Tag: Statistics for Management

UGC NET MBA Unit-8 MCQs

Statistics for Management, Operations, and Operations Research

SECTION A – STATISTICS: CONCEPTS & DESCRIPTIVE ANALYSIS

1. Statistics in management is primarily used to:
A. Replace intuition with quantitative analysis
B. Eliminate human judgment
C. Avoid decision-making
D. Only summarize data
Answer: A
Explanation: Statistics brings objectivity and helps managers make rational decisions based on numerical evidence.*

2. Which of the following is not a function of statistics?
A. Data presentation
B. Forecasting
C. Moral judgment
D. Data collection
Answer: C
Explanation: Statistics is factual and quantitative; moral judgment is outside its scope.*

3. Descriptive statistics involves:
A. Summarizing and presenting data
B. Drawing inferences
C. Testing hypotheses
D. Estimating parameters
Answer: A

4. The process of drawing conclusions about a population based on sample data is called:
A. Inferential statistics
B. Descriptive statistics
C. Enumeration
D. Classification
Answer: A

5. Which of the following scales has a true zero?
A. Nominal
B. Ordinal
C. Interval
D. Ratio
Answer: D
Explanation: Ratio scale includes an absolute zero, allowing ratio comparisons (e.g., weight, income).*

6. In a normal distribution, mean = median = mode.
Answer: True
Explanation: Normal distribution is symmetric; all measures of central tendency coincide.*

7. Which is the most affected by extreme values?
A. Median
B. Mode
C. Mean
D. Geometric mean
Answer: C

8. The measure that divides data into 100 equal parts is called:
A. Percentiles
B. Quartiles
C. Deciles
D. None
Answer: A

9. If Mean = 50 and SD = 5, what is the coefficient of variation (CV)?
A. 10%
B. 5%
C. 15%
D. 20%
Answer: A

$C V = \frac{σ}{\overset{ˉ}{X}} \times 100 = \frac{5}{50} \times 100 = 10 %$

10. For skewed data, which measure is most appropriate?
A. Median
B. Mean
C. Mode
D. Range
Answer: A
Explanation: Median is less affected by extreme values and better represents central tendency.*

🔹 SECTION B – DISPERSION & PROBABILITY DISTRIBUTIONS

11. The square root of variance gives:
A. Mean deviation
B. Standard deviation
C. Coefficient of variation
D. Range
Answer: B

12. A low coefficient of variation indicates:
A. High consistency
B. High variability
C. Instability
D. None
Answer: A

13. In a binomial distribution, mean = np. If n = 10 and p = 0.3, mean = ?
A. 3
B. 7
C. 10
D. 5
Answer: A

14. For Poisson distribution, variance = ?
A. Mean
B. np(1−p)
C. n²p
D. None
Answer: A

15. The shape of a normal distribution curve is:
A. Bell-shaped and symmetric
B. Positively skewed
C. Negatively skewed
D. Uniform
Answer: A

16. In a normal distribution, 95.45% of data lies within:
A. ±1σ
B. ±2σ
C. ±3σ
D. ±4σ
Answer: B

17. The total area under a probability density curve equals:
A. 1
B. 0
C. 100
D. Mean value
Answer: A

18. The expected number of occurrences in Poisson distribution is represented by:
A. m or λ
B. σ²
C. n
D. p
Answer: A

19. Exponential distribution is used to model:
A. Waiting or service time
B. Number of trials
C. Income distribution
D. None
Answer: A

20. If two events A and B are independent, then $P (A \cap B) = ?$
A. $P (A) + P (B)$
B. $P (A) \times P (B)$
C. $P (A) - P (B)$
D. $1 - P (A)$
Answer: B

🔹 SECTION C – SAMPLING AND QUESTIONNAIRE DESIGN

21. Sampling is used because:
A. Studying the whole population is costly and time-consuming
B. Complete enumeration is impossible
C. Both A and B
D. None
Answer: C

22. Sampling error arises due to:
A. Observing only a part of population
B. Faulty data recording
C. Incorrect coding
D. None
Answer: A

23. In stratified sampling, the population is divided into:
A. Homogeneous groups (strata)
B. Heterogeneous groups
C. Random clusters
D. None
Answer: A

24. Systematic sampling involves:
A. Selecting every kth item from a list
B. Random selection
C. Dividing into strata
D. None
Answer: A

25. Snowball sampling is especially useful for:
A. Hidden or hard-to-reach populations
B. Large homogeneous populations
C. Statistical inference
D. None
Answer: A

26. The first step in questionnaire design is:
A. Defining objectives
B. Drafting questions
C. Testing
D. Editing
Answer: A

27. Which type of question allows freedom in answering?
A. Open-ended
B. Closed-ended
C. Dichotomous
D. Multiple choice
Answer: A

28. A biased questionnaire results in:
A. Invalid data
B. Accurate results
C. Efficient sampling
D. None
Answer: A

29. The purpose of pilot testing a questionnaire is:
A. To identify and correct errors before final use
B. To collect final data
C. To test reliability only
D. None
Answer: A

30. The most common non-probability sampling used in marketing surveys is:
A. Convenience sampling
B. Stratified sampling
C. Random sampling
D. Systematic sampling
Answer: A

🔹 SECTION D – HYPOTHESIS TESTING

31. The null hypothesis (H₀) assumes:
A. No significant difference exists
B. A difference exists
C. Sample is biased
D. None
Answer: A

32. The level of significance is:
A. Probability of committing Type I error
B. Probability of Type II error
C. Power of test
D. None
Answer: A

33. Type I error occurs when:
A. True H₀ is rejected
B. False H₀ is accepted
C. False H₁ is accepted
D. Both A and C
Answer: A

34. Which test is suitable for small samples with unknown variance?
A. t-test
B. Z-test
C. F-test
D. χ² test
Answer: A

35. Z-test is used when:
A. Population variance is known
B. Sample size is large (n>30)
C. Both A and B
D. None
Answer: C

36. Chi-square test is applied to:
A. Qualitative or categorical data
B. Continuous data
C. Large numerical datasets
D. None
Answer: A

37. F-test is used to compare:
A. Two variances
B. Two means
C. Two proportions
D. Two correlations
Answer: A

38. In hypothesis testing, “p-value” indicates:
A. Probability of obtaining test statistic at least as extreme as observed
B. Mean of data
C. Sample size
D. None
Answer: A

39. A two-tailed test is used when:
A. Deviation can occur in either direction
B. Deviation occurs only on one side
C. Data are nominal
D. None
Answer: A

40. The decision to reject H₀ is made when:
A. p-value < α
B. p-value > α
C. Mean difference = 0
D. None
Answer: A

🔹 SECTION E – CORRELATION & REGRESSION

41. Karl Pearson’s correlation coefficient measures:
A. Strength and direction of linear relationship
B. Cause-effect relationship
C. Non-linear association
D. None
Answer: A

42. If r = +1, the two variables are:
A. Perfectly positively correlated
B. Perfectly negatively correlated
C. Unrelated
D. None
Answer: A

43. If correlation between X and Y is zero, it means:
A. No linear relationship
B. Independent variables
C. Non-linear relation exists
D. None
Answer: A

44. Regression analysis helps in:
A. Predicting one variable from another
B. Comparing means
C. Sampling
D. None
Answer: A

45. The slope (b) in regression equation $Y = a + b X$ indicates:
A. Change in Y per unit change in X
B. Mean of Y
C. Intercept
D. None
Answer: A

46. If r = 0.8, coefficient of determination $r^{2} = ?$
A. 0.64
B. 0.8
C. 1.6
D. 0.4
Answer: A

47. In multiple regression, number of independent variables is:
A. More than one
B. One
C. Two only
D. None
Answer: A

48. Regression coefficient can be negative.
Answer: True

49. Correlation implies causation.
Answer: False
Explanation: Correlation shows association, not cause-effect relationship.*

50. Spearman’s rank correlation is suitable for:
A. Ordinal data
B. Ratio data
C. Interval data
D. None
Answer: A

SECTION F – OPERATIONS MANAGEMENT

51. Operations Management primarily deals with:
A. Conversion of inputs into outputs efficiently
B. Marketing of products
C. Accounting for profit
D. Setting financial goals
Answer: A
Explanation: Operations Management focuses on optimizing production and service processes to deliver value.*

52. The main objective of Operations Management is:
A. Increase efficiency and effectiveness
B. Maximize advertising
C. Minimize competition
D. None
Answer: A

53. Which of the following is not a function of Operations Management?
A. Product design
B. Plant layout
C. Inventory control
D. Stock valuation
Answer: D

54. Productivity is defined as:
A. Output / Input
B. Input / Output
C. Profit / Cost
D. Cost / Revenue
Answer: A

55. A major decision area in Operations Management includes:
A. Facility location
B. Promotion mix
C. Financial investment
D. Tax planning
Answer: A

56. Facility location decision is critical because:
A. It affects cost, accessibility, and efficiency
B. It changes daily
C. It has no long-term impact
D. None
Answer: A

57. The process layout is most suitable for:
A. Job production
B. Mass production
C. Continuous production
D. None
Answer: A
Explanation: In job production, different processes are grouped by function (e.g., hospital, repair shop).*

58. Product layout is ideal when:
A. Volume is high and variety is low
B. Volume is low and variety is high
C. Demand is uncertain
D. None
Answer: A

59. A fixed-position layout is used in:
A. Shipbuilding and construction projects
B. Assembly lines
C. Textile manufacturing
D. None
Answer: A

60. “Cellular Layout” combines advantages of:
A. Process and Product Layout
B. Job and Project Layout
C. Fixed and Functional Layout
D. None
Answer: A

SECTION G – ENTERPRISE RESOURCE PLANNING (ERP)

61. ERP stands for:
A. Enterprise Resource Planning
B. Enterprise Research Process
C. Efficient Resource Planning
D. None
Answer: A

62. ERP is best described as:
A. An integrated information system covering all functional areas
B. A financial planning tool
C. A marketing strategy
D. A manufacturing technique
Answer: A

63. The core modules of ERP typically include:
A. Finance, HR, Production, SCM, CRM
B. Advertising and Design
C. Tax and Audit
D. None
Answer: A

64. Which of the following is an ERP vendor?
A. SAP
B. Oracle
C. Microsoft Dynamics
D. All of the above
Answer: D

65. A major challenge in ERP implementation is:
A. Resistance to change
B. System integration
C. High cost
D. All of the above
Answer: D

66. ERP improves decision-making by:
A. Providing real-time data and integrated reports
B. Isolating departments
C. Reducing transparency
D. None
Answer: A

67. The first step in ERP implementation is:
A. Requirement analysis and planning
B. Testing
C. Data migration
D. Training
Answer: A

68. One of the main benefits of ERP is:
A. Reduced redundancy and duplication of data
B. Increased manual work
C. Fragmented systems
D. None
Answer: A

69. ERP integrates:
A. Information across departments
B. Competitors’ data
C. Market research
D. None
Answer: A

70. ERP’s biggest advantage in supply chain management is:
A. Visibility of inventory and orders
B. Advertisement design
C. Labour reduction
D. None
Answer: A

🔹 SECTION H – SCHEDULING, SEQUENCING & MONITORING

71. Scheduling refers to:
A. Determining when and in what order jobs are performed
B. Assigning machines to workers
C. Estimating production cost
D. None
Answer: A

72. Loading is:
A. Assigning jobs to specific machines or departments
B. Recording sales
C. Maintenance activity
D. None
Answer: A

73. Sequencing is about:
A. Deciding priority of jobs
B. Setting targets
C. Estimating time
D. None
Answer: A

74. The rule “SPT” in sequencing means:
A. Shortest Processing Time
B. Standard Production Target
C. Sequential Process Time
D. None
Answer: A

75. Monitoring in production control ensures:
A. Adherence to schedule and corrective actions
B. Pricing policy
C. Quality audit only
D. None
Answer: A

76. “Dispatching” in production control refers to:
A. Issuing work orders to start operations
B. Planning demand
C. Procurement
D. None
Answer: A

77. Which of the following minimizes average job flow time?
A. SPT rule
B. FCFS rule
C. EDD rule
D. LPT rule
Answer: A

78. A Gantt chart is used for:
A. Scheduling and progress tracking
B. Statistical analysis
C. Regression analysis
D. None
Answer: A

79. Bottleneck operations are:
A. Work centers limiting system capacity
B. Unused machines
C. Idle resources
D. None
Answer: A

80. Effective scheduling results in:
A. Improved utilization, reduced idle time, timely delivery
B. Increased cost
C. Delayed production
D. None
Answer: A

SECTION I – QUALITY MANAGEMENT

81. Quality means:
A. Fitness for intended purpose
B. High cost
C. Luxury
D. None
Answer: A

82. Statistical Quality Control (SQC) uses:
A. Control charts
B. Inventory models
C. Demand forecasting
D. None
Answer: A

83. Total Quality Management (TQM) emphasizes:
A. Continuous improvement and customer satisfaction
B. Inspection only
C. Reduction in workforce
D. None
Answer: A

84. Kaizen refers to:
A. Continuous small improvements
B. Employee layoffs
C. Major innovation
D. None
Answer: A

85. Benchmarking means:
A. Comparing performance with best practices
B. Copying competitors blindly
C. Auditing accounts
D. None
Answer: A

86. Six Sigma aims at:
A. Reducing defects to less than 3.4 per million opportunities
B. Achieving 100% output
C. Increasing production cost
D. None
Answer: A

87. The DMAIC cycle stands for:
A. Define, Measure, Analyze, Improve, Control
B. Develop, Manage, Audit, Implement, Correct
C. None
Answer: A

88. ISO 9000 is related to:
A. Quality management system standards
B. Environmental norms
C. Cost control
D. None
Answer: A

89. A Pareto chart identifies:
A. Major causes contributing most to problems (80/20 rule)
B. Average performance
C. Random variation
D. None
Answer: A

90. Control limits in control charts are set at:
A. ±3σ
B. ±1σ
C. ±2σ
D. None
Answer: A

SECTION J – OPERATIONS RESEARCH (OR)

91. Operations Research is:
A. Application of scientific methods to decision-making
B. Human resource study
C. Market analysis
D. None
Answer: A

92. Objective of Operations Research is:
A. Optimization of limited resources
B. Cost increase
C. Sales increase only
D. None
Answer: A

93. The transportation model aims to:
A. Minimize cost of shipping goods between sources and destinations
B. Forecast demand
C. Schedule maintenance
D. None
Answer: A

94. The initial feasible solution in transportation problems can be found by:
A. North-West Corner, Least Cost, VAM
B. Linear Regression
C. PERT
D. None
Answer: A

95. Queuing theory deals with:
A. Waiting lines and service systems
B. Inventory planning
C. Facility layout
D. None
Answer: A

96. Arrival rate (λ) and service rate (μ) are parameters in:
A. Queuing model
B. Regression model
C. Inventory model
D. None
Answer: A

97. Decision theory helps in:
A. Selecting best alternative under risk or uncertainty
B. Marketing segmentation
C. Labour planning
D. None
Answer: A

98. In PERT, expected time is calculated as:

$t_{e} = \frac{a + 4 m + b}{6}$

A. True
B. False
Answer: A

99. Critical path in CPM represents:
A. Longest path determining project duration
B. Shortest path
C. Average path
D. None
Answer: A

100. Slack time indicates:
A. Maximum delay possible without affecting project completion
B. Idle labour
C. Cost overrun
D. None
Answer: A

November 11, 2025

UGC NET MBA Unit-8

Statistics for Management, Operations, and Operations Research

PART 1: STATISTICS FOR MANAGEMENT

1. Concept and Scope

Statistics is both a science and an art of collecting, classifying, presenting, analyzing, and interpreting numerical data to aid rational decision-making under uncertainty.

It provides quantitative foundations for managerial functions such as planning, control, and forecasting.

Branches of Statistics

Descriptive Statistics – summarizing data through tables, charts, and averages.
Inferential Statistics – drawing conclusions about populations from samples using probability theory.

Role of Statistics in Management

Marketing: Market surveys, consumer behaviour analysis.
Finance: Portfolio risk analysis, stock price movements.
Production: Quality control, forecasting demand.
HR: Wage analysis, performance evaluation.
Operations: Scheduling, process optimization.

2. Data and Its Types

A. Based on Source

Primary Data: Collected first-hand for a specific study (surveys, interviews).
Secondary Data: Collected earlier for another purpose (reports, journals, databases).

B. Based on Nature

Qualitative (Attribute): Categorical (e.g., gender, brand preference).
Quantitative (Variable): Numeric (e.g., income, profit).

C. Based on Measurement Scale

Scale	Meaning	Example
Nominal	Classification only	Gender, religion
Ordinal	Rank order	Satisfaction level
Interval	Equal intervals, no true zero	Temperature (°C)
Ratio	True zero and intervals	Sales, weight

🟩 3. MEASURES OF CENTRAL TENDENCY

Central tendency expresses the “typical” or “representative” value of a dataset.

A. Arithmetic Mean

$\overset{ˉ}{X} = \frac{\sum X}{N}$

For grouped data:

$\overset{ˉ}{X} = \frac{\sum f X}{\sum f}$

Merits: Simple, algebraically tractable.
Limitations: Affected by extreme values.

B. Median

The middle value when data are arranged in order.

$Median = L + \frac{(\frac{N}{2} - C F)}{f} \times h$

Less affected by outliers.
Appropriate for skewed data.

C. Mode

Most frequent value.
For grouped data:

$Mode = L + \frac{(f_{1} - f_{0})}{(2 f_{1} - f_{0} - f_{2})} \times h$

Used for qualitative data like brand or color preference.

D. Relationship among Mean, Median, and Mode

$Mode = 3 (Median) - 2 (Mean)$

Useful for estimating one measure from the other two.

🟩 4. MEASURES OF DISPERSION

Dispersion measures how values deviate from the average — indicating consistency or risk.

Measure	Formula	Interpretation
Range	Max − Min	Simple measure of spread
Quartile Deviation (Q.D.)	(Q₃ − Q₁) / 2	Dispersion of middle 50%
Mean Deviation (M.D.)	(\frac{\sum	X – \bar{X}
Variance (σ²)	$\frac{\sum (X - \overset{ˉ}{X})^{2}}{N}$	Fundamental for inferential statistics
Standard Deviation (σ)	√Variance	Most widely used measure
Coefficient of Variation (CV)	$\frac{σ}{\overset{ˉ}{X}} \times 100$	Compare variability between datasets

Example:
Dataset A: Mean = 50, SD = 10 → CV = 20%
Dataset B: Mean = 80, SD = 16 → CV = 20%
→ Both have equal relative variability.

🟩 5. PROBABILITY AND PROBABILITY DISTRIBUTIONS

A. Concept of Probability

Probability quantifies the likelihood of occurrence of an event.

$P (A) = \frac{Favourable outcomes}{Total outcomes}$

Range: 0 ≤ P(A) ≤ 1

P(A) = 1: Certain event
P(A) = 0: Impossible event

B. Rules of Probability

Addition Rule:
If A and B are mutually exclusive,

$P (A \cup B) = P (A) + P (B)$
Multiplication Rule:
For independent events,

$P (A \cap B) = P (A) \times P (B)$
Conditional Probability:

$P (A ∣ B) = \frac{P (A \cap B)}{P (B)}$

C. Probability Distributions

(1) Binomial Distribution

Discrete distribution used for number of successes in n independent trials.

$P (X = x) = (\binom{n}{x}) p^{x} (1 - p)^{n - x}$

Mean = np
Variance = np(1−p)

Example: Probability of 3 defective bulbs out of 10 when defect rate = 0.1.

(2) Poisson Distribution

For rare events (e.g., accidents per day).

$P (X = x) = \frac{e^{- m} m^{x}}{x!}$

Mean = Variance = m

Used when n → large, p → small, and np = constant.

(3) Normal Distribution

Continuous, bell-shaped curve.

$f (x) = \frac{1}{σ \sqrt{2 π}} e^{- \frac{(x - μ)^{2}}{2 σ^{2}}}$

Properties:

Symmetrical about mean.
68.26% within ±1σ, 95.45% within ±2σ, 99.73% within ±3σ.
Used in hypothesis testing and control charts.

(4) Exponential Distribution

Used to model time between events (e.g., waiting time).

$f (x) = λ e^{- λ x}, x \geq 0$

Mean = 1/λ
Variance = 1/λ²

🟩 6. DATA COLLECTION AND QUESTIONNAIRE DESIGN

Data Collection

Primary: Through direct observation, survey, or experimentation.
Secondary: Government reports, journals, internet sources.

Questionnaire Design

Define objectives
Select information to collect
Choose question type:
- Open-ended (qualitative insights)
- Closed-ended (quantitative analysis)
Logical sequencing (easy → complex)
Pilot testing and revision

Common Mistakes: Ambiguous wording, double-barreled questions, poor scaling.

🟩 7. SAMPLING THEORY

A. Basic Concepts

Population (Universe): Entire group under study
Sample: Representative subset
Sampling Unit: Element from which data is collected

B. Steps in Sampling Process

Define population
Select sampling frame
Decide sample size
Choose technique
Collect and analyze

C. Probability Sampling Techniques

Method	Description	When to Use
Simple Random	Equal chance for each unit	Small, homogeneous population
Systematic	Every kth item selected	Sequential data
Stratified	Population divided into strata, then random sample	Heterogeneous population
Cluster	Dividing into clusters, sampling entire cluster	Wide geographical dispersion

D. Non-Probability Sampling Techniques

Method	Description
Convenience	Easy to reach sample (quick but biased)
Judgmental	Based on researcher’s expertise
Quota	Fixed proportion from categories
Snowball	Existing respondents recruit new ones

🟩 8. HYPOTHESIS TESTING

A. Key Definitions

Parameter: Numerical summary of population (μ, σ²).
Statistic: Calculated from sample (x̄, s²).

Goal: Use sample data to infer about population.

B. Hypothesis Types

Null Hypothesis (H₀): No significant difference.
Alternative Hypothesis (H₁): Significant difference exists.

Errors:

Type I (α): Rejecting true H₀
Type II (β): Accepting false H₀

C. Testing Steps

Formulate H₀ and H₁
Choose significance level (α = 0.05 or 0.01)
Choose appropriate test statistic (Z, t, F, χ²)
Compute test statistic
Compare with critical value
Draw conclusion

D. Parametric Tests

Test	Application	Condition
Z-Test	Large samples, known σ	n > 30
t-Test	Small samples, unknown σ	n < 30
F-Test	Compare variances	Ratio test
Paired t-Test	Before–after comparison	Related samples

E. Non-Parametric Test

Chi-Square (χ²) Test:

$χ^{2} = \sum \frac{(O - E)^{2}}{E}$

Used for testing independence or goodness of fit.

Example: Relationship between gender and brand preference.

🟩 9. CORRELATION AND REGRESSION

A. Correlation

Measures strength and direction of linear relationship.

Karl Pearson’s Coefficient (r):

$r = \frac{Σ (X - \overset{ˉ}{X}) (Y - \overset{ˉ}{Y})}{\sqrt{Σ (X - \overset{ˉ}{X})^{2} Σ (Y - \overset{ˉ}{Y})^{2}}}$

Range: -1 to +1
r = +1 → Perfect positive
r = -1 → Perfect negative

B. Rank Correlation (Spearman’s ρ)

$ρ = 1 - \frac{6 Σ D^{2}}{n (n^{2} - 1)}$

Used when data are ordinal (ranks).

C. Regression Analysis

Used to predict value of dependent variable (Y) from independent variable (X).

Simple Linear Regression:

$Y = a + b X$

where
$b = \frac{Σ (X - \overset{ˉ}{X}) (Y - \overset{ˉ}{Y})}{Σ (X - \overset{ˉ}{X})^{2}}$

Multiple Regression:
$Y = a + b_{1} X_{1} + b_{2} X_{2} + . . . + b_{n} X_{n}$

🟩 10. OPERATIONS MANAGEMENT

A. Concept

Operations Management deals with conversion of inputs (materials, labour, capital, information) into outputs (goods/services) efficiently.

B. Functions

Product design & process selection
Plant layout and facility location
Capacity planning
Scheduling and inventory control
Maintenance and quality management

C. Objectives

Improve productivity
Optimize resources
Ensure quality and timely delivery
Minimize cost

🟩 11. FACILITY LOCATION AND LAYOUT

Facility Location

The strategic decision of choosing where to situate production or service facilities.

Quantitative Methods:

Centre of Gravity Method: minimizes transport cost
Break-even Analysis: compares fixed and variable cost by site

Plant Layout

Arrangement of machines, departments, or work areas.

Type	Features	Example
Product Layout	Line flow, high volume	Automobile plant
Process Layout	Functional grouping	Hospitals
Fixed Position	Product remains stationary	Shipbuilding
Cellular Layout	Hybrid for efficiency	Electronics plant

🟩 12. ENTERPRISE RESOURCE PLANNING (ERP)

A. Concept

ERP integrates core business functions through a central database.

ERP Modules:

Finance
HR
Production
SCM
CRM

B. ERP Implementation Steps

Project planning
Requirement analysis
System design & customization
Data migration
Training
Testing & Go-live

Benefits: Integration, transparency, faster reporting.
Challenges: High cost, change resistance, data migration errors.

🟩 13. PRODUCTION SCHEDULING AND CONTROL

A. Loading: Assigning jobs to machines.

B. Scheduling: Determining when and in what sequence jobs are processed.

C. Sequencing: Prioritizing jobs (rules: FCFS, SPT, EDD).

D. Monitoring: Tracking progress, revising schedules.

🟩 14. QUALITY MANAGEMENT

A. Quality Concepts

Quality = fitness for purpose.
Quality management ensures that output meets customer expectations.

B. Statistical Quality Control (SQC)

Uses control charts (mean, range, p-chart, c-chart) to monitor process variation.

C. Total Quality Management (TQM)

An organization-wide philosophy emphasizing continuous improvement and customer satisfaction.

Principles:

Customer orientation
Continuous improvement (Kaizen)
Employee involvement
Scientific decision-making

D. Quality Tools

Control Charts
Fishbone Diagram (Ishikawa)
Pareto Analysis (80/20 Rule)
Check Sheets, Histograms, Scatter Diagrams

E. Kaizen

Continuous small improvements involving all employees.

F. Benchmarking

Comparing performance with best-in-class organizations.

G. Six Sigma

A disciplined methodology targeting defect reduction to 3.4 defects per million opportunities (DPMO).
Focuses on DMAIC cycle – Define, Measure, Analyze, Improve, Control.

H. ISO 9000 Series

Global quality management system standards (documentation, process control, auditing).

🟩 15. OPERATIONS RESEARCH (OR)

Definition

Operations Research applies scientific and mathematical models to managerial decision-making for optimization of limited resources.

Applications:

Production scheduling
Inventory control
Transportation and distribution
Network planning
Queuing and service design

A. Transportation Problem

Objective: Minimize total cost of shipping goods.

$Z = \sum_{i = 1}^{m} \sum_{j = 1}^{n} C_{i j} X_{i j}$

Methods:

Initial solution → North-West Corner, Least Cost, Vogel’s Approximation (VAM).
Optimality test → MODI method.

B. Queuing Theory

Studies waiting line systems.

Parameters:
λ = Arrival rate, μ = Service rate
System utilization: ρ = λ / μ
Objective → minimize waiting cost + service cost.

C. Decision Theory

Used when decisions must be made under risk or uncertainty.

Criteria:

Maximax (optimistic)
Maximin (pessimistic)
Minimax regret (Savage)
Expected monetary value (probabilistic)

D. PERT / CPM (Project Scheduling)

Technique	Time Estimate	Nature
PERT	Probabilistic (a, m, b)	Uncertain projects
CPM	Deterministic	Routine projects

PERT Expected Time:

$t_{e} = \frac{a + 4 m + b}{6}$

Variance $= {(\frac{b - a}{6})}^{2}$

Critical Path: Longest path through network; determines project duration.
Slack Time: $L S T - E S T$ → available delay time.

November 11, 2025

Tag: Statistics for Management

UGC NET MBA Unit-8 MCQs

Statistics for Management, Operations, and Operations Research

SECTION A – STATISTICS: CONCEPTS & DESCRIPTIVE ANALYSIS

🔹 SECTION B – DISPERSION & PROBABILITY DISTRIBUTIONS

🔹 SECTION C – SAMPLING AND QUESTIONNAIRE DESIGN

🔹 SECTION D – HYPOTHESIS TESTING

🔹 SECTION E – CORRELATION & REGRESSION

SECTION F – OPERATIONS MANAGEMENT

SECTION G – ENTERPRISE RESOURCE PLANNING (ERP)

🔹 SECTION H – SCHEDULING, SEQUENCING & MONITORING

SECTION I – QUALITY MANAGEMENT

SECTION J – OPERATIONS RESEARCH (OR)

UGC NET MBA Unit-8

Statistics for Management, Operations, and Operations Research

PART 1: STATISTICS FOR MANAGEMENT

1. Concept and Scope

Branches of Statistics

Role of Statistics in Management

2. Data and Its Types

🟩 3. MEASURES OF CENTRAL TENDENCY

A. Arithmetic Mean

B. Median

C. Mode

D. Relationship among Mean, Median, and Mode

🟩 4. MEASURES OF DISPERSION

🟩 5. PROBABILITY AND PROBABILITY DISTRIBUTIONS

A. Concept of Probability

B. Rules of Probability

C. Probability Distributions

(1) Binomial Distribution

(2) Poisson Distribution

(3) Normal Distribution

(4) Exponential Distribution

🟩 6. DATA COLLECTION AND QUESTIONNAIRE DESIGN

Data Collection

Questionnaire Design

🟩 7. SAMPLING THEORY

A. Basic Concepts

B. Steps in Sampling Process

C. Probability Sampling Techniques

D. Non-Probability Sampling Techniques

🟩 8. HYPOTHESIS TESTING

A. Key Definitions

B. Hypothesis Types

C. Testing Steps

D. Parametric Tests

E. Non-Parametric Test

🟩 9. CORRELATION AND REGRESSION

A. Correlation

B. Rank Correlation (Spearman’s ρ)

C. Regression Analysis

🟩 10. OPERATIONS MANAGEMENT

A. Concept

B. Functions

C. Objectives

🟩 11. FACILITY LOCATION AND LAYOUT

Facility Location

Plant Layout

🟩 12. ENTERPRISE RESOURCE PLANNING (ERP)

A. Concept

B. ERP Implementation Steps

🟩 13. PRODUCTION SCHEDULING AND CONTROL

A. Loading: Assigning jobs to machines.

B. Scheduling: Determining when and in what sequence jobs are processed.

C. Sequencing: Prioritizing jobs (rules: FCFS, SPT, EDD).

D. Monitoring: Tracking progress, revising schedules.

🟩 14. QUALITY MANAGEMENT

A. Quality Concepts

B. Statistical Quality Control (SQC)

C. Total Quality Management (TQM)

D. Quality Tools

E. Kaizen

F. Benchmarking

G. Six Sigma

H. ISO 9000 Series

🟩 15. OPERATIONS RESEARCH (OR)

Definition

Applications:

A. Transportation Problem