Chat with us, powered by LiveChat Step 1: Practice Final Exam To help you prepare for the Week 5 ? Final, there is a practice final ex - Safehomework

Step 1: Practice Final Exam To help you prepare for the Week 5 ? Final, there is a practice final ex

 Step 1:

Practice Final Exam

To help you prepare for the Week 5 – Final, there is a practice final exam, the Week 5 – Practice Final, within MyStatLab. The advantage of completing a practice final is that questions are similar to those on the final, and all the help features are included. You will be able to view a similar solved example or check out the most applicable textbook pages as you learn on the Week 5 – Practice Final. You are required to take the Week 5 – Practice Final, but you don’t need to ace it. A score of 30% or better is required to move forward to take the graded final.

This preparation is important since you can only take the final one time. You must answer the questions in order and you may not return to a question after you have worked on it, so be sure to complete the question before clicking NEXT or SAVE FOR LATER. The final is timed; you need not complete it in a single sitting as the “Save for Later” option is enabled. It may be less stressful to complete half of the final, save the final and complete the rest later. Note: When you return to complete the final, you cannot revise answers on any questions previously viewed or answered.

To access the practice final exam, click on the following link:

Step 2:

Final Exam

This final exam must be completed on the MyStatLab system by the deadline. The exam contains 20 multiple choice and short answer questions. You have four hours to complete the exam and just one attempt. Other than StatCrunch, help is not included on the final exam.

Topics covered in the final exam include

  • the empirical rule (68-95-99.7 rule),
  • reading and interpreting normal distributions,
  • applying the central limit theorem,
  • determining whether variables are correlated,
  • finding the correlation coefficient, r,
  • constructing scatterplots,
  • finding best fit lines,
  • finding and interpreting confidence intervals, and
  • determining if a normal distribution is present when given real life variables

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