{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

End Solutions Statistics chapter 4

# End Solutions Statistics chapter 4 - Chapter 4 Introduction...

This preview shows pages 1–5. Sign up to view the full content.

Chapter 4 Introduction to Probability Learning Objectives 1. Obtain an appreciation of the role probability information plays in the decision making process. 2. Understand probability as a numerical measure of the likelihood of occurrence. 3. Know the three methods commonly used for assigning probabilities and understand when they should be used. 4. Know how to use the laws that are available for computing the probabilities of events. 5. Understand how new information can be used to revise initial (prior) probability estimates using Bayes’ theorem. 4 - 1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Chapter 4 Solutions: 1. Number of experimental Outcomes = (3) (2) (4) = 24 2. 6 3 6! 3 3 6 5 4 3 2 1 3 2 1 3 2 1 20 F H G I K J = = ⋅ ⋅ = ! ! ( )( ) ABC ACE BCD BEF ABD ACF BCE CDE ABE ADE BCF CDF ABF ADF BDE CEF ACD AEF BDF DEF 3. P 3 6 6! 6 3 6 5 4 120 = - = = ( )! ( )( )( ) BDF BFD DBF DFB FBD FDB 4. a. H T H T H T H T H T H T H T (H,H,H) (H,H,T) (H,T,H) (H,T,T) (T,H,H) (T,H,T) (T,T,H) (T,T,T) 1st Toss 2nd Toss 3rd Toss b. Let: H be head and T be tail (H,H,H) (T,H,H) (H,H,T) (T,H,T) (H,T,H) (T,T,H) (H,T,T) (T,T,T) c. The outcomes are equally likely, so the probability of each outcomes is 1/8. 5. P(E i ) = 1 / 5 for i = 1, 2, 3, 4, 5 P(E i ) 0 for i = 1, 2, 3, 4, 5 P(E 1 ) + P(E 2 ) + P(E 3 ) + P(E 4 ) + P(E 5 ) = 1 / 5 + 1 / 5 + 1 / 5 + 1 / 5 + 1 / 5 = 1 The classical method was used. 6. P (E 1 ) = .40, P(E 2 ) = .26, P(E 3 ) = .34 4 - 2
Introduction to Probability The relative frequency method was used. 7. No. Requirement (4.4) is not satisfied; the probabilities do not sum to 1. P(E 1 ) + P(E 2 ) + P(E 3 ) + P(E 4 ) = .10 + .15 + .40 + .20 = .85 8. a. There are four outcomes possible for this 2-step experiment; planning commission positive - council approves; planning commission positive - council disapproves; planning commission negative - council approves; planning commission negative - council disapproves. b. Let p = positive, n = negative, a = approves, and d = disapproves . Planning Commission Council p n a d a d (p, a) (p, d) (n, a) (n, d) 9. 50 4 50! 4 46! 50 49 48 47 4 3 2 1 230 300 F H G I K J = = = ! , 10. a. Use the relative frequency approach: P(California) = 1,434/2,374 = .60 b. Number not from 4 states = 2,374 - 1,434 - 390 - 217 - 112 = 221 P(Not from 4 States) = 221/2,374 = .09 c. P(Not in Early Stages) = 1 - .22 = .78 d. Estimate of number of Massachusetts companies in early stage of development = (.22)390 86 e. If we assume the size of the awards did not differ by states, we can multiply the probability an award went to Colorado by the total venture funds disbursed to get an estimate. Estimate of Colorado funds = (112/2374)(\$32.4) = \$1.53 billion Authors' Note: The actual amount going to Colorado was \$1.74 billion. 4 - 3

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Chapter 4 11. a. Total drivers = 858 + 228 = 1086 P(Seatbelt) = 858 .79 1086 = or 79% b. Yes, the overall probability is up from .75 to .79, or 4%, in one year. Thus .79 does exceed his .78 expectation. c. Northeast 148 .74 200 = Midwest 162 .75 216 = South 296 .80 370 = West 252 .84 300 = The West with .84 shows the highest probability of use. d. Probability of selection by region: Northeast 200 .184 1086 = Midwest 216 .200 1086 = South 370 .340 1086 = West 300 .286 1086 = South has the highest probability (.34) and West was second (.286).
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern