BUSINESS STATISTICS SYLLABUS CBCS PATTERN
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Business Statistics Complete Syllabus of Dibrugarh, Gauhati and Assam
University. Also Syllabus of IGNOU B.Com and syllabus prescribed by UGC are
also added.
Business Statistics complete Notes, Multiple
choice questions and answers and solved papers will be uploaded very soon.
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4. IGNOU B.Com 2nd Sem Syllabus 5. Business Statistics Syllabus as per UGC Guidelines 6. Business Statistics Complete Notes Also Read: Business Statistics Multiple Choice Questions and Answers (MCQs) |
DIBRUGARH UNIVERSITY
B.COM 3rd SEM SYLLABUS (Hons.)
B.Com. (Hons.): (CBCS) Semester - III
Paper – G 303: BUSINESS STATISTICS Full Marks:
100 (Internal Assessment 20 + 80 End-Term)
Lectures: 45, Practical: 26 Hours, Tutorial: 7
Hrs
Objective:
The objective of this course is to familiarise students with the basic
statistical tools used for managerial decision-making.
Unit
1: Statistical Data and Descriptive Statistics 7 L + 1 T (Marks: 10)
a. Nature and Classification of data: univariate, bivariate and
multivariate data; time-series and cross-sectional data
b. Measures of Central Tendency
i. Mathematical averages including arithmetic mean geometric mean
and harmonic mean. Properties and applications.
ii. Positional Averages Mode and Median (and other partition
values including quartiles, deciles, and percentiles) (including graphic
determination)
c. Measures of Variation: absolute and relative. Range, quartile
deviation, mean deviation, standard deviation, and their coefficients,
Properties of standard deviation/variance
d. Skewness: Meaning, Measurement using Karl Pearson and Bowley’s
measures; Concept of Kurtosis.
Unit
2: Probability and Probability Distributions 9 L + 1 T (Marks: 16)
a. Theory of Probability. Approaches to the calculation of
probability; Calculation of event probabilities. Addition and multiplication
laws of probability (Proof not required); Conditional probability and Bayes’
Theorem (Proof not required)
b. Expectation and variance of a random variable
c. Probability distributions:
i. Binomial distribution: Probability distribution function,
Constants, Shape, Fitting of binomial distribution
ii. Poisson distribution: Probability function, (including Poisson
approximation to binomial distribution), Constants, Fitting of Poisson
distribution
iii. Normal distribution: Probability distribution function,
Properties of normal curve, Calculation of probabilities.
Unit
3: Simple Correlation and Regression Analysis 8 L + 1 T (Marks: 16)
a. Correlation Analysis: Meaning of Correlation: simple, multiple
and partial; linear and non-linear, Correlation and Causation, Scatter diagram,
Pearson’s co-efficient of correlation; calculation and properties (Proof not
required). Correlation and Probable error; Rank Correlation
b. Regression Analysis: Principle of least squares and regression
lines, Regression equations and estimation; Properties of regression
coefficients; Relationship between Correlation and Regression coefficients;
Standard Error of Estimate and its use in interpreting the results.
Unit
4: Index Numbers 8 L + 1 T (Marks: 16)
Meaning and uses of index numbers; Construction of index numbers:
fixed and chain base: univariate and composite. Aggregative and average of
relatives – simple and weighted.
Tests of adequacy of index numbers, Base shifting, splicing and
deflating.
Problems in the construction of index numbers; Construction of
consumer price indices: Important share price indices, including BSE SENSEX and
NSE NIFTY.
Also Read: Index Number Complete Notes
Unit
5: Time Series Analysis 8 L + 1 T (Marks: 14)
Components of time series; Additive and multiplicative models;
Trend analysis: Fitting of trend line using principle of least squares –
linear, second degree parabola and exponential.
Conversion of annual linear trend equation to quarterly/monthly
basis and vice-versa; Moving averages;
Seasonal variations: Calculation of Seasonal Indices using Simple
averages, Ratio-to-trend, and Ratio-to-moving averages methods. Uses of
Seasonal Indices.
UNIT
6: Sampling Concepts, Sampling Distributions and Estimation: 5 L + 1 T (Marks:
8)
Sampling: Populations and samples, Parameters and Statistics,
Descriptive and inferential statistics; Sampling methods (including Simple
Random sampling, Stratified sampling, Systematic sampling, Judgement sampling,
and Convenience sampling)
Concept of Sampling distributions and Theory of Estimation: Point
and Interval estimation of means (large samples) and proportions.
Practical Lab: 26 The
students will be familiarized with software (Spreadsheet and/or SPSS) and the
statistical and other functions contained therein related to form action of
frequency distributions and calculation of averages, measures of Dispersion and
variation, correlation and regression coefficient.
Important Note:
1. There shall be 4 Credit Hrs. for Lectures + one Credit hr. (Two
Practical Periods per week per batch) for Practical Lab + one credit Hr for
Tutorials (per group)
2. Latest edition of text books may be used.
Suggested Readings:
1. Levin, Richard, David S. Rubin, Sanjay Rastogi, and HM
Siddiqui. Statistics for Management. 7th ed., Pearson Education.
2. David M. Levine, Mark L. Berenson, Timothy C. Krehbiel, P. K.
Viswanathan, Business Statistics: A First Course, Pearson Education.
3. Siegel Andrew F. Practical Business Statistics. McGraw Hill
Education.
4. Gupta, S.P., and Archana Agarwal. Business Statistics, Sultan Chand
and Sons, New Delhi.
5. Vohra N. D., Business Statistics, McGraw Hill Education.
6. Murray R Spiegel, Larry J. Stephens, Narinder Kumar. Statistics
(Schaum’s Outline Series), McGraw Hill Education.
7. Gupta, S.C. Fundamentals of Statistics. Himalaya Publishing
House.
8. Anderson, Sweeney, and Williams, Statistics for Students of Economics and Business, Cengage Learning.
GAUHATI UNIVERSITY
B.COM 3rd SEM SYLLABUS
COM-GE-3046(A): BUSINESS STATISTICS
Marks: 100 Credit: 6 Lectures: 65 (☝BackToTop☝)
Objective: The objective of this course is to familiarise
students with the basic statistical tools used for managerial decision-making.
Unit 1: Statistical Data and Descriptive Statistics (12
Lectures)
a. Nature and
Classification of data: univariate, bivariate and multivariate data;
time-series and cross-sectional data
b. Measures of Central
Tendency
i. Mathematical averages
including arithmetic mean geometric mean and harmonic mean. Properties and
applications.
ii. Positional Averages
Mode and Median (and other partition values including quartiles, deciles, and
percentiles).
c. Measures of Variation:
absolute and relative. Range, quartile deviation, mean deviation, standard
deviation, and their coefficients, Properties of standard deviation/variance
d. Skewness: Meaning,
Measurement using Karl Pearson and Bowley’s measures; Concept of Kurtosis
Unit 2: Probability and Probability Distributions (12
Lectures)
a. Theory of Probability.
Approaches to the calculation of probability; Calculation of event
probabilities. Addition and multiplication laws of probability (Proof not
required); Conditional probability
b. Expectation and
variance of a random variable, Probability distribution of random variable.
c. Probability
distributions:
i. Binomial distribution:
Probability distribution function, Constants, calculation for simple exercise
ii. Poisson distribution:
Probability function, (including Poisson approximation to binomial
distribution), Constants, Solution of related problems.
iii. Normal distribution:
Probability distribution function, Properties of normal curve (Theory Part
only)
Unit 3: Simple Correlation and Regression Analysis (12
Lectures)
a. Correlation Analysis:
Meaning of Correlation: simple, multiple and partial; linear and non-linear,
Correlation and Causation, Scatter diagram, Pearson’s co-efficient of
correlation; calculation and properties (Proof not required). Rank Correlation,
Interpretation of various values of correlation co-efficient.
b. Regression Analysis:
Principle of least squares and regression lines, Regression equations and
estimation; Properties of regression coefficients; Relationship between
Correlation and Regression coefficients;
Unit 4: Index Numbers (12 Lectures)
Meaning and uses of index
numbers; Idea of price – relative, Price, Quantity and Value indices.
Construction of index
numbers: Laspeyere’s, Paasche’s and fisher’s indices-Aggregative and average of
relatives (simple and weighted).
Problems in the
construction of index numbers, Tests of adequacy of index numbers- Time
reversal test and Factor reversal test.
Deflating and
Construction of consumer price indices, chain base index and limitation of
index number.
Unit 5: Time Series Analysis (7 Lectures)
Components of time
series; Additive and multiplicative models; Trend analysis: Fitting of trend
line using principle of least squares – linear case.
Determination of trend by
semi- average and moving average. Uses of Time Series analysis.
UNIT 6: Sampling Concepts, Sampling Distributions,
Estimation and testing of Hypothesis (10 Lectures)
Sampling: Populations and
samples, Parameters and Statistic, Census vs Sampling.
Sampling methods
(including Simple Random sampling, Stratified sampling, Systematic sampling,
Judgment sampling, and Convenience sampling)
Concept of Sampling
distributions and Estimation: Point and Interval estimation of means (large
samples) and sample proportion. Characteristics of a good estimation.
Testing of hypothesis-
concepts of Null hypothesis, alternative hypothesis, level of significance,
test of significance, one- tailed and two- tailed test and errors in testing
hypothesis.
Suggested Readings:-
1. Gupta, S.C,
Fundamentals of statistics – Himalaya Publishing House.
2. Murray, R Spiegel,
Larry J. Stephens , Narinder Kumar. Statistics (Schaum’s Outline Series)
3. Hazarika, Padmalochan,
Business Statistics – S.Chand
4. Bhowal, M.K.
Fundamentals of Business Statistics (Asian Books Private Limited)
ASSAM UNIVERSITY B.COM 3rd SEM
SYLLABUS
B.Com. (Hons.): Semester III
Paper BCH 3.4: BUSINESS STATISTICS
Marks: 100 Theory: 70 Practical: 30
Internal Assessment: 20 Term End Exam: 50
(Lectures: 52, Practical: 26) (☝BackToTop☝)
Objective:
The objective of this course is to familiarise students with the basic
statistical tools used for managerial decision-making.
Unit
1: Statistical Data and Descriptive Statistics 7 L + 1 T (Marks: 10)
a. Nature and Classification of data: univariate, bivariate and
multivariate data; time-series and cross-sectional data
b. Measures of Central Tendency
i. Mathematical averages including arithmetic mean geometric mean
and harmonic mean. Properties and applications.
ii. Positional Averages Mode and Median (and other partition
values including quartiles, deciles, and percentiles) (including graphic
determination)
c. Measures of Variation: absolute and relative. Range, quartile
deviation, mean deviation, standard deviation, and their coefficients,
Properties of standard deviation/variance
d. Skewness: Meaning, Measurement using Karl Pearson and Bowley’s
measures; Concept of Kurtosis.
Unit
2: Probability and Probability Distributions 9 L + 1 T (Marks: 16)
a. Theory of Probability. Approaches to the calculation of
probability; Calculation of event probabilities. Addition and multiplication
laws of probability (Proof not required); Conditional probability and Bayes’
Theorem (Proof not required)
b. Expectation and variance of a random variable
c. Probability distributions:
i. Binomial distribution: Probability distribution function,
Constants, Shape, Fitting of binomial distribution
ii. Poisson distribution: Probability function, (including Poisson
approximation to binomial distribution), Constants, Fitting of Poisson
distribution
iii. Normal distribution: Probability distribution function,
Properties of normal curve, Calculation of probabilities.
Unit
3: Simple Correlation and Regression Analysis 8 L + 1 T (Marks: 16)
a. Correlation Analysis: Meaning of Correlation: simple, multiple
and partial; linear and non-linear, Correlation and Causation, Scatter diagram,
Pearson’s co-efficient of correlation; calculation and properties (Proof not
required). Correlation and Probable error; Rank Correlation
b. Regression Analysis: Principle of least squares and regression
lines, Regression equations and estimation; Properties of regression
coefficients; Relationship between Correlation and Regression coefficients;
Standard Error of Estimate and its use in interpreting the results.
Unit
4: Index Numbers 8 L + 1 T (Marks: 16)
Meaning and uses of index numbers; Construction of index numbers:
fixed and chain base: univariate and composite. Aggregative and average of
relatives – simple and weighted.
Tests of adequacy of index numbers, Base shifting, splicing and
deflating.
Problems in the construction of index numbers; Construction of
consumer price indices: Important share price indices, including BSE SENSEX and
NSE NIFTY.
Unit
5: Time Series Analysis 8 L + 1 T (Marks: 14)
Components of time series; Additive and multiplicative models;
Trend analysis: Fitting of trend line using principle of least squares –
linear, second degree parabola and exponential.
Conversion of annual linear trend equation to quarterly/monthly
basis and vice-versa; Moving averages;
Seasonal variations: Calculation of Seasonal Indices using Simple
averages, Ratio-to-trend, and Ratio-to-moving averages methods. Uses of
Seasonal Indices.
UNIT
6: Sampling Concepts, Sampling Distributions and Estimation: 5 L + 1 T (Marks:
8)
Sampling: Populations and samples, Parameters and Statistics,
Descriptive and inferential statistics; Sampling methods (including Simple
Random sampling, Stratified sampling, Systematic sampling, Judgement sampling,
and Convenience sampling)
Concept of Sampling distributions and Theory of Estimation: Point
and Interval estimation of means (large samples) and proportions.
Practical – 30: The students will be familiarized with software
(MS-Excel) and the statistical and other functions contained therein related to
formation of frequency distributions and calculation of averages, measures of
Dispersion and variation, correlation and regression co-efficient.
Note:
1. There shall be 4
Credit Hrs. for Lectures + one Credit Hr. (Two Practical Periods per week per
batch) for Practical Lab + one Credit Hr. for Tutorials (per group)
2. Latest Edition of text
books may be used.
Suggested Readings:
1. Levin, Richard, David
S. Rubin, Sanjay Rastogi, and HM Siddiqui. Statistics for Management. 7th ed.,
Pearson Education.
2. David M. Levine, Mark
L. Berenson, Timothy C. Krehbiel, P.K. Viswanathan, Business Statistics: A
First Course, Pearson Education.
3. Siegel Andrew F.
Practical Business Statistics. McGraw Hill Education.
4. Gupta, S.P., and
Archana Agarwal. Business Statistics, Sultan Chand and Sons, New Delhi.
5. Vohra N.D., Business
Statistics, McGraw Hill Education.
6. Murray R Spiegel,
Larry J. Stephens, Narinder Kumar. Statistics (Schaum’s Outline Series), McGraw
Hill Education.
7. Anderson, Sweeney, and
Williams, Statistics for Students of Economics and Business, Cengage Learning
IGNOU B.Com 2nd
(Second) Semester Syllabus
BCOC-134: BUSINESS MATHEMATICS AND STATISTICS (Core Course
– 6 Credits)
PART-A: BUSINESS MATHEMATICS (☝BackToTop☝)
BLOCK 1: MATRICES
Unit 1: Introduction to
Matrices
Unit 2: Determinants
Unit 3: Inverse of
Matrices
Unit 4: Applications of
Matrices in Business and Economics
BLOCK 2: DIFFERENTIALS CALCULUS
Unit 5: Mathematics
Functions and Types
Unit 6: Limit and
Continuity
Unit 7: Differentiations
Unit 8: Maxima and Minima
Functions
Unit 9: Applications of
Differentials
BLOCK 3: BASIC MATHEMATICS OF FINANCE
Unit 10: Interest Rates
Unit 11: Compounding and
Discounting
BLOCK 4: UNI – VARIATE ANALYSIS
Unit 12: Introduction to
Statistics
Unit 13: Measures of
Central Tendency
Unit 14: Measures of
Dispersion
BLOCK 5: BI-VARIATE ANALYSIS
Unit 15: Simple Linear
Correlation
Unit 16: Simple Linear
Regression
BLOCK 6 INDEX NUMBERS AND TIME SERIES ANALYSIS
Unit 17: Index Numbers
Unit 18: Times Series
Syllabus Prescribed
by UGC (University Grant Commission)
B.Com. (Hons.): Semester - III
Paper – BCH 3.4: BUSINESS STATISTICS
Duration: 3 hrs. Marks: 100 Lectures: 52, Practical Lab: 26 (☝BackToTop☝)
Objective: The objective of this course is to familiarise
students with the basic statistical tools used for managerial decision-making.
Unit 1:
Statistical Data and Descriptive Statistics (9 Lectures)
a. Nature and Classification of data:
univariate, bivariate and multivariate data; time-series and cross-sectional
data
b. Measures of Central Tendency
i. Mathematical averages including
arithmetic mean, geometric mean and harmonic mean. Properties and applications.
ii. Positional Averages Mode and
Median (and other partition values including quartiles, deciles, and
percentiles) (including graphic determination)
c. Measures of Variation: absolute
and relative. Range, quartile deviation, mean deviation, standard deviation,
and their coefficients, Properties of standard deviation/variance
d. Skewness: Meaning, Measurement
using Karl Pearson and Bowley’s measures; Concept of Kurtosis
Unit 2:
Probability and Probability Distributions (10 Lectures)
a. Theory of Probability. Approaches
to the calculation of probability; Calculation of event probabilities. Addition
and multiplication laws of probability (Proof not required); Conditional
probability and Bayes’ Theorem (Proof not required)
b. Expectation and variance of a
random variable
c. Probability distributions:
i. Binomial
distribution: Probability distribution function, Constants, Shape, Fitting of
binomial distribution
ii. Poisson
distribution: Probability function, (including Poisson approximation to
binomial distribution), Constants, Fitting of Poisson distribution
iii. Normal
distribution: Probability distribution function, Properties of normal curve,
Calculation of probabilities
Unit 3: Simple Correlation and Regression Analysis (10
Lectures)
a. Correlation Analysis:
Meaning of Correlation: simple, multiple and partial; linear and non-linear,
Correlation and Causation, Scatter diagram, Pearson’s co-efficient of correlation;
calculation and properties (Proof not required). Correlation and Probable
error; Rank Correlation
b. Regression Analysis:
Principle of least squares and regression lines, Regression equations and
estimation; Properties of regression coefficients; Relationship between
Correlation and Regression coefficients; Standard Error of Estimate and its use
in interpreting the results.
Unit 4:
Index Numbers (10 Lectures)
Meaning and uses of index
numbers; Construction of index numbers: fixed and chain base: univariate and
composite. Aggregative and average of relatives – simple and weighted Tests of
adequacy of index numbers, Base shifting, splicing and deflating.
Problems in the
construction of index numbers; Construction of consumer price indices:
Important share price indices, including BSE SENSEX and NSE NIFTY
Unit 5:
Time Series Analysis (8 Lectures)
Components of time
series; Additive and multiplicative models; Trend analysis: Fitting of trend
line using principle of least squares – linear, second degree parabola and
exponential.
Conversion of annual
linear trend equation to quarterly/monthly basis and vice-versa; Moving
averages;
Seasonal variations:
Calculation of Seasonal Indices using Simple averages, Ratio-to-trend, and
Ratio-to-moving averages methods. Uses of Seasonal Indices
UNIT 6:
Sampling Concepts, Sampling Distributions and Estimation: (5 Lectures)
Sampling: Populations and
samples, Parameters and Statistics, Descriptive and inferential statistics;
Sampling methods (including Simple Random sampling, Stratified sampling,
Systematic sampling, Judgement sampling, and Convenience sampling) Concept of
Sampling distributions and Theory of Estimation: Point and Interval estimation
of means (large samples) and proportions.
Practical Lab: 26 The students
will be familiarized with software (Spreadsheet and/or SPSS) and the
statistical and other functions contained therein related to formation of
frequency distributions and calculation of averages, measures of Dispersion and
variation, correlation and regression coefficient.
Note:
1. There shall be 4
Credit Hrs. for Lectures + one Credit hr. (Two Practical Periods per week per
batch) for Practical Lab + one credit Hr for Tutorials (per group)
2. Latest edition of text
books may be used.
Suggested Readings:
1. Levin, Richard, David
S. Rubin, Sanjay Rastogi, and HM Siddiqui. Statistics for Management. 7th ed.,
Pearson Education.
2. David M. Levine, Mark
L. Berenson, Timothy C. Krehbiel, P. K. Viswanathan, Business Statistics: A
First Course, Pearson Education.
3. Siegel Andrew F.
Practical Business Statistics. McGraw Hill Education.
4. Gupta, S.P., and
Archana Agarwal. Business Statistics, Sultan Chand and Sons, New Delhi.
5. Vohra N. D., Business
Statistics, McGraw Hill Education.
6. Murray R Spiegel,
Larry J. Stephens, Narinder Kumar. Statistics (Schaum’s Outline Series), McGraw
Hill Education.
7. Gupta, S.C.
Fundamentals of Statistics. Himalaya Publishing House.
8. Anderson, Sweeney, and
Williams
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