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21
“31”,1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7
“32”,1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5
“33”,1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2
“34”,1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1
“35”,1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5
“36”,0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68,18.9
“37”,0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20
“38”,0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21
“39”,0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7
“40”,0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8
“41”,0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9
“42”,0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6
“43”,0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3
“44”,0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7
“45”,0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2
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“497”,0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
“498”,0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
“499”,0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
“500”,0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
“501”,0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
“502”,0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
“503”,0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
“504”,0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
“505”,0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
“506”,0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9

,Income,Limit,Rating,Cards,Age,Education,Gender,Student,Married,Ethnicity,Balance
1,14.891,3606,283,2,34,11,Male,No,Yes,Caucasian,333
2,106.025,6645,483,3,82,15,Female,Yes,Yes,Asian,903
3,104.593,7075,514,4,71,11,Male,No,No,Asian,580
4,148.924,9504,681,3,36,11,Female,No,No,Asian,964
5,55.882,4897,357,2,68,16,Male,No,Yes,Caucasian,331
6,80.18,8047,569,4,77,10,Male,No,No,Caucasian,1151
7,20.996,3388,259,2,37,12,Female,No,No,African American,203
8,71.408,7114,512,2,87,9,Male,No,No,Asian,872
9,15.125,3300,266,5,66,13,Female,No,No,Caucasian,279
10,71.061,6819,491,3,41,19,Female,Yes,Yes,African American,1350
11,63.095,8117,589,4,30,14,Male,No,Yes,Caucasian,1407
12,15.045,1311,138,3,64,16,Male,No,No,Caucasian,0
13,80.616,5308,394,1,57,7,Female,No,Yes,Asian,204
14,43.682,6922,511,1,49,9,Male,No,Yes,Caucasian,1081
15,19.144,3291,269,2,75,13,Female,No,No,African American,148
16,20.089,2525,200,3,57,15,Female,No,Yes,African American,0
17,53.598,3714,286,3,73,17,Female,No,Yes,African American,0
18,36.496,4378,339,3,69,15,Female,No,Yes,Asian,368
19,49.57,6384,448,1,28,9,Female,No,Yes,Asian,891
20,42.079,6626,479,2,44,9,Male,No,No,Asian,1048
21,17.7,2860,235,4,63,16,Female,No,No,Asian,89
22,37.348,6378,458,1,72,17,Female,No,No,Caucasian,968
23,20.103,2631,213,3,61,10,Male,No,Yes,African American,0
24,64.027,5179,398,5,48,8,Male,No,Yes,African American,411
25,10.742,1757,156,3,57,15,Female,No,No,Caucasian,0
26,14.09,4323,326,5,25,16,Female,No,Yes,African American,671
27,42.471,3625,289,6,44,12,Female,Yes,No,Caucasian,654
28,32.793,4534,333,2,44,16,Male,No,No,African American,467
29,186.634,13414,949,2,41,14,Female,No,Yes,African American,1809
30,26.813,5611,411,4,55,16,Female,No,No,Caucasian,915
31,34.142,5666,413,4,47,5,Female,No,Yes,Caucasian,863
32,28.941,2733,210,5,43,16,Male,No,Yes,Asian,0
33,134.181,7838,563,2,48,13,Female,No,No,Caucasian,526
34,31.367,1829,162,4,30,10,Male,No,Yes,Caucasian,0
35,20.15,2646,199,2,25,14,Female,No,Yes,Asian,0
36,23.35,2558,220,3,49,12,Female,Yes,No,Caucasian,419
37,62.413,6457,455,2,71,11,Female,No,Yes,Caucasian,762
38,30.007,6481,462,2,69,9,Female,No,Yes,Caucasian,1093
39,11.795,3899,300,4,25,10,Female,No,No,Caucasian,531
40,13.647,3461,264,4,47,14,Male,No,Yes,Caucasian,344
41,34.95,3327,253,3,54,14,Female,No,No,African American,50
42,113.659,7659,538,2,66,15,Male,Yes,Yes,African American,1155
43,44.158,4763,351,2,66,13,Female,No,Yes,Asian,385
44,36.929,6257,445,1,24,14,Female,No,Yes,Asian,976
45,31.861,6375,469,3,25,16,Female,No,Yes,Caucasian,1120
46,77.38,7569,564,3,50,12,Female,No,Yes,Caucasian,997
47,19.531,5043,376,2,64,16,Female,Yes,Yes,Asian,1241
48,44.646,4431,320,2,49,15,Male,Yes,Yes,Caucasian,797
49,44.522,2252,205,6,72,15,Male,No,Yes,Asian,0
50,43.479,4569,354,4,49,13,Male,Yes,Yes,African American,902
51,36.362,5183,376,3,49,15,Male,No,Yes,African American,654
52,39.705,3969,301,2,27,20,Male,No,Yes,African American,211
53,44.205,5441,394,1,32,12,Male,No,Yes,Caucasian,607
54,16.304,5466,413,4,66,10,Male,No,Yes,Asian,957
55,15.333,1499,138,2,47,9,Female,No,Yes,Asian,0
56,32.916,1786,154,2,60,8,Female,No,Yes,Asian,0
57,57.1,4742,372,7,79,18,Female,No,Yes,Asian,379
58,76.273,4779,367,4,65,14,Female,No,Yes,Caucasian,133
59,10.354,3480,281,2,70,17,Male,No,Yes,Caucasian,333
60,51.872,5294,390,4,81,17,Female,No,No,Caucasian,531
61,35.51,5198,364,2,35,20,Female,No,No,Asian,631
62,21.238,3089,254,3,59,10,Female,No,No,Caucasian,108
63,30.682,1671,160,2,77,7,Female,No,No,Caucasian,0
64,14.132,2998,251,4,75,17,Male,No,No,Caucasian,133
65,32.164,2937,223,2,79,15,Female,No,Yes,African American,0
66,12,4160,320,4,28,14,Female,No,Yes,Caucasian,602
67,113.829,9704,694,4,38,13,Female,No,Yes,Asian,1388
68,11.187,5099,380,4,69,16,Female,No,No,African American,889
69,27.847,5619,418,2,78,15,Female,No,Yes,Caucasian,822
70,49.502,6819,505,4,55,14,Male,No,Yes,Caucasian,1084
71,24.889,3954,318,4,75,12,Male,No,Yes,Caucasian,357
72,58.781,7402,538,2,81,12,Female,No,Yes,Asian,1103
73,22.939,4923,355,1,47,18,Female,No,Yes,Asian,663
74,23.989,4523,338,4,31,15,Male,No,No,Caucasian,601
75,16.103,5390,418,4,45,10,Female,No,Yes,Caucasian,945
76,33.017,3180,224,2,28,16,Male,No,Yes,African American,29
77,30.622,3293,251,1,68,16,Male,Yes,No,Caucasian,532
78,20.936,3254,253,1,30,15,Female,No,No,Asian,145
79,110.968,6662,468,3,45,11,Female,No,Yes,Caucasian,391
80,15.354,2101,171,2,65,14,Male,No,No,Asian,0
81,27.369,3449,288,3,40,9,Female,No,Yes,Caucasian,162
82,53.48,4263,317,1,83,15,Male,No,No,Caucasian,99
83,23.672,4433,344,3,63,11,Male,No,No,Caucasian,503
84,19.225,1433,122,3,38,14,Female,No,No,Caucasian,0
85,43.54,2906,232,4,69,11,Male,No,No,Caucasian,0
86,152.298,12066,828,4,41,12,Female,No,Yes,Asian,1779
87,55.367,6340,448,1,33,15,Male,No,Yes,Caucasian,815
88,11.741,2271,182,4,59,12,Female,No,No,Asian,0
89,15.56,4307,352,4,57,8,Male,No,Yes,African American,579
90,59.53,7518,543,3,52,9,Female,No,No,African American,1176
91,20.191,5767,431,4,42,16,Male,No,Yes,African American,1023
92,48.498,6040,456,3,47,16,Male,No,Yes,Caucasian,812
93,30.733,2832,249,4,51,13,Male,No,No,Caucasian,0
94,16.479,5435,388,2,26,16,Male,No,No,African American,937
95,38.009,3075,245,3,45,15,Female,No,No,African American,0
96,14.084,855,120,5,46,17,Female,No,Yes,African American,0
97,14.312,5382,367,1,59,17,Male,Yes,No,Asian,1380
98,26.067,3388,266,4,74,17,Female,No,Yes,African American,155
99,36.295,2963,241,2,68,14,Female,Yes,No,African American,375
100,83.851,8494,607,5,47,18,Male,No,No,Caucasian,1311
101,21.153,3736,256,1,41,11,Male,No,No,Caucasian,298
102,17.976,2433,190,3,70,16,Female,Yes,No,Caucasian,431
103,68.713,7582,531,2,56,16,Male,Yes,No,Caucasian,1587
104,146.183,9540,682,6,66,15,Male,No,No,Caucasian,1050
105,15.846,4768,365,4,53,12,Female,No,No,Caucasian,745
106,12.031,3182,259,2,58,18,Female,No,Yes,Caucasian,210
107,16.819,1337,115,2,74,15,Male,No,Yes,Asian,0
108,39.11,3189,263,3,72,12,Male,No,No,Asian,0
109,107.986,6033,449,4,64,14,Male,No,Yes,Caucasian,227
110,13.561,3261,279,5,37,19,Male,No,Yes,Asian,297
111,34.537,3271,250,3,57,17,Female,No,Yes,Asian,47
112,28.575,2959,231,2,60,11,Female,No,No,African American,0
113,46.007,6637,491,4,42,14,Male,No,Yes,Caucasian,1046
114,69.251,6386,474,4,30,12,Female,No,Yes,Asian,768
115,16.482,3326,268,4,41,15,Male,No,No,Caucasian,271
116,40.442,4828,369,5,81,8,Female,No,No,African American,510
117,35.177,2117,186,3,62,16,Female,No,No,Caucasian,0
118,91.362,9113,626,1,47,17,Male,No,Yes,Asian,1341
119,27.039,2161,173,3,40,17,Female,No,No,Caucasian,0
120,23.012,1410,137,3,81,16,Male,No,No,Caucasian,0
121,27.241,1402,128,2,67,15,Female,No,Yes,Asian,0
122,148.08,8157,599,2,83,13,Male,No,Yes,Caucasian,454
123,62.602,7056,481,1,84,11,Female,No,No,Caucasian,904
124,11.808,1300,117,3,77,14,Female,No,No,African American,0
125,29.564,2529,192,1,30,12,Female,No,Yes,Caucasian,0
126,27.578,2531,195,1,34,15,Female,No,Yes,Caucasian,0
127,26.427,5533,433,5,50,15,Female,Yes,Yes,Asian,1404
128,57.202,3411,259,3,72,11,Female,No,No,Caucasian,0
129,123.299,8376,610,2,89,17,Male,Yes,No,African American,1259
130,18.145,3461,279,3,56,15,Male,No,Yes,African American,255
131,23.793,3821,281,4,56,12,Female,Yes,Yes,African American,868
132,10.726,1568,162,5,46,19,Male,No,Yes,Asian,0
133,23.283,5443,407,4,49,13,Male,No,Yes,African American,912
134,21.455,5829,427,4,80,12,Female,No,Yes,African American,1018
135,34.664,5835,452,3,77,15,Female,No,Yes,African American,835
136,44.473,3500,257,3,81,16,Female,No,No,African American,8
137,54.663,4116,314,2,70,8,Female,No,No,African American,75
138,36.355,3613,278,4,35,9,Male,No,Yes,Asian,187
139,21.374,2073,175,2,74,11,Female,No,Yes,Caucasian,0
140,107.841,10384,728,3,87,7,Male,No,No,African American,1597
141,39.831,6045,459,3,32,12,Female,Yes,Yes,African American,1425
142,91.876,6754,483,2,33,10,Male,No,Yes,Caucasian,605
143,103.893,7416,549,3,84,17,Male,No,No,Asian,669
144,19.636,4896,387,3,64,10,Female,No,No,African American,710
145,17.392,2748,228,3,32,14,Male,No,Yes,Caucasian,68
146,19.529,4673,341,2,51,14,Male,No,No,Asian,642
147,17.055,5110,371,3,55,15,Female,No,Yes,Caucasian,805
148,23.857,1501,150,3,56,16,Male,No,Yes,Caucasian,0
149,15.184,2420,192,2,69,11,Female,No,Yes,Caucasian,0
150,13.444,886,121,5,44,10,Male,No,Yes,Asian,0
151,63.931,5728,435,3,28,14,Female,No,Yes,African American,581
152,35.864,4831,353,3,66,13,Female,No,Yes,Caucasian,534
153,41.419,2120,184,4,24,11,Female,Yes,No,Caucasian,156
154,92.112,4612,344,3,32,17,Male,No,No,Caucasian,0
155,55.056,3155,235,2,31,16,Male,No,Yes,African American,0
156,19.537,1362,143,4,34,9,Female,No,Yes,Asian,0
157,31.811,4284,338,5,75,13,Female,No,Yes,Caucasian,429
158,56.256,5521,406,2,72,16,Female,Yes,Yes,Caucasian,1020
159,42.357,5550,406,2,83,12,Female,No,Yes,Asian,653
160,53.319,3000,235,3,53,13,Male,No,No,Asian,0
161,12.238,4865,381,5,67,11,Female,No,No,Caucasian,836
162,31.353,1705,160,3,81,14,Male,No,Yes,Caucasian,0
163,63.809,7530,515,1,56,12,Male,No,Yes,Caucasian,1086
164,13.676,2330,203,5,80,16,Female,No,No,African American,0
165,76.782,5977,429,4,44,12,Male,No,Yes,Asian,548
166,25.383,4527,367,4,46,11,Male,No,Yes,Caucasian,570
167,35.691,2880,214,2,35,15,Male,No,No,African American,0
168,29.403,2327,178,1,37,14,Female,No,Yes,Caucasian,0
169,27.47,2820,219,1,32,11,Female,No,Yes,Asian,0
170,27.33,6179,459,4,36,12,Female,No,Yes,Caucasian,1099
171,34.772,2021,167,3,57,9,Male,No,No,Asian,0
172,36.934,4270,299,1,63,9,Female,No,Yes,Caucasian,283
173,76.348,4697,344,4,60,18,Male,No,No,Asian,108
174,14.887,4745,339,3,58,12,Male,No,Yes,African American,724
175,121.834,10673,750,3,54,16,Male,No,No,African American,1573
176,30.132,2168,206,3,52,17,Male,No,No,Caucasian,0
177,24.05,2607,221,4,32,18,Male,No,Yes,Caucasian,0
178,22.379,3965,292,2,34,14,Female,No,Yes,Asian,384
179,28.316,4391,316,2,29,10,Female,No,No,Caucasian,453
180,58.026,7499,560,5,67,11,Female,No,No,Caucasian,1237
181,10.635,3584,294,5,69,16,Male,No,Yes,Asian,423
182,46.102,5180,382,3,81,12,Male,No,Yes,African American,516
183,58.929,6420,459,2,66,9,Female,No,Yes,African American,789
184,80.861,4090,335,3,29,15,Female,No,Yes,Asian,0
185,158.889,11589,805,1,62,17,Female,No,Yes,Caucasian,1448
186,30.42,4442,316,1,30,14,Female,No,No,African American,450
187,36.472,3806,309,2,52,13,Male,No,No,African American,188
188,23.365,2179,167,2,75,15,Male,No,No,Asian,0
189,83.869,7667,554,2,83,11,Male,No,No,African American,930
190,58.351,4411,326,2,85,16,Female,No,Yes,Caucasian,126
191,55.187,5352,385,4,50,17,Female,No,Yes,Caucasian,538
192,124.29,9560,701,3,52,17,Female,Yes,No,Asian,1687
193,28.508,3933,287,4,56,14,Male,No,Yes,Asian,336
194,130.209,10088,730,7,39,19,Female,No,Yes,Caucasian,1426
195,30.406,2120,181,2,79,14,Male,No,Yes,African American,0
196,23.883,5384,398,2,73,16,Female,No,Yes,African American,802
197,93.039,7398,517,1,67,12,Male,No,Yes,African American,749
198,50.699,3977,304,2,84,17,Female,No,No,African American,69
199,27.349,2000,169,4,51,16,Female,No,Yes,African American,0
200,10.403,4159,310,3,43,7,Male,No,Yes,Asian,571
201,23.949,5343,383,2,40,18,Male,No,Yes,African American,829
202,73.914,7333,529,6,67,15,Female,No,Yes,Caucasian,1048
203,21.038,1448,145,2,58,13,Female,No,Yes,Caucasian,0
204,68.206,6784,499,5,40,16,Female,Yes,No,African American,1411
205,57.337,5310,392,2,45,7,Female,No,No,Caucasian,456
206,10.793,3878,321,8,29,13,Male,No,No,Caucasian,638
207,23.45,2450,180,2,78,13,Male,No,No,Caucasian,0
208,10.842,4391,358,5,37,10,Female,Yes,Yes,Caucasian,1216
209,51.345,4327,320,3,46,15,Male,No,No,African American,230
210,151.947,9156,642,2,91,11,Female,No,Yes,African American,732
211,24.543,3206,243,2,62,12,Female,No,Yes,Caucasian,95
212,29.567,5309,397,3,25,15,Male,No,No,Caucasian,799
213,39.145,4351,323,2,66,13,Male,No,Yes,Caucasian,308
214,39.422,5245,383,2,44,19,Male,No,No,African American,637
215,34.909,5289,410,2,62,16,Female,No,Yes,Caucasian,681
216,41.025,4229,337,3,79,19,Female,No,Yes,Caucasian,246
217,15.476,2762,215,3,60,18,Male,No,No,Asian,52
218,12.456,5395,392,3,65,14,Male,No,Yes,Caucasian,955
219,10.627,1647,149,2,71,10,Female,Yes,Yes,Asian,195
220,38.954,5222,370,4,76,13,Female,No,No,Caucasian,653
221,44.847,5765,437,3,53,13,Female,Yes,No,Asian,1246
222,98.515,8760,633,5,78,11,Female,No,No,African American,1230
223,33.437,6207,451,4,44,9,Male,Yes,No,Caucasian,1549
224,27.512,4613,344,5,72,17,Male,No,Yes,Asian,573
225,121.709,7818,584,4,50,6,Male,No,Yes,Caucasian,701
226,15.079,5673,411,4,28,15,Female,No,Yes,Asian,1075
227,59.879,6906,527,6,78,15,Female,No,No,Caucasian,1032
228,66.989,5614,430,3,47,14,Female,No,Yes,Caucasian,482
229,69.165,4668,341,2,34,11,Female,No,No,African American,156
230,69.943,7555,547,3,76,9,Male,No,Yes,Asian,1058
231,33.214,5137,387,3,59,9,Male,No,No,African American,661
232,25.124,4776,378,4,29,12,Male,No,Yes,Caucasian,657
233,15.741,4788,360,1,39,14,Male,No,Yes,Asian,689
234,11.603,2278,187,3,71,11,Male,No,Yes,Caucasian,0
235,69.656,8244,579,3,41,14,Male,No,Yes,African American,1329
236,10.503,2923,232,3,25,18,Female,No,Yes,African American,191
237,42.529,4986,369,2,37,11,Male,No,Yes,Asian,489
238,60.579,5149,388,5,38,15,Male,No,Yes,Asian,443
239,26.532,2910,236,6,58,19,Female,No,Yes,Caucasian,52
240,27.952,3557,263,1,35,13,Female,No,Yes,Asian,163
241,29.705,3351,262,5,71,14,Female,No,Yes,Asian,148
242,15.602,906,103,2,36,11,Male,No,Yes,African American,0
243,20.918,1233,128,3,47,18,Female,Yes,Yes,Asian,16
244,58.165,6617,460,1,56,12,Female,No,Yes,Caucasian,856
245,22.561,1787,147,4,66,15,Female,No,No,Caucasian,0
246,34.509,2001,189,5,80,18,Female,No,Yes,African American,0
247,19.588,3211,265,4,59,14,Female,No,No,Asian,199
248,36.364,2220,188,3,50,19,Male,No,No,Caucasian,0
249,15.717,905,93,1,38,16,Male,Yes,Yes,Caucasian,0
250,22.574,1551,134,3,43,13,Female,Yes,Yes,Caucasian,98
251,10.363,2430,191,2,47,18,Female,No,Yes,Asian,0
252,28.474,3202,267,5,66,12,Male,No,Yes,Caucasian,132
253,72.945,8603,621,3,64,8,Female,No,No,Caucasian,1355
254,85.425,5182,402,6,60,12,Male,No,Yes,African American,218
255,36.508,6386,469,4,79,6,Female,No,Yes,Caucasian,1048
256,58.063,4221,304,3,50,8,Male,No,No,African American,118
257,25.936,1774,135,2,71,14,Female,No,No,Asian,0
258,15.629,2493,186,1,60,14,Male,No,Yes,Asian,0
259,41.4,2561,215,2,36,14,Male,No,Yes,Caucasian,0
260,33.657,6196,450,6,55,9,Female,No,No,Caucasian,1092
261,67.937,5184,383,4,63,12,Male,No,Yes,Asian,345
262,180.379,9310,665,3,67,8,Female,Yes,Yes,Asian,1050
263,10.588,4049,296,1,66,13,Female,No,Yes,Caucasian,465
264,29.725,3536,270,2,52,15,Female,No,No,African American,133
265,27.999,5107,380,1,55,10,Male,No,Yes,Caucasian,651
266,40.885,5013,379,3,46,13,Female,No,Yes,African American,549
267,88.83,4952,360,4,86,16,Female,No,Yes,Caucasian,15
268,29.638,5833,433,3,29,15,Female,No,Yes,Asian,942
269,25.988,1349,142,4,82,12,Male,No,No,Caucasian,0
270,39.055,5565,410,4,48,18,Female,No,Yes,Caucasian,772
271,15.866,3085,217,1,39,13,Male,No,No,Caucasian,136
272,44.978,4866,347,1,30,10,Female,No,No,Caucasian,436
273,30.413,3690,299,2,25,15,Female,Yes,No,Asian,728
274,16.751,4706,353,6,48,14,Male,Yes,No,Asian,1255
275,30.55,5869,439,5,81,9,Female,No,No,African American,967
276,163.329,8732,636,3,50,14,Male,No,Yes,Caucasian,529
277,23.106,3476,257,2,50,15,Female,No,No,Caucasian,209
278,41.532,5000,353,2,50,12,Male,No,Yes,Caucasian,531
279,128.04,6982,518,2,78,11,Female,No,Yes,Caucasian,250
280,54.319,3063,248,3,59,8,Female,Yes,No,Caucasian,269
281,53.401,5319,377,3,35,12,Female,No,No,African American,541
282,36.142,1852,183,3,33,13,Female,No,No,African American,0
283,63.534,8100,581,2,50,17,Female,No,Yes,Caucasian,1298
284,49.927,6396,485,3,75,17,Female,No,Yes,Caucasian,890
285,14.711,2047,167,2,67,6,Male,No,Yes,Caucasian,0
286,18.967,1626,156,2,41,11,Female,No,Yes,Asian,0
287,18.036,1552,142,2,48,15,Female,No,No,Caucasian,0
288,60.449,3098,272,4,69,8,Male,No,Yes,Caucasian,0
289,16.711,5274,387,3,42,16,Female,No,Yes,Asian,863
290,10.852,3907,296,2,30,9,Male,No,No,Caucasian,485
291,26.37,3235,268,5,78,11,Male,No,Yes,Asian,159
292,24.088,3665,287,4,56,13,Female,No,Yes,Caucasian,309
293,51.532,5096,380,2,31,15,Male,No,Yes,Caucasian,481
294,140.672,11200,817,7,46,9,Male,No,Yes,African American,1677
295,42.915,2532,205,4,42,13,Male,No,Yes,Asian,0
296,27.272,1389,149,5,67,10,Female,No,Yes,Caucasian,0
297,65.896,5140,370,1,49,17,Female,No,Yes,Caucasian,293
298,55.054,4381,321,3,74,17,Male,No,Yes,Asian,188
299,20.791,2672,204,1,70,18,Female,No,No,African American,0
300,24.919,5051,372,3,76,11,Female,No,Yes,African American,711
301,21.786,4632,355,1,50,17,Male,No,Yes,Caucasian,580
302,31.335,3526,289,3,38,7,Female,No,No,Caucasian,172
303,59.855,4964,365,1,46,13,Female,No,Yes,Caucasian,295
304,44.061,4970,352,1,79,11,Male,No,Yes,African American,414
305,82.706,7506,536,2,64,13,Female,No,Yes,Asian,905
306,24.46,1924,165,2,50,14,Female,No,Yes,Asian,0
307,45.12,3762,287,3,80,8,Male,No,Yes,Caucasian,70
308,75.406,3874,298,3,41,14,Female,No,Yes,Asian,0
309,14.956,4640,332,2,33,6,Male,No,No,Asian,681
310,75.257,7010,494,3,34,18,Female,No,Yes,Caucasian,885
311,33.694,4891,369,1,52,16,Male,Yes,No,African American,1036
312,23.375,5429,396,3,57,15,Female,No,Yes,Caucasian,844
313,27.825,5227,386,6,63,11,Male,No,Yes,Caucasian,823
314,92.386,7685,534,2,75,18,Female,No,Yes,Asian,843
315,115.52,9272,656,2,69,14,Male,No,No,African American,1140
316,14.479,3907,296,3,43,16,Male,No,Yes,Caucasian,463
317,52.179,7306,522,2,57,14,Male,No,No,Asian,1142
318,68.462,4712,340,2,71,16,Male,No,Yes,Caucasian,136
319,18.951,1485,129,3,82,13,Female,No,No,Caucasian,0
320,27.59,2586,229,5,54,16,Male,No,Yes,African American,0
321,16.279,1160,126,3,78,13,Male,Yes,Yes,African American,5
322,25.078,3096,236,2,27,15,Female,No,Yes,Caucasian,81
323,27.229,3484,282,6,51,11,Male,No,No,Caucasian,265
324,182.728,13913,982,4,98,17,Male,No,Yes,Caucasian,1999
325,31.029,2863,223,2,66,17,Male,Yes,Yes,Asian,415
326,17.765,5072,364,1,66,12,Female,No,Yes,Caucasian,732
327,125.48,10230,721,3,82,16,Male,No,Yes,Caucasian,1361
328,49.166,6662,508,3,68,14,Female,No,No,Asian,984
329,41.192,3673,297,3,54,16,Female,No,Yes,Caucasian,121
330,94.193,7576,527,2,44,16,Female,No,Yes,Caucasian,846
331,20.405,4543,329,2,72,17,Male,Yes,No,Asian,1054
332,12.581,3976,291,2,48,16,Male,No,Yes,Caucasian,474
333,62.328,5228,377,3,83,15,Male,No,No,Caucasian,380
334,21.011,3402,261,2,68,17,Male,No,Yes,African American,182
335,24.23,4756,351,2,64,15,Female,No,Yes,Caucasian,594
336,24.314,3409,270,2,23,7,Female,No,Yes,Caucasian,194
337,32.856,5884,438,4,68,13,Male,No,No,Caucasian,926
338,12.414,855,119,3,32,12,Male,No,Yes,African American,0
339,41.365,5303,377,1,45,14,Male,No,No,Caucasian,606
340,149.316,10278,707,1,80,16,Male,No,No,African American,1107
341,27.794,3807,301,4,35,8,Female,No,Yes,African American,320
342,13.234,3922,299,2,77,17,Female,No,Yes,Caucasian,426
343,14.595,2955,260,5,37,9,Male,No,Yes,African American,204
344,10.735,3746,280,2,44,17,Female,No,Yes,Caucasian,410
345,48.218,5199,401,7,39,10,Male,No,Yes,Asian,633
346,30.012,1511,137,2,33,17,Male,No,Yes,Caucasian,0
347,21.551,5380,420,5,51,18,Male,No,Yes,Asian,907
348,160.231,10748,754,2,69,17,Male,No,No,Caucasian,1192
349,13.433,1134,112,3,70,14,Male,No,Yes,Caucasian,0
350,48.577,5145,389,3,71,13,Female,No,Yes,Asian,503
351,30.002,1561,155,4,70,13,Female,No,Yes,Caucasian,0
352,61.62,5140,374,1,71,9,Male,No,Yes,Caucasian,302
353,104.483,7140,507,2,41,14,Male,No,Yes,African American,583
354,41.868,4716,342,2,47,18,Male,No,No,Caucasian,425
355,12.068,3873,292,1,44,18,Female,No,Yes,Asian,413
356,180.682,11966,832,2,58,8,Female,No,Yes,African American,1405
357,34.48,6090,442,3,36,14,Male,No,No,Caucasian,962
358,39.609,2539,188,1,40,14,Male,No,Yes,Asian,0
359,30.111,4336,339,1,81,18,Male,No,Yes,Caucasian,347
360,12.335,4471,344,3,79,12,Male,No,Yes,African American,611
361,53.566,5891,434,4,82,10,Female,No,No,Caucasian,712
362,53.217,4943,362,2,46,16,Female,No,Yes,Asian,382
363,26.162,5101,382,3,62,19,Female,No,No,African American,710
364,64.173,6127,433,1,80,10,Male,No,Yes,Caucasian,578
365,128.669,9824,685,3,67,16,Male,No,Yes,Asian,1243
366,113.772,6442,489,4,69,15,Male,Yes,Yes,Caucasian,790
367,61.069,7871,564,3,56,14,Male,No,Yes,Caucasian,1264
368,23.793,3615,263,2,70,14,Male,No,No,African American,216
369,89,5759,440,3,37,6,Female,No,No,Caucasian,345
370,71.682,8028,599,3,57,16,Male,No,Yes,Caucasian,1208
371,35.61,6135,466,4,40,12,Male,No,No,Caucasian,992
372,39.116,2150,173,4,75,15,Male,No,No,Caucasian,0
373,19.782,3782,293,2,46,16,Female,Yes,No,Caucasian,840
374,55.412,5354,383,2,37,16,Female,Yes,Yes,Caucasian,1003
375,29.4,4840,368,3,76,18,Female,No,Yes,Caucasian,588
376,20.974,5673,413,5,44,16,Female,No,Yes,Caucasian,1000
377,87.625,7167,515,2,46,10,Female,No,No,African American,767
378,28.144,1567,142,3,51,10,Male,No,Yes,Caucasian,0
379,19.349,4941,366,1,33,19,Male,No,Yes,Caucasian,717
380,53.308,2860,214,1,84,10,Male,No,Yes,Caucasian,0
381,115.123,7760,538,3,83,14,Female,No,No,African American,661
382,101.788,8029,574,2,84,11,Male,No,Yes,Caucasian,849
383,24.824,5495,409,1,33,9,Male,Yes,No,Caucasian,1352
384,14.292,3274,282,9,64,9,Male,No,Yes,Caucasian,382
385,20.088,1870,180,3,76,16,Male,No,No,African American,0
386,26.4,5640,398,3,58,15,Female,No,No,Asian,905
387,19.253,3683,287,4,57,10,Male,No,No,African American,371
388,16.529,1357,126,3,62,9,Male,No,No,Asian,0
389,37.878,6827,482,2,80,13,Female,No,No,Caucasian,1129
390,83.948,7100,503,2,44,18,Male,No,No,Caucasian,806
391,135.118,10578,747,3,81,15,Female,No,Yes,Asian,1393
392,73.327,6555,472,2,43,15,Female,No,No,Caucasian,721
393,25.974,2308,196,2,24,10,Male,No,No,Asian,0
394,17.316,1335,138,2,65,13,Male,No,No,African American,0
395,49.794,5758,410,4,40,8,Male,No,No,Caucasian,734
396,12.096,4100,307,3,32,13,Male,No,Yes,Caucasian,560
397,13.364,3838,296,5,65,17,Male,No,No,African American,480
398,57.872,4171,321,5,67,12,Female,No,Yes,Caucasian,138
399,37.728,2525,192,1,44,13,Male,No,Yes,Caucasian,0
400,18.701,5524,415,5,64,7,Female,No,No,Asian,966

“”,”mpg”,”cylinders”,”displacement”,”horsepower”,”weight”,”acceleration”,”year”,”origin”,”name”
“1”,18,8,307,130,3504,12,70,1,”chevrolet chevelle malibu”
“2”,15,8,350,165,3693,11.5,70,1,”buick skylark 320″
“3”,18,8,318,150,3436,11,70,1,”plymouth satellite”
“4”,16,8,304,150,3433,12,70,1,”amc rebel sst”
“5”,17,8,302,140,3449,10.5,70,1,”ford torino”
“6”,15,8,429,198,4341,10,70,1,”ford galaxie 500″
“7”,14,8,454,220,4354,9,70,1,”chevrolet impala”
“8”,14,8,440,215,4312,8.5,70,1,”plymouth fury iii”
“9”,14,8,455,225,4425,10,70,1,”pontiac catalina”
“10”,15,8,390,190,3850,8.5,70,1,”amc ambassador dpl”
“11”,15,8,383,170,3563,10,70,1,”dodge challenger se”
“12”,14,8,340,160,3609,8,70,1,”plymouth ‘cuda 340″
“13”,15,8,400,150,3761,9.5,70,1,”chevrolet monte carlo”
“14”,14,8,455,225,3086,10,70,1,”buick estate wagon (sw)”
“15”,24,4,113,95,2372,15,70,3,”toyota corona mark ii”
“16”,22,6,198,95,2833,15.5,70,1,”plymouth duster”
“17”,18,6,199,97,2774,15.5,70,1,”amc hornet”
“18”,21,6,200,85,2587,16,70,1,”ford maverick”
“19”,27,4,97,88,2130,14.5,70,3,”datsun pl510″
“20”,26,4,97,46,1835,20.5,70,2,”volkswagen 1131 deluxe sedan”
“21”,25,4,110,87,2672,17.5,70,2,”peugeot 504″
“22”,24,4,107,90,2430,14.5,70,2,”audi 100 ls”
“23”,25,4,104,95,2375,17.5,70,2,”saab 99e”
“24”,26,4,121,113,2234,12.5,70,2,”bmw 2002″
“25”,21,6,199,90,2648,15,70,1,”amc gremlin”
“26”,10,8,360,215,4615,14,70,1,”ford f250″
“27”,10,8,307,200,4376,15,70,1,”chevy c20″
“28”,11,8,318,210,4382,13.5,70,1,”dodge d200″
“29”,9,8,304,193,4732,18.5,70,1,”hi 1200d”
“30”,27,4,97,88,2130,14.5,71,3,”datsun pl510″
“31”,28,4,140,90,2264,15.5,71,1,”chevrolet vega 2300″
“32”,25,4,113,95,2228,14,71,3,”toyota corona”
“34”,19,6,232,100,2634,13,71,1,”amc gremlin”
“35”,16,6,225,105,3439,15.5,71,1,”plymouth satellite custom”
“36”,17,6,250,100,3329,15.5,71,1,”chevrolet chevelle malibu”
“37”,19,6,250,88,3302,15.5,71,1,”ford torino 500″
“38”,18,6,232,100,3288,15.5,71,1,”amc matador”
“39”,14,8,350,165,4209,12,71,1,”chevrolet impala”
“40”,14,8,400,175,4464,11.5,71,1,”pontiac catalina brougham”
“41”,14,8,351,153,4154,13.5,71,1,”ford galaxie 500″
“42”,14,8,318,150,4096,13,71,1,”plymouth fury iii”
“43”,12,8,383,180,4955,11.5,71,1,”dodge monaco (sw)”
“44”,13,8,400,170,4746,12,71,1,”ford country squire (sw)”
“45”,13,8,400,175,5140,12,71,1,”pontiac safari (sw)”
“46”,18,6,258,110,2962,13.5,71,1,”amc hornet sportabout (sw)”
“47”,22,4,140,72,2408,19,71,1,”chevrolet vega (sw)”
“48”,19,6,250,100,3282,15,71,1,”pontiac firebird”
“49”,18,6,250,88,3139,14.5,71,1,”ford mustang”
“50”,23,4,122,86,2220,14,71,1,”mercury capri 2000″
“51”,28,4,116,90,2123,14,71,2,”opel 1900″
“52”,30,4,79,70,2074,19.5,71,2,”peugeot 304″
“53”,30,4,88,76,2065,14.5,71,2,”fiat 124b”
“54”,31,4,71,65,1773,19,71,3,”toyota corolla 1200″
“55”,35,4,72,69,1613,18,71,3,”datsun 1200″
“56”,27,4,97,60,1834,19,71,2,”volkswagen model 111″
“57”,26,4,91,70,1955,20.5,71,1,”plymouth cricket”
“58”,24,4,113,95,2278,15.5,72,3,”toyota corona hardtop”
“59”,25,4,97.5,80,2126,17,72,1,”dodge colt hardtop”
“60”,23,4,97,54,2254,23.5,72,2,”volkswagen type 3″
“61”,20,4,140,90,2408,19.5,72,1,”chevrolet vega”
“62”,21,4,122,86,2226,16.5,72,1,”ford pinto runabout”
“63”,13,8,350,165,4274,12,72,1,”chevrolet impala”
“64”,14,8,400,175,4385,12,72,1,”pontiac catalina”
“65”,15,8,318,150,4135,13.5,72,1,”plymouth fury iii”
“66”,14,8,351,153,4129,13,72,1,”ford galaxie 500″
“67”,17,8,304,150,3672,11.5,72,1,”amc ambassador sst”
“68”,11,8,429,208,4633,11,72,1,”mercury marquis”
“69”,13,8,350,155,4502,13.5,72,1,”buick lesabre custom”
“70”,12,8,350,160,4456,13.5,72,1,”oldsmobile delta 88 royale”
“71”,13,8,400,190,4422,12.5,72,1,”chrysler newport royal”
“72”,19,3,70,97,2330,13.5,72,3,”mazda rx2 coupe”
“73”,15,8,304,150,3892,12.5,72,1,”amc matador (sw)”
“74”,13,8,307,130,4098,14,72,1,”chevrolet chevelle concours (sw)”
“75”,13,8,302,140,4294,16,72,1,”ford gran torino (sw)”
“76”,14,8,318,150,4077,14,72,1,”plymouth satellite custom (sw)”
“77”,18,4,121,112,2933,14.5,72,2,”volvo 145e (sw)”
“78”,22,4,121,76,2511,18,72,2,”volkswagen 411 (sw)”
“79”,21,4,120,87,2979,19.5,72,2,”peugeot 504 (sw)”
“80”,26,4,96,69,2189,18,72,2,”renault 12 (sw)”
“81”,22,4,122,86,2395,16,72,1,”ford pinto (sw)”
“82”,28,4,97,92,2288,17,72,3,”datsun 510 (sw)”
“83”,23,4,120,97,2506,14.5,72,3,”toyouta corona mark ii (sw)”
“84”,28,4,98,80,2164,15,72,1,”dodge colt (sw)”
“85”,27,4,97,88,2100,16.5,72,3,”toyota corolla 1600 (sw)”
“86”,13,8,350,175,4100,13,73,1,”buick century 350″
“87”,14,8,304,150,3672,11.5,73,1,”amc matador”
“88”,13,8,350,145,3988,13,73,1,”chevrolet malibu”
“89”,14,8,302,137,4042,14.5,73,1,”ford gran torino”
“90”,15,8,318,150,3777,12.5,73,1,”dodge coronet custom”
“91”,12,8,429,198,4952,11.5,73,1,”mercury marquis brougham”
“92”,13,8,400,150,4464,12,73,1,”chevrolet caprice classic”
“93”,13,8,351,158,4363,13,73,1,”ford ltd”
“94”,14,8,318,150,4237,14.5,73,1,”plymouth fury gran sedan”
“95”,13,8,440,215,4735,11,73,1,”chrysler new yorker brougham”
“96”,12,8,455,225,4951,11,73,1,”buick electra 225 custom”
“97”,13,8,360,175,3821,11,73,1,”amc ambassador brougham”
“98”,18,6,225,105,3121,16.5,73,1,”plymouth valiant”
“99”,16,6,250,100,3278,18,73,1,”chevrolet nova custom”
“100”,18,6,232,100,2945,16,73,1,”amc hornet”
“101”,18,6,250,88,3021,16.5,73,1,”ford maverick”
“102”,23,6,198,95,2904,16,73,1,”plymouth duster”
“103”,26,4,97,46,1950,21,73,2,”volkswagen super beetle”
“104”,11,8,400,150,4997,14,73,1,”chevrolet impala”
“105”,12,8,400,167,4906,12.5,73,1,”ford country”
“106”,13,8,360,170,4654,13,73,1,”plymouth custom suburb”
“107”,12,8,350,180,4499,12.5,73,1,”oldsmobile vista cruiser”
“108”,18,6,232,100,2789,15,73,1,”amc gremlin”
“109”,20,4,97,88,2279,19,73,3,”toyota carina”
“110”,21,4,140,72,2401,19.5,73,1,”chevrolet vega”
“111”,22,4,108,94,2379,16.5,73,3,”datsun 610″
“112”,18,3,70,90,2124,13.5,73,3,”maxda rx3″
“113”,19,4,122,85,2310,18.5,73,1,”ford pinto”
“114”,21,6,155,107,2472,14,73,1,”mercury capri v6″
“115”,26,4,98,90,2265,15.5,73,2,”fiat 124 sport coupe”
“116”,15,8,350,145,4082,13,73,1,”chevrolet monte carlo s”
“117”,16,8,400,230,4278,9.5,73,1,”pontiac grand prix”
“118”,29,4,68,49,1867,19.5,73,2,”fiat 128″
“119”,24,4,116,75,2158,15.5,73,2,”opel manta”
“120”,20,4,114,91,2582,14,73,2,”audi 100ls”
“121”,19,4,121,112,2868,15.5,73,2,”volvo 144ea”
“122”,15,8,318,150,3399,11,73,1,”dodge dart custom”
“123”,24,4,121,110,2660,14,73,2,”saab 99le”
“124”,20,6,156,122,2807,13.5,73,3,”toyota mark ii”
“125”,11,8,350,180,3664,11,73,1,”oldsmobile omega”
“126”,20,6,198,95,3102,16.5,74,1,”plymouth duster”
“128”,19,6,232,100,2901,16,74,1,”amc hornet”
“129”,15,6,250,100,3336,17,74,1,”chevrolet nova”
“130”,31,4,79,67,1950,19,74,3,”datsun b210″
“131”,26,4,122,80,2451,16.5,74,1,”ford pinto”
“132”,32,4,71,65,1836,21,74,3,”toyota corolla 1200″
“133”,25,4,140,75,2542,17,74,1,”chevrolet vega”
“134”,16,6,250,100,3781,17,74,1,”chevrolet chevelle malibu classic”
“135”,16,6,258,110,3632,18,74,1,”amc matador”
“136”,18,6,225,105,3613,16.5,74,1,”plymouth satellite sebring”
“137”,16,8,302,140,4141,14,74,1,”ford gran torino”
“138”,13,8,350,150,4699,14.5,74,1,”buick century luxus (sw)”
“139”,14,8,318,150,4457,13.5,74,1,”dodge coronet custom (sw)”
“140”,14,8,302,140,4638,16,74,1,”ford gran torino (sw)”
“141”,14,8,304,150,4257,15.5,74,1,”amc matador (sw)”
“142”,29,4,98,83,2219,16.5,74,2,”audi fox”
“143”,26,4,79,67,1963,15.5,74,2,”volkswagen dasher”
“144”,26,4,97,78,2300,14.5,74,2,”opel manta”
“145”,31,4,76,52,1649,16.5,74,3,”toyota corona”
“146”,32,4,83,61,2003,19,74,3,”datsun 710″
“147”,28,4,90,75,2125,14.5,74,1,”dodge colt”
“148”,24,4,90,75,2108,15.5,74,2,”fiat 128″
“149”,26,4,116,75,2246,14,74,2,”fiat 124 tc”
“150”,24,4,120,97,2489,15,74,3,”honda civic”
“151”,26,4,108,93,2391,15.5,74,3,”subaru”
“152”,31,4,79,67,2000,16,74,2,”fiat x1.9″
“153”,19,6,225,95,3264,16,75,1,”plymouth valiant custom”
“154”,18,6,250,105,3459,16,75,1,”chevrolet nova”
“155”,15,6,250,72,3432,21,75,1,”mercury monarch”
“156”,15,6,250,72,3158,19.5,75,1,”ford maverick”
“157”,16,8,400,170,4668,11.5,75,1,”pontiac catalina”
“158”,15,8,350,145,4440,14,75,1,”chevrolet bel air”
“159”,16,8,318,150,4498,14.5,75,1,”plymouth grand fury”
“160”,14,8,351,148,4657,13.5,75,1,”ford ltd”
“161”,17,6,231,110,3907,21,75,1,”buick century”
“162”,16,6,250,105,3897,18.5,75,1,”chevroelt chevelle malibu”
“163”,15,6,258,110,3730,19,75,1,”amc matador”
“164”,18,6,225,95,3785,19,75,1,”plymouth fury”
“165”,21,6,231,110,3039,15,75,1,”buick skyhawk”
“166”,20,8,262,110,3221,13.5,75,1,”chevrolet monza 2+2″
“167”,13,8,302,129,3169,12,75,1,”ford mustang ii”
“168”,29,4,97,75,2171,16,75,3,”toyota corolla”
“169”,23,4,140,83,2639,17,75,1,”ford pinto”
“170”,20,6,232,100,2914,16,75,1,”amc gremlin”
“171”,23,4,140,78,2592,18.5,75,1,”pontiac astro”
“172”,24,4,134,96,2702,13.5,75,3,”toyota corona”
“173”,25,4,90,71,2223,16.5,75,2,”volkswagen dasher”
“174”,24,4,119,97,2545,17,75,3,”datsun 710″
“175”,18,6,171,97,2984,14.5,75,1,”ford pinto”
“176”,29,4,90,70,1937,14,75,2,”volkswagen rabbit”
“177”,19,6,232,90,3211,17,75,1,”amc pacer”
“178”,23,4,115,95,2694,15,75,2,”audi 100ls”
“179”,23,4,120,88,2957,17,75,2,”peugeot 504″
“180”,22,4,121,98,2945,14.5,75,2,”volvo 244dl”
“181”,25,4,121,115,2671,13.5,75,2,”saab 99le”
“182”,33,4,91,53,1795,17.5,75,3,”honda civic cvcc”
“183”,28,4,107,86,2464,15.5,76,2,”fiat 131″
“184”,25,4,116,81,2220,16.9,76,2,”opel 1900″
“185”,25,4,140,92,2572,14.9,76,1,”capri ii”
“186”,26,4,98,79,2255,17.7,76,1,”dodge colt”
“187”,27,4,101,83,2202,15.3,76,2,”renault 12tl”
“188”,17.5,8,305,140,4215,13,76,1,”chevrolet chevelle malibu classic”
“189”,16,8,318,150,4190,13,76,1,”dodge coronet brougham”
“190”,15.5,8,304,120,3962,13.9,76,1,”amc matador”
“191”,14.5,8,351,152,4215,12.8,76,1,”ford gran torino”
“192”,22,6,225,100,3233,15.4,76,1,”plymouth valiant”
“193”,22,6,250,105,3353,14.5,76,1,”chevrolet nova”
“194”,24,6,200,81,3012,17.6,76,1,”ford maverick”
“195”,22.5,6,232,90,3085,17.6,76,1,”amc hornet”
“196”,29,4,85,52,2035,22.2,76,1,”chevrolet chevette”
“197”,24.5,4,98,60,2164,22.1,76,1,”chevrolet woody”
“198”,29,4,90,70,1937,14.2,76,2,”vw rabbit”
“199”,33,4,91,53,1795,17.4,76,3,”honda civic”
“200”,20,6,225,100,3651,17.7,76,1,”dodge aspen se”
“201”,18,6,250,78,3574,21,76,1,”ford granada ghia”
“202”,18.5,6,250,110,3645,16.2,76,1,”pontiac ventura sj”
“203”,17.5,6,258,95,3193,17.8,76,1,”amc pacer d/l”
“204”,29.5,4,97,71,1825,12.2,76,2,”volkswagen rabbit”
“205”,32,4,85,70,1990,17,76,3,”datsun b-210″
“206”,28,4,97,75,2155,16.4,76,3,”toyota corolla”
“207”,26.5,4,140,72,2565,13.6,76,1,”ford pinto”
“208”,20,4,130,102,3150,15.7,76,2,”volvo 245″
“209”,13,8,318,150,3940,13.2,76,1,”plymouth volare premier v8″
“210”,19,4,120,88,3270,21.9,76,2,”peugeot 504″
“211”,19,6,156,108,2930,15.5,76,3,”toyota mark ii”
“212”,16.5,6,168,120,3820,16.7,76,2,”mercedes-benz 280s”
“213”,16.5,8,350,180,4380,12.1,76,1,”cadillac seville”
“214”,13,8,350,145,4055,12,76,1,”chevy c10″
“215”,13,8,302,130,3870,15,76,1,”ford f108″
“216”,13,8,318,150,3755,14,76,1,”dodge d100″
“217”,31.5,4,98,68,2045,18.5,77,3,”honda accord cvcc”
“218”,30,4,111,80,2155,14.8,77,1,”buick opel isuzu deluxe”
“219”,36,4,79,58,1825,18.6,77,2,”renault 5 gtl”
“220”,25.5,4,122,96,2300,15.5,77,1,”plymouth arrow gs”
“221”,33.5,4,85,70,1945,16.8,77,3,”datsun f-10 hatchback”
“222”,17.5,8,305,145,3880,12.5,77,1,”chevrolet caprice classic”
“223”,17,8,260,110,4060,19,77,1,”oldsmobile cutlass supreme”
“224”,15.5,8,318,145,4140,13.7,77,1,”dodge monaco brougham”
“225”,15,8,302,130,4295,14.9,77,1,”mercury cougar brougham”
“226”,17.5,6,250,110,3520,16.4,77,1,”chevrolet concours”
“227”,20.5,6,231,105,3425,16.9,77,1,”buick skylark”
“228”,19,6,225,100,3630,17.7,77,1,”plymouth volare custom”
“229”,18.5,6,250,98,3525,19,77,1,”ford granada”
“230”,16,8,400,180,4220,11.1,77,1,”pontiac grand prix lj”
“231”,15.5,8,350,170,4165,11.4,77,1,”chevrolet monte carlo landau”
“232”,15.5,8,400,190,4325,12.2,77,1,”chrysler cordoba”
“233”,16,8,351,149,4335,14.5,77,1,”ford thunderbird”
“234”,29,4,97,78,1940,14.5,77,2,”volkswagen rabbit custom”
“235”,24.5,4,151,88,2740,16,77,1,”pontiac sunbird coupe”
“236”,26,4,97,75,2265,18.2,77,3,”toyota corolla liftback”
“237”,25.5,4,140,89,2755,15.8,77,1,”ford mustang ii 2+2″
“238”,30.5,4,98,63,2051,17,77,1,”chevrolet chevette”
“239”,33.5,4,98,83,2075,15.9,77,1,”dodge colt m/m”
“240”,30,4,97,67,1985,16.4,77,3,”subaru dl”
“241”,30.5,4,97,78,2190,14.1,77,2,”volkswagen dasher”
“242”,22,6,146,97,2815,14.5,77,3,”datsun 810″
“243”,21.5,4,121,110,2600,12.8,77,2,”bmw 320i”
“244”,21.5,3,80,110,2720,13.5,77,3,”mazda rx-4″
“245”,43.1,4,90,48,1985,21.5,78,2,”volkswagen rabbit custom diesel”
“246”,36.1,4,98,66,1800,14.4,78,1,”ford fiesta”
“247”,32.8,4,78,52,1985,19.4,78,3,”mazda glc deluxe”
“248”,39.4,4,85,70,2070,18.6,78,3,”datsun b210 gx”
“249”,36.1,4,91,60,1800,16.4,78,3,”honda civic cvcc”
“250”,19.9,8,260,110,3365,15.5,78,1,”oldsmobile cutlass salon brougham”
“251”,19.4,8,318,140,3735,13.2,78,1,”dodge diplomat”
“252”,20.2,8,302,139,3570,12.8,78,1,”mercury monarch ghia”
“253”,19.2,6,231,105,3535,19.2,78,1,”pontiac phoenix lj”
“254”,20.5,6,200,95,3155,18.2,78,1,”chevrolet malibu”
“255”,20.2,6,200,85,2965,15.8,78,1,”ford fairmont (auto)”
“256”,25.1,4,140,88,2720,15.4,78,1,”ford fairmont (man)”
“257”,20.5,6,225,100,3430,17.2,78,1,”plymouth volare”
“258”,19.4,6,232,90,3210,17.2,78,1,”amc concord”
“259”,20.6,6,231,105,3380,15.8,78,1,”buick century special”
“260”,20.8,6,200,85,3070,16.7,78,1,”mercury zephyr”
“261”,18.6,6,225,110,3620,18.7,78,1,”dodge aspen”
“262”,18.1,6,258,120,3410,15.1,78,1,”amc concord d/l”
“263”,19.2,8,305,145,3425,13.2,78,1,”chevrolet monte carlo landau”
“264”,17.7,6,231,165,3445,13.4,78,1,”buick regal sport coupe (turbo)”
“265”,18.1,8,302,139,3205,11.2,78,1,”ford futura”
“266”,17.5,8,318,140,4080,13.7,78,1,”dodge magnum xe”
“267”,30,4,98,68,2155,16.5,78,1,”chevrolet chevette”
“268”,27.5,4,134,95,2560,14.2,78,3,”toyota corona”
“269”,27.2,4,119,97,2300,14.7,78,3,”datsun 510″
“270”,30.9,4,105,75,2230,14.5,78,1,”dodge omni”
“271”,21.1,4,134,95,2515,14.8,78,3,”toyota celica gt liftback”
“272”,23.2,4,156,105,2745,16.7,78,1,”plymouth sapporo”
“273”,23.8,4,151,85,2855,17.6,78,1,”oldsmobile starfire sx”
“274”,23.9,4,119,97,2405,14.9,78,3,”datsun 200-sx”
“275”,20.3,5,131,103,2830,15.9,78,2,”audi 5000″
“276”,17,6,163,125,3140,13.6,78,2,”volvo 264gl”
“277”,21.6,4,121,115,2795,15.7,78,2,”saab 99gle”
“278”,16.2,6,163,133,3410,15.8,78,2,”peugeot 604sl”
“279”,31.5,4,89,71,1990,14.9,78,2,”volkswagen scirocco”
“280”,29.5,4,98,68,2135,16.6,78,3,”honda accord lx”
“281”,21.5,6,231,115,3245,15.4,79,1,”pontiac lemans v6″
“282”,19.8,6,200,85,2990,18.2,79,1,”mercury zephyr 6″
“283”,22.3,4,140,88,2890,17.3,79,1,”ford fairmont 4″
“284”,20.2,6,232,90,3265,18.2,79,1,”amc concord dl 6″
“285”,20.6,6,225,110,3360,16.6,79,1,”dodge aspen 6″
“286”,17,8,305,130,3840,15.4,79,1,”chevrolet caprice classic”
“287”,17.6,8,302,129,3725,13.4,79,1,”ford ltd landau”
“288”,16.5,8,351,138,3955,13.2,79,1,”mercury grand marquis”
“289”,18.2,8,318,135,3830,15.2,79,1,”dodge st. regis”
“290”,16.9,8,350,155,4360,14.9,79,1,”buick estate wagon (sw)”
“291”,15.5,8,351,142,4054,14.3,79,1,”ford country squire (sw)”
“292”,19.2,8,267,125,3605,15,79,1,”chevrolet malibu classic (sw)”
“293”,18.5,8,360,150,3940,13,79,1,”chrysler lebaron town @ country (sw)”
“294”,31.9,4,89,71,1925,14,79,2,”vw rabbit custom”
“295”,34.1,4,86,65,1975,15.2,79,3,”maxda glc deluxe”
“296”,35.7,4,98,80,1915,14.4,79,1,”dodge colt hatchback custom”
“297”,27.4,4,121,80,2670,15,79,1,”amc spirit dl”
“298”,25.4,5,183,77,3530,20.1,79,2,”mercedes benz 300d”
“299”,23,8,350,125,3900,17.4,79,1,”cadillac eldorado”
“300”,27.2,4,141,71,3190,24.8,79,2,”peugeot 504″
“301”,23.9,8,260,90,3420,22.2,79,1,”oldsmobile cutlass salon brougham”
“302”,34.2,4,105,70,2200,13.2,79,1,”plymouth horizon”
“303”,34.5,4,105,70,2150,14.9,79,1,”plymouth horizon tc3″
“304”,31.8,4,85,65,2020,19.2,79,3,”datsun 210″
“305”,37.3,4,91,69,2130,14.7,79,2,”fiat strada custom”
“306”,28.4,4,151,90,2670,16,79,1,”buick skylark limited”
“307”,28.8,6,173,115,2595,11.3,79,1,”chevrolet citation”
“308”,26.8,6,173,115,2700,12.9,79,1,”oldsmobile omega brougham”
“309”,33.5,4,151,90,2556,13.2,79,1,”pontiac phoenix”
“310”,41.5,4,98,76,2144,14.7,80,2,”vw rabbit”
“311”,38.1,4,89,60,1968,18.8,80,3,”toyota corolla tercel”
“312”,32.1,4,98,70,2120,15.5,80,1,”chevrolet chevette”
“313”,37.2,4,86,65,2019,16.4,80,3,”datsun 310″
“314”,28,4,151,90,2678,16.5,80,1,”chevrolet citation”
“315”,26.4,4,140,88,2870,18.1,80,1,”ford fairmont”
“316”,24.3,4,151,90,3003,20.1,80,1,”amc concord”
“317”,19.1,6,225,90,3381,18.7,80,1,”dodge aspen”
“318”,34.3,4,97,78,2188,15.8,80,2,”audi 4000″
“319”,29.8,4,134,90,2711,15.5,80,3,”toyota corona liftback”
“320”,31.3,4,120,75,2542,17.5,80,3,”mazda 626″
“321”,37,4,119,92,2434,15,80,3,”datsun 510 hatchback”
“322”,32.2,4,108,75,2265,15.2,80,3,”toyota corolla”
“323”,46.6,4,86,65,2110,17.9,80,3,”mazda glc”
“324”,27.9,4,156,105,2800,14.4,80,1,”dodge colt”
“325”,40.8,4,85,65,2110,19.2,80,3,”datsun 210″
“326”,44.3,4,90,48,2085,21.7,80,2,”vw rabbit c (diesel)”
“327”,43.4,4,90,48,2335,23.7,80,2,”vw dasher (diesel)”
“328”,36.4,5,121,67,2950,19.9,80,2,”audi 5000s (diesel)”
“329”,30,4,146,67,3250,21.8,80,2,”mercedes-benz 240d”
“330”,44.6,4,91,67,1850,13.8,80,3,”honda civic 1500 gl”
“332”,33.8,4,97,67,2145,18,80,3,”subaru dl”
“333”,29.8,4,89,62,1845,15.3,80,2,”vokswagen rabbit”
“334”,32.7,6,168,132,2910,11.4,80,3,”datsun 280-zx”
“335”,23.7,3,70,100,2420,12.5,80,3,”mazda rx-7 gs”
“336”,35,4,122,88,2500,15.1,80,2,”triumph tr7 coupe”
“338”,32.4,4,107,72,2290,17,80,3,”honda accord”
“339”,27.2,4,135,84,2490,15.7,81,1,”plymouth reliant”
“340”,26.6,4,151,84,2635,16.4,81,1,”buick skylark”
“341”,25.8,4,156,92,2620,14.4,81,1,”dodge aries wagon (sw)”
“342”,23.5,6,173,110,2725,12.6,81,1,”chevrolet citation”
“343”,30,4,135,84,2385,12.9,81,1,”plymouth reliant”
“344”,39.1,4,79,58,1755,16.9,81,3,”toyota starlet”
“345”,39,4,86,64,1875,16.4,81,1,”plymouth champ”
“346”,35.1,4,81,60,1760,16.1,81,3,”honda civic 1300″
“347”,32.3,4,97,67,2065,17.8,81,3,”subaru”
“348”,37,4,85,65,1975,19.4,81,3,”datsun 210 mpg”
“349”,37.7,4,89,62,2050,17.3,81,3,”toyota tercel”
“350”,34.1,4,91,68,1985,16,81,3,”mazda glc 4″
“351”,34.7,4,105,63,2215,14.9,81,1,”plymouth horizon 4″
“352”,34.4,4,98,65,2045,16.2,81,1,”ford escort 4w”
“353”,29.9,4,98,65,2380,20.7,81,1,”ford escort 2h”
“354”,33,4,105,74,2190,14.2,81,2,”volkswagen jetta”
“356”,33.7,4,107,75,2210,14.4,81,3,”honda prelude”
“357”,32.4,4,108,75,2350,16.8,81,3,”toyota corolla”
“358”,32.9,4,119,100,2615,14.8,81,3,”datsun 200sx”
“359”,31.6,4,120,74,2635,18.3,81,3,”mazda 626″
“360”,28.1,4,141,80,3230,20.4,81,2,”peugeot 505s turbo diesel”
“361”,30.7,6,145,76,3160,19.6,81,2,”volvo diesel”
“362”,25.4,6,168,116,2900,12.6,81,3,”toyota cressida”
“363”,24.2,6,146,120,2930,13.8,81,3,”datsun 810 maxima”
“364”,22.4,6,231,110,3415,15.8,81,1,”buick century”
“365”,26.6,8,350,105,3725,19,81,1,”oldsmobile cutlass ls”
“366”,20.2,6,200,88,3060,17.1,81,1,”ford granada gl”
“367”,17.6,6,225,85,3465,16.6,81,1,”chrysler lebaron salon”
“368”,28,4,112,88,2605,19.6,82,1,”chevrolet cavalier”
“369”,27,4,112,88,2640,18.6,82,1,”chevrolet cavalier wagon”
“370”,34,4,112,88,2395,18,82,1,”chevrolet cavalier 2-door”
“371”,31,4,112,85,2575,16.2,82,1,”pontiac j2000 se hatchback”
“372”,29,4,135,84,2525,16,82,1,”dodge aries se”
“373”,27,4,151,90,2735,18,82,1,”pontiac phoenix”
“374”,24,4,140,92,2865,16.4,82,1,”ford fairmont futura”
“375”,36,4,105,74,1980,15.3,82,2,”volkswagen rabbit l”
“376”,37,4,91,68,2025,18.2,82,3,”mazda glc custom l”
“377”,31,4,91,68,1970,17.6,82,3,”mazda glc custom”
“378”,38,4,105,63,2125,14.7,82,1,”plymouth horizon miser”
“379”,36,4,98,70,2125,17.3,82,1,”mercury lynx l”
“380”,36,4,120,88,2160,14.5,82,3,”nissan stanza xe”
“381”,36,4,107,75,2205,14.5,82,3,”honda accord”
“382”,34,4,108,70,2245,16.9,82,3,”toyota corolla”
“383”,38,4,91,67,1965,15,82,3,”honda civic”
“384”,32,4,91,67,1965,15.7,82,3,”honda civic (auto)”
“385”,38,4,91,67,1995,16.2,82,3,”datsun 310 gx”
“386”,25,6,181,110,2945,16.4,82,1,”buick century limited”
“387”,38,6,262,85,3015,17,82,1,”oldsmobile cutlass ciera (diesel)”
“388”,26,4,156,92,2585,14.5,82,1,”chrysler lebaron medallion”
“389”,22,6,232,112,2835,14.7,82,1,”ford granada l”
“390”,32,4,144,96,2665,13.9,82,3,”toyota celica gt”
“391”,36,4,135,84,2370,13,82,1,”dodge charger 2.2″
“392”,27,4,151,90,2950,17.3,82,1,”chevrolet camaro”
“393”,27,4,140,86,2790,15.6,82,1,”ford mustang gl”
“394”,44,4,97,52,2130,24.6,82,2,”vw pickup”
“395”,32,4,135,84,2295,11.6,82,1,”dodge rampage”
“396”,28,4,120,79,2625,18.6,82,1,”ford ranger”
“397”,31,4,119,82,2720,19.4,82,1,”chevy s-10″