The consumer decision-making process consists of:

1. Need Recognition

2. Information Search

3. Information and Evaluation

4. Buying Decision

5. Post Purchase Evaluation

How can we use the information obtained through the analysis of each one of the stages of the consumer decision-making process to improve the knowledge about consumers?

Suppose that buses arriving at a certain stop can be modeled as a Poisson process with a rate parameter of 6 per hour. (Give answers with 3 digits after decimal) a) [1pt] What is the probability that 5 buses arrive during an hour? Submit Answer. Tries 0/99 b) [2pts] What is the probability that no bus arrives during 40 mins? Submit Amver Tries 0/99 c) [2pts] Suppose you just arrive at this stop, what is the probability that you need to wait at least 25 minutes for the bus? Submit Apoem Tries 0/99 d) [2pts] What is the 40'th percentile of your waiting time (In hours]? Subma Arwer Tries 0/99 e) [1pt] What is your expected waiting time (in hours) ? Submit Answer Tries 0/99Consider the sequence {an} dened by an 2 1/4732 + 1m 2n. \"=1 (1) Use the Main Limit Theorem to prove that this sequence converges. Please point out which part of this theorem you use each time you invoke it, taking care to justify why you can apply it. Hint: As it is written, convince yourself that you cannot naively apply the Main Limit Theorem to {a}ff'=1. (2) Using only the definition of what it means for a sequence to converge, prove that this sequence converges to the h'rnit you determined above. (3) Write a. few words describing your appreciation for the Main Limit Theorem. Fourier analysis 1. Say, X1 (t) = A( 2 . t/9) x e-15 .t/71 x cos (2 n . t - Tt/) ; It| $ 5 , and zero elsewhere. Using the format of time - frequency pairs: a. Produce an educated guess (by hand) for the spectral decomposition of the signal X, (t) b. Carry on the spectral decomposition computation of X, (t) c. Compare the results in parts a and b above. d. Consider this time contamination with additive white Gaussian noise