Study Guides (248,609)
Canada (121,634)
Marketing (517)
MKT 600 (9)

MKT600 Test 1

3 Pages

Course Code
MKT 600
Christopher Gore

This preview shows page 1. Sign up to view the full 3 pages of the document.
Bohdan Kaflyuk / Sec 021 Probability: is the numeric value representing the chance, like hood, or possibility a particular event will occur, such as the price of a stock increasing, a rainy day, a defective product, or the outcome five in a single toss of a die. There is 3 types of probability: A Priori, Empirical, Subjective. A priori probability : Probability of success is based on prior kProbability of occurrence: X. X= number of ways in which the event occurs / T= total number of possible outcomes. Ex: A die has 6 sides. Each face has a number 1 to 6. If you roll a die what's the probability? Solution: Each face is equally likely to occur. Since there is 6 faces, the probability of getting a face with 5 dots is 1/6. Empirical Probability: Probability based on observed data, not on prior knowledge of a process. Surveys are often used to generate empirical probability. Examples of this type of probability are the proportion of individuals in using Subjective Probability: Different from the other 2.ts , and 60% state that they have part time jobs , then the ratio is 6.0 for students having part time job. Probability distribution for a discrete random variable: is a mutually exclusive listing of all the possible numerical outcomes along with the probability of occurrence of each outcome. Random Variable: A variable whose value is determined by outcomes of a random experiment. SYMBOL used is usually X. Discrete random Variable: A random variable whose possible values are discrete, i.e. clearly separated individual values. Discrete Probability Distribution: A table or formula that shows the probability associated with each possible value of a discrete random variable. Let X = # cars sold on Sunµ= MeanSunday at a particular dealership shows that the following probability distribution exists: n ∑ XiP(Xi) Probability distributionµ= E(X)=: ------->Expected Value of a Discrete Variable. i=1 X P(x) µ= E(X) =∑ XP(X) -------------> Mean or Expected Value X−μ 2 ¿ 1 0.1 √ σ = ¿ ----------> Variance ∑ ¿ 2 X−μ¿ (¿ ¿P(X) ) σ 2 0.1 = ∑ ¿ ------> Standard Deviation ¿ √ ¿ 3 0.4 4 0.3 Total 1.0 The mean, µ, of a probability distribution is the Expected Value of its random variable. To calculate Expected Value: Multiply each possible outcomes, X, by its corresponding probability, P(X), and then sum these products.(of a discrete random variable) EX: (0)(0.01)+(1)(0.1)+(2)(0.2)+(3)(0.3)+(4)(0.15)........=2.8 Press: F6---> list:List1/XList: List/ Freq: List 2 ∑ ¿ i=1 √ ¿ 2 Xi−E X ¿ ( ) ¿ 2 2 ¿ Standard Deviation of a Discrete Random =ariable: = n List two:0.10, 0.10,0.2To get the answer press F1(Var 1) ∑ ¿ i=1 √ ¿ EX. Home Mortgages mean : 2.8 / x= 1.5684 2 Approved per Week P(X) XP(X) [X- E(X) P(Xi) i i i i i 2 0 0.10 (0)(0.10)=0(0-2.8) (0.10)= 0.784 σ 2 1 0.10 (1)(0.10)=0.(1- 2.8) (0.10)= 0.32=* √ σ = √ 2.46 = 1.57 2 0.20 (2)(0.20)=0............ 3 0.30 (3)(0.30)=0............. * The mean number of mortgages approved per week is 2.8, the Variance is 2.46,and the Standard Deviation is 1.57 5 0.10 (5)(0.10)=0............. 6 0.05 (6)(0.05)=0...... 2 1.00 μ = E(X)= 2.8 σ = 2.46 Mathematical Model: is a mathematical expression that represents a variable of interest. Binomial Distribution: is one of the most useful mathematical models. You use it when discrete random variable is the number of events of interest in a sample of N observations. F5F5---F1 OR F2 IF you want to find a probability even an exact amount of times THEN USE Bpd (Probability) X= # of successes / n= # of trials or sample space. / π= probability of success of each trial. / n!= n factorial(6!= 720) EX: toss coin 6 times, find probability of getting 2nCaRl/ n choose r / order doesn't matter. n= 6 / π= 0.5 / x = 2 CALC: F5 then F5 then F1 choose variable EX: Roll a die 10 times. Find the probability of getting EX: Survey of people in 30 and under 40 age bracket shows that 43 % of them investments in mutual funds. In a particular condominium are 18 x= # of 5's / n= 10 / π= 1/6....................CALC: x= 3 / numtriaadults in the age bracket. what is the probability that : - at most 2 of these people will have inv. in mutual funds? : CALC: (C.D) = 4.1007E.03--->= 0.0041007 Mean of Binomial Distribution: The mean of binomial distribution is equal to simple size ,n, multiply by the probability of an event of interest , π.: (C.D) e −A A x Poisson Distribution: P(X)= X! * P(X)= the probability of X events in an area of opportunity / A= expected number of events / e= Mathematical constant approximated by 2.71828/ X number of events( X = 0, 1 , 2 ...) μ CALC: F5--->F6--->F1--->F1 for (Ppd).........." symbol instead of "A"......................Ppd calculates p(X = #)............Pcd calculates P(X ≤ #) Normal distribution: EX: Suppose that the weights of adults are normally distributed when a mean of 170.0 lbs. and a standard deviation of 25.0 lbs. CALC: F5-->F1--->F2 A. what is the probability that a person weights from 150 to 190 lbs.? X= persons weight / = 170lbs / σ = 25.0 lbs.... CALC: lower =150/Upper= 1=25/ μ = 170...It will give you the answer. B. what is the probability that a person weights less than 140lbs? μ σ
More Less
Unlock Document

Only page 1 are available for preview. Some parts have been intentionally blurred.

Unlock Document
You're Reading a Preview

Unlock to view full version

Unlock Document

Log In


Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.