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 use for convenience. But you are not restricted to use only those datasets.I will add files that will tell project instructions, links of data set, and a sample project to help you understand the assignment

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

Ann Marie SteeleAlihaa AtteeqAbner Casillas-Colon Final Statistical Project “Carbon Emissions by Weight” Data Set Model Year 2017 Vehicle Attributes by ManufacturerThis is the data set depicting the average weight (in pounds) of vehicles made by automobile 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.

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

Statistical Project : Points: 5+10+5=20 points It has 3 parts. Please read carefully and upload and submit all of the sections. You can use any dataset of your choice to analysis. I have some data sets and web link to use for convenience. But you are not restricted to use only those datasets. Part 1: (5 points) (Based on Chapter 2) Upload your data set and link A short description of the data set and your objective. Don’t write anything in first person Provide some descriptive Statistics (Mean, Median, Mode, whisker’s box plot, Five number summary) Plots (appropriate plots) Interpret your finds from charts and five number summaries. This may have a conclusion of your findings. Part 2: (10 points) For this part you can use the dataset that you have used in part 1 or can select a new dataset from any legitimate website. But it is better to select a dataset with at least on independent and one dependent variable. Because this concept will be needed in regression analysis. (Based on Chapters 7 or 8) 1.Conduct the hypothesis test on your selected data. The test can be based on one or two sample tests based on your selected data. (Based on Chapter 9) 2.Use your selected data to find the correlation(r) between two variables on which you are interested in. You can also conduct the correlation test. Interpret the correlation value and test result. 3. Find the dependent and independent variables of your dataset and construct the regression equation of y on x. [where y= dependent variable, x= independent variable] 4. Fit the regression line. Interpret the regression equation. 5. Include part 1 and part 2 of statistical project 2. You will learn the concepts for part 2 from Chapters 7 to 9. You need to upload the pdf of your presentation on module under statistical project as well by April 26 by 11.59 pm. Part 3: Presentation (5 points) Prepare a PPT and upload a 5 minutes presentation of your analysis displaying the ppt based on your analysis on the discussion board by April 26 at 11.59 pm.

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

Link for different data set: https://www.data.gov/ (Links to an external site.) *Demographic_Statistics_By_Zip_Code.csv https://www.bjs.gov/latestreleases.cfm (Links to an external site.)

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