CHAPTER 8. TRIP DISTRIBUTION NPTEL May 3‚ 2007 Chapter 8 Trip distribution 8.1 Overview The decision to travel for a given purpose is called trip generation. These generated trips from each zone is then distributed to all other zones based on the choice of destination. This is called trip distribution which forms the second stage of travel demand modeling. There are a number of methods to distribute trips among destinations; and two such methods are growth factor model and gravity
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Normal Distribution:- A continuous random variable X is a normal distribution with the parameters mean and variance then the probability function can be written as f(x) = - < x < ‚ - < μ < ‚ σ > 0. When σ2 = 1‚ μ = 0 is called as standard normal. Normal distribution problems and solutions – Formulas: X < μ = 0.5 – Z X > μ = 0.5 + Z X = μ = 0.5 where‚ μ = mean σ = standard deviation X = normal random variable Normal Distribution Problems and Solutions – Example
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25‚ 28 26‚ 28‚ 26‚ 28‚ 31‚ 30‚ 26‚ 26 the information is to be organized into a frequency distribution. A. How many classes would you recommend? b. What class interval would you suggest? C .what lower limit would you recommend for the first class? d. organize the information into a frequency distribution and determine the relative frequency distribution. e. comment on the shape of the distribution. 15. Molly’s Candle Shop has several retail stores in the coastal areas of North and South
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DISTRIBUTION IN BANKING BUSINESS Distribution in financial services marketing is concerned with how the service is delivered to the customer‚ making sure that it is available in a place‚ at a time and in a format that is appropriate and convenient for the customer. In a growing number of countries‚ the expansion of the financial services sector has been accompanied by a significant blurring of lines between different institutional types with‚ for instance‚ retail banks offering insurance products
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Statistics: • Science of gathering‚ analyzing‚ interpreting‚ and presenting data • Measurement taken on a sample • Type of distribution being used to analyze data Descriptive statistics: Using data gathered on a group to describe or reach conclusions about that same group only. Descriptive statistics are the tabular‚ graphical‚ and numerical methods used to summarize data. Collect‚ organize‚ summarize‚ display‚ analyze Eg: According to Consumer Reports‚ General Electric washing machine
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1.1 GENERAL INTRODUCTION Diabetes mellitus‚ often simply referred to as diabetes‚ is a group of metabolic diseases in which a person has high blood sugar‚ either because the body does not produce enough insulin‚ or because cells do not respond to the insulin that is produced. It is a serious‚ lifelong condition. The three main types of diabetes are: Type 1 diabetes results from the body’s failure to produce insulin‚ and presently requires the person to inject insulin. Type 2 diabetes
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Distribution channel of LIC Child Fortune plus ANAND.G MOULAN.S MANOJ.K RAVEE KUMAR.M.S Primary objective of the distribution is to increase the customer base who have a disposable income level of more than 2 lakhs per annum. Price: The price of a life insurance depends upon the period by which premium is bieng paid. Specifications of LIC child fortune plus is given below Specifications | LIC Child Fortune plus | Age (Male) | 35 years | Premium | 1‚00‚000 | Sum Assured | 5‚00
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solve k = 20.275 d) P ( 17 < X < 21) P ( (17 -18)/2.5 < Z < ( 21-18)/2.5) P ( -0.4 < Z < 1.2) = 0.8849 – 0.3446 = 0.5403 ( 4 decimal places) 4. In a sample of 25 observations from a Normal Distribution with mean 98.6 and standard deviation 17.2‚ find: Ans: a) n = 25‚ [pic] = ( = 98.6‚ [pic] = /n = 17.2/(25 = 3.44 [pic]( N
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SALES AND DISTRIBUTION MANAGEMENT ASSIGNMENT MORGAN & BOSS OFFICE EQUIPMENT DIVISION SUBMITTED BY ANGANA (F13005) ASHISH CHANDY (F13015) CHRISTINA IMMACULATE (F13021) DHANYA ANN ROY (F13025)
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Simple random sample (SRS) In statistics‚ a simple random sample from a population is a sample chosen randomly‚ so that each possible sample has the same probability of being chosen. One consequence is that each member of the population has the same probability of being chosen as any other. In small populations such sampling is typically done "without replacement"‚ i.e.‚ one deliberately avoids choosing any member of the population more than once. Although simple random sampling can be conducted
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