# Quantitative Techniques in Management (VVN)

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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 (4×3) 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
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