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Quantitative Techniques in Management (VVN)
Quantitative Techniques in Management (VVN)
We Also Provide SYNOPSIS AND PROJECT. Contact www.kimsharma.co.in for best and lowest cost solution or Email: solvedstudymaterial@gmail.com Call: +91 82907-72200 (Call/WhatsApp) or +91 88003-52777 (WhatsApp Only) Quantitative Techniques in Management (VVN) 1 . What do you understand by a Linear Programming Problem? What are its limitations? Discuss briefly the applications of linear programming in any functional area of management. 2 . Solve the following transportation problem for optimal solution. W1 W2 W3 W4 W5 quantity P1 20 28 32 55 70 50 P2 48 36 40 44 25 100 P3 35 35 22 45 48 150 Demand 100 70 50 40 40 3 . What is the Hungarain mathod for the assingnment problem ? 4 . What do you mean by correlation? Explain various type of correlation with the help of examples . 5 . What is the probability of getting a sum 'FOUR' when two dice are thrown . 6 . Discuss various components of time series with the help of examples. 7 . For the given of a random variable x and associated probabilities ( given in rows 1 and 2 of the following table ) work out the variance and standard deviation X 2 3 4 5 6 7 8 9 10 Total P(x) .05 .10 .30 .20 .05 .10 .05 .10 .05 1.00 8 . Boys of a certain age are known to have a mean weight of μ = 45 Kilograms. A complaint is made that the boys living in a municipal children's home are underfed. As one bit of evidence, n = 50 boys (of the same age) are weighed and found to have a mean weight of x¯¯ = 41.5 Kilograms. It is known that the population standard deviation σ is 5.6 Kilograms (the unrealistic part of this example!). Based on the available data, what should be concluded concerning the complaint? Case Detail : The length of life of an instrument produced by a machine has a normal ditribution with a mean of 14 months and standard deviation of 2.5 months. Find the probability that an instrument produced by this machine will last 1. between 10 and 14 months. 2. less than 10 months. 3. more than 10 months 1. Scatter diagram is also called _________________ Correlation graph Dot Chart Zero correlation None of these 2. Correlation can be ____________________________________________ Positive only Positive or negative Negative only None of these 3. In correlation analysis, P.E. = ________________. x 0.6745 Standard Error Probable Error Correlation analysis None of these 4. Regression lines are also called ________________________. Correlation graph Scatter diagram Estimating lines None of these 5. The arithmetic mean of bxy and byx is ____________________________. Equal to 1 Equal to 2 Greater than r Less than r 6. ____________________________. refers to the chance of happening or not happening of an event. Regression Probability Correlation None of these 7. An event whose occurrence is impossible, is called ______________________ Sure event Impossible event Uncertain event None of these 8. If two events, A and B are not mutually exclusive, the P(AUB) = __________________ P(A) + P(B) P(A) + P(B) - P(A and B) P(A) + P(B) + P(A and B) None of these 9. The definition of priori probability was originally given by ____________________________ De-Moivre Laplace Pierre de Fermat James bernoulli 10. Three dies are thrown, probability of getting a sum of 3 is ____________________. 3/216 (2/3) (3/36) 1/216 11. Binomial distribution is a ________________________________ probability distribution Discrete Continuous Continuous distribution None of these 12. When probability is revised on the basis of all the available information, it is called ____________. Priori probability Continuous Posterior probability None of these 13. The height of persons in a country is a ________________________. random variable. Discrete Continuous Discrete as well as continuous None of these 14. For a binomial distribution with probability p of a success and of q of a failure, the relation between mean and variance is ____________________________. Mean is greater than variance Mean is less than variance Mean is equal than variance Mean is greater than or equal to variance 15. In a binomial distribution, if n =8 and p = 1/3, then variance = ________________________ (16/9) (8/3) 48/3 64/3 16. Poisson distribution is the limiting form of ______________________________. Poisson Binomial distribution Normal distribution None of these 17. Poisson distribution is a ____________________________probability distribution. Discrete Continuous Poisson None of these 18. In Poisson distribution, the value of ‘e’ = __________________________ 282 718 1.718 2.718 19. Mean and variance of Poisson distribution is equal to ______________________________. nq e m npq 20. __________________________.distribution gives a normal bell shaped curve. Poison Binomial Normal None of These 21. The height of normal curve is at its maximum at the ______________________. Mean Mode Medain None of these 22. Normal distribution is ______________________ Continuous Unimodal Symmetrical All of these 23. An approximate relation between MD about mean and SD of a normal distribution is 5MD = 4 SD 3MD = 3 SD 3MD = 2 SD 4MD = 5 SD 24. In a ________________________. distribution, quartiles are equi-distant from median Poison Normal Binomial None of These 25. A normal distribution requires two parameters, namely the mean and ______________ Standard deviation mean deviation Mode Medain 26. Mean ± 2 S.D. covers ______________.% area of normal curve. 95.45 98.73 68.27 95.54 27. A __________________________ is a function of sample values. Statistic Parameter Population None of these 28. Test of hypothesis and ________________________ are the two branches of statistical inference Probability Statistical analysis Estimation None of these 29. Quartile deviation of normal distribution is equal to ____________________ 2/3 S.D. 4/5 S.D. 3/4 S.D. 1 S.D. 30. Type I error is denoted by the symbol ________________________________. Alpha Beta Gramma None of these 31. A sample is treated as large sample when its sample size is ____________________________ More than 30 More than 100 More than 20 More than 50 32. Degrees of freedom for Chi-square in case of contingency table of (4x3) order are __________________. 6 7 8 9 33. By test of significance , we mean ____________________________ A significant procedure in statistics A method of making a significant statement A rule of accepting or rejecting hypothesis A significant estimation problem 34. When sample is small, ________________________ test is applied. z- test y- test i- test t- test 35. Who developed F-test ? R.A. Fischer Karl Pearson James Bernoulli Charles Babage 36. Chi-square test was developed by __________________ R.A. Fischer Karl Pearson William Gosset James Bernoulli 37. In a normal curve, the significance level is usually termed as ______________________region Acceptance region Critical region Level of acceptance None of these 38. Chi-square test was first used by____________________________ R.A. Fischer Karl Pearson William Gosset James Bernoulli 39. If two samples of size 9 and 11 have means 6.8 and 8.8, and variance 36 and 25 respectively, then value of t = ____________________. 0.79 1.79` 2.79 None of these 40. In one way ANOVA, the variances are ______________________ Between samples Within samples Both 1&2 Neither 1 nor 2 option We Also Provide SYNOPSIS AND PROJECT. Contact www.kimsharma.co.in for best and lowest cost solution or Email: solvedstudymaterial@gmail.com Call: +91 82907-72200 (Call/WhatsApp) or +91 88003-52777 (WhatsApp Only)

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