This is a statistics Project. It has 3 parts. Please read carefully and upload and submit all of the sections.You can use any dataset of your choice to analyze. I have some data sets and weblinks to u

manufacturers and the amount of real-world carbon emissions (measured by g/mi) given off by

each in the year 2017. The data set has 14 observations with 2 variables. All variables are

quantitative.

Source: Office of Transportation and Air Quality EPA-420-R-19-002 March 2019Link:

https://nepis.epa.gov/Exe/ZyNET.exe/P100W5C2.TXT?ZyActionD=ZyDocument&Client=EPA&In

dex=2016+Thru+2020&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=n&T

oc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&IntQFieldOp=0&ExtQField

Op=0&XmlQuery=&File=D%3A%5Czyfiles%5CIndex%20Data%5C16thru20%5CTxt%5C00000011

%5CP100W5C2.txt&User=ANONYMOUS&Password=anonymous&SortMethod=h%7C-

&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Dis

play=hpfr&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&BackDesc=Results%20pag

e&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL Reliability This data set is reliable in terms that it comes from a reputable source – i.e. the Office of

Transportation and Air Quality within the Environmental Protection Agency of the United States

government. The reason for choosing this data set is to seek information on the current

international concern of climate change and how the correlation between vehicle weight and

carbon emissions contributes to this discussion. The data provides sufficient information

between the carbon emissions produced and the weight of a vehicle manufactured in 2017 by

analyzing 14 different vehicle manufacturers. Each manufacturer includes average weights for

several different models of cars.

Descriptive Statistics Variables of interestThe variables of interest here are how the weight of a vehicle affects the amount of real-world

CO2 emissions

X: Weight of Vehicle Y: Amount of Real-

World CO2 emissions

MeanoCO2 emissions:

343.714 g/mi

oWeight: 4031.5

pounds

MedianoCO2 emissions: 331

g/mi

oWeight: 3976.5

pounds

ModeoCO2 emissions:

327g/mi and 388 g/mi

oWeight: no mode In both plots, there are no outliers, meaning there is no extreme value that affects the data or

the graphs.

Correlation & Regression The correlation coefficient of our data set is R=.73The Regression Equation of our data set is: Y=92.80 + 0.06xThis regression equation indicates that for every 1 pound of weight increase the increment of

CO2 emission will be .06 (g/mi). This would seemingly indicate strong positive correlation

between vehicle weight and CO2 emissions. The Y-intercept would indicate that an engine

would still emit 92.80(g/mi) of CO2 if it was not moving any additional weight. Hypothesis Test The hypothesis test will seek to provide evidence for an Alternative Hypothesis by rejecting a

null hypothesis. For Vehicle weight and carbon emissions it will be examining whether vehicle

weight has an effect of CO2 emission levels. As the P-Value is smaller than the significance level

we reject the Null Hypothesis

The Null Hypothesis is H: L o= 0 The Alternative Hypothesis is H: L a M 0 The significance level for this test will be at 95% or N=.05 The Degrees of freedom are 12The T Statistic=3.67 and the P-Value=.0032 Conclusion Vehicle weight may be overlooked when making the purchasing decision for a new vehicle. This

may be something to reconsider as we have shown that there is a strong positive correlation

between vehicle weight and Carbon Emissions. In addition, our Hypothesis test has shown that

at a 95% significance level this change is significant. Our hypothesis test does have some failings

when exposed to scrutiny. While the data set is quantitative the fact that all our data points

were mean summary statistics limited our ability to draw a usable sample size. This means that

the data set was not randomly obtained. In future testing this can be resolved by using a data

set that does not average the data of each manufacturer and keeps it isolated to each specific

vehicle model. This also presents issues with the distribution as the data set was relatively

miniscule. The lack of outliers and usage of a two-sided test are methods by which we

attempted to address some of the possible issues with the distribution of the data. Issues with

this testing however may arise from hidden variables such as engine performance and exhaust

quality.