Question I - The appellate court decides that the trial court committed reversible error by including evidence found by law enforcement. Law enforcement discovered this evidence when committing a Fourth Amendment violation, which should have been excluded at trial. This inadmissible evidence was the lynchpin of the prosecutor’s case, which resulted in a conviction. Where does the case go from here? Is the Defendant free to go? Does it go back to the trial court? Does it go all the way up to the Supreme Court?Question II - Lonnie dies while working on a barge. Lonnie’s widow sues the barge company in state court. The Parties agree on the facts and cause of Lonnie’s death; however, they do not agree whether the Longshoreman Act should apply to this case. Lonnie’s widow has already received a remedy in a separate administrative action as part of a workers’ compensation claim. The Longshoreman Act would allow the decedent’s family to pursue an action in court, even if the family has agreed to a settlement as part of the worker’s compensation action. If the Longshoreman Act does not apply, then the decedent’s family will have no remedy in court. Prior to trial, what motion should the barge company’s attorney make? What must this attorney prove in order for her motion to be successful?

5-2 Jupyter Notebook (Discussion Prep)External Learning ToolThis activity will take you to the Jupyter Notebook containing the Python scripts for your Module Five discussion. It is highly recommended that you read through the discussion prompt before completing your work in this notebook. When you are finished completing and running the Python scripts, begin work on your initial discussion post.Note: This task is not graded, but you will be required to attach your completed Jupyter notebook to your discussion post in the next activity.5-3 Discussion: Simple Linear RegressionDiscussion Topic Starts May 29, 2021 8:59 PMUse the link in the Jupyter Notebook activity to access your Python script. Once you have made your calculations, complete this discussion. The script will output answers to the questions given below. You must attach your Python script output as an HTML file and respond to the questions below.In this discussion, you will apply the statistical concepts and techniques covered in this week's reading about correlation coefficient and simple linear regression. A car rental company wants to evaluate the premise that heavier cars are less fuel efficient than lighter cars. In other words, the company expects that fuel efficiency (miles per gallon) and weight of the car (often measured in thousands of pounds) are correlated. Performing this analysis will help the company optimize its business model and charge its customers appropriately.In this discussion, you will work with a cars data set that includes two variables:Miles per gallon (coded as mpg in the data set)Weight of the car (coded as wt in the data set)The random sample will be drawn from a CSV file. This data will be unique to you, and therefore your answers will be unique as well. Run Step 1 in the Python script to generate your unique sample data.In your initial post, address the following items:You created a scatterplot of miles per gallon against weight; check to make sure it was included in your attachment. Does the graph show any trend? If yes, is the trend what you expected? Why or why not? See Step 2 in the Python script.What is the coefficient of correlation between miles per gallon and weight? What is the sign of the correlation coefficient? Does the coefficient of correlation indicate a strong correlation, weak correlation, or no correlation between the two variables? How do you know? See Step 3 in the Python script.Write the simple linear regression equation for miles per gallon as the response variable and weight as the predictor variable. How might the car rental company use this model? See Step 4 in the Python script.What is the slope coefficient? Is this coefficient significant at a 5% level of significance (alpha=0.05)? (Hint: Check the P-value, , for weight in the Python output.) See Step 4 in the Python script.In your follow-up posts to other students, review your peers' calculations and provide some analysis and interpretation:How do their plots and correlation coefficients compare with yours?Would you recommend this regression model to the car rental company? Why or why not?Remember to attach your Python output and respond to all questions in your initial and follow-up posts. Be sure to clearly communicate your ideas using appropriate terminology. Finally, be sure to review the Discussion Rubric to understand how you will be graded on this assignment.