## BUSINESS STATISTICS SYLLABUS CBCS PATTERN

In this Page you will B.Com 3rd Sem 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.

 Click on the link below to get Business Statistics Syllabus 1. Dibrugarh University 2. Gauhati University 3. Assam University Also Read: Business Statistics Multiple Choice Questions and Answers (MCQs) (Coming Soon)

## DIBRUGARH UNIVERSITY B.COM 3rd SEM SYLLABUS (Hons.)B.Com. (Hons.): (CBCS) Semester - IIIPaper – 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.

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.

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 SYLLABUSCOM-GE-3046(A): BUSINESS STATISTICSMarks: 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.

1. Gupta, S.C, Fundamentals of statistics – Himalaya Publishing House.

2. Murray, R Spiegel, Larry J. Stephens , Narinder Kumar. Statistics (Schaum’s Outline Series)

4. Bhowal, M.K. Fundamentals of Business Statistics (Asian Books Private Limited)

## ASSAM UNIVERSITY B.COM 3rd SEM SYLLABUSB.Com. (Hons.): Semester III Paper BCH 3.4: BUSINESS STATISTICS Marks: 100 Theory: 70 Practical: 30Internal 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.

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 SyllabusBCOC-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 - IIIPaper – BCH 3.4: BUSINESS STATISTICSDuration: 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.

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