**Project:**

** **

This project is made up of 4 different parts. The dataset for all 4 parts is given in the table below. Students are encouraged to use software (Minitab, excel etc.) to complete the project. For more help see your professor.

CARS: A sample of 20 cars, including measurements of fuel consumption (city mi/gal and highway mi/gal), weight (pounds), number of cylinders, engine displacement (in liters), amount of greenhouse gases emitted (in tons/year), and amount of tailpipe emissions of NO_{x} (in lb/yr).

CAR |
CITY |
HWY |
WEIGHT |
CYLINDERS |
DISPLACEMENT |
MAN/AUTO |
GHG |
NOX |

Chev. Camaro | 19 | 30 | 3545 | 6 | 3.8 | M | 12 | 34.4 |

Chev. Cavalier | 23 | 31 | 2795 | 4 | 2.2 | A | 10 | 25.1 |

Dodge Neon | 23 | 32 | 2600 | 4 | 2 | A | 10 | 25.1 |

Ford Taurus | 19 | 27 | 3515 | 6 | 3 | A | 12 | 25.1 |

Honda Accord | 23 | 30 | 3245 | 4 | 2.3 | A | 11 | 25.1 |

Lincoln Cont. | 17 | 24 | 3930 | 8 | 4.6 | A | 14 | 25.1 |

Mercury Mystique | 20 | 29 | 3115 | 6 | 2.5 | A | 12 | 34.4 |

Mitsubishi Eclipse | 22 | 33 | 3235 | 4 | 2 | M | 10 | 25.1 |

Olds. Aurora | 17 | 26 | 3995 | 8 | 4 | A | 13 | 34.4 |

Pontiac Grand Am | 22 | 30 | 3115 | 4 | 2.4 | A | 11 | 25.1 |

Toyota Camry | 23 | 32 | 3240 | 4 | 2.2 | M | 10 | 25.1 |

Cadillac DeVille | 17 | 26 | 4020 | 8 | 4.6 | A | 13 | 34.4 |

Chev. Corvette | 18 | 28 | 3220 | 8 | 5.7 | M | 12 | 34.4 |

Chrysler Sebring | 19 | 27 | 3175 | 6 | 2.5 | A | 12 | 25.1 |

Ford Mustang | 20 | 29 | 3450 | 6 | 3.8 | M | 12 | 34.4 |

BMW 3-Series | 19 | 27 | 3225 | 6 | 2.8 | A | 12 | 34.4 |

Ford Crown Victoria | 17 | 24 | 3985 | 8 | 4.6 | A | 14 | 25.1 |

Honda Civic | 32 | 37 | 2440 | 4 | 1.6 | M | 8 | 25.1 |

Mazda Protege | 29 | 34 | 2500 | 4 | 1.6 | A | 9 | 25.1 |

Hyundai Accent | 28 | 37 | 2290 | 4 | 1.5 | A | 9 | 34.4 |

**Part I**

Generally the first step to analyze a dataset that is given to you is to identify the type of data, and picture the data using graphs etc. Use the data set above to answer the following questions:

- Assume that the
**car**column represents all car models. Use the random number Table B, to generate a simple random sample of 15 car models from the set above.

- Classify all the columns: car, city, HWY, weight, cylinders, displacement man/auto GHG NOX according to the following:
- Categorical or quantitative
- Discrete or continuous or none

- Levels of measurement: nominal, ordinal, interval or ratio.

- Make a frequency distribution for MAN/AUTO
- Make a frequency distribution for DISPLACEMENT. (also include the column for cumulative frequency)
- Make a bar graph or pie chart for MAN/AUTO.
- Make a histogram for DISPLACEMENT.
- Determine the type of skewness- left, right, symmetric or none.
- Determine the variability-high or low.
- Make a stemplot for CITY
- Determine the type of skewness- left, right, symmetric or none.
- Determine the variability-high or low or none.

- Make a dotplot for CYLINDER.
- Determine the type of skewness- left, right , symmetric or none.
- Determine the variability-high or low, or none.

**Part II**

To summarize a dataset sometimes we have to find the measures of center or variation. Sometime we have to compute quartiles and make boxplots. Summarize the datasets given in the table above by answering the following questions:

- Find the measures of center for the column NOX (i.e. the mean, mode, median, midrange).
- Use the measures of center (mean, median, mode) to determine the type of skewness (to the right, to the left, or symmetric) in NOX.

- Calculate the measures of variation for the datset-GHG, that is, find the standard deviation, the variance and interquartile range.
- What does the standard deviation measure for this dataset?
- What does the interquartile range measure for this dataset?

- Use the HWY data above to answer the following:
- Make a 5 number summary
- Make a boxplot.

- Identify any outliers.

**Part III**

To determine whether there is any linear relationship between the number of cylinders (CYLINDERS) a car has and the greenhouse emission gasses (GHG) , first we make a scatterplot for the data, then we calculate the linear correlation coefficient. If there is strong linear correlation then we do regression. Answer the following questions:

- Make a scatterplot for CYLINDERS and GHG. Use your independent variable as CYLINDERS and dependent variable as GHG.
- Describe the type of linear correlation- positive, negative, no correlation. Is it nonlinear?
- Find the linear correlation coefficient between CLYLINERS and GHG.
- Describe the linear correlation coefficient. Is it positive or negative? Is it strong, moderate or week?
- Use Table A6 and to determine whether there is correlation between CYLINDER and GHG in the population.

- Find the regression line between CYLINDERS and GHG.
- What is the meaning of the slope for your regression equation?
- What is the meaning of y-intercept for your regression equation?

- Estimate the greenhouse emission gasses amount if the number of cylinders for cars could be 5.

**Part IV**

The ultimate goal in any statistical study is to make inferences about the population using the sample information. This is called inferential statistics.

- Suppose we are interested to predict the average tailpipe emissions of NO
_{x}(in lb/yr)(NOX) per year for all car models using the sample that is given in the column One way to do this is to construct a confidence interval for the population mean tailpipe emission of NOX. - Construct a 99% confidence interval for the mean tailpipe emission of NOX. Assume that the population of the tailpipe emission of NOX values are normally distributed. Find your point estimate, determine the sampling distribution, find the critical value, find the margin of error, and find the confidence interval.

- Conclude the confidence interval.

- Suppose we are interested to test hypotheses to determine a value for the population mean engine displacement (in liters) for all car models.
- Use a 0.01 significance level to investigate whether the mean engine displacement is more than 2.5 liters. Assume that the engine displacement of all cars is normally distributed. Set hypotheses, find your point estimate, determine the sampling distribution, find the test statistics, find the p-value and
- Conclude the test.

- Suppose we are also interested in the proportion of car models that have 4 cylindersin a sample. Suppose it is known than about 50% of all car models have 4 cylinders. Use the dataset CYLINDERS as a sample, and find the probability of randomly selecting a sample of 20 car models that contains
**more**4 cylinder cars than the number of 4 cylinder cars in dataset CYLINDERS. Find the sample proportion, determine the sampling distribution (normal), and find the probability (See chapter 15-the last couple of slides).

- Is there evidence that automatic cars are more common than manual cars?
- Use a 0.05 significance level to conduct a suitable hypotheses test using the dataset MAN/AUTO as a sample to conduct your test.Set hypotheses, find your point estimate, determine the sampling distribution, find the test statistic, find the p-value and
- conclude the test.