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**The Introductory Statistics for Economics (ECON 003) Course for BA Economics (Honours) UGCF Semester I, Delhi University has been taught by Mr. Dheeraj Suri. The Video Lectures are based upon the books prescribed by the University of Delhi. The Duration of Video Lectures is approximately 50 Hours.**

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**Exam Pattern**

**1. The following is distribution of topics, indicative weightage, and the amount of choice within each section**

**Section 1: Unit 1 and Unit 2: (weightage 20 marks), Two questions of 10 marks each with one question from Unit 1 and the other from Unit 2. Internal choice in these units should be given as 2 out of 3 questions****Section 2: Unit 3: (weightage 20 marks), Two questions out of Three for 10 marks each.****Section 3: Unit 4: weightage 20 marks), Two questions out of Three for 10 marks each.****Section 4: Unit 5: (weightage 15 marks), Three questions out of Four for 5 marks each.**

**2. There would be no compulsory question in any of the sections.**

**3. The internal assessment would comprise of 10 marks Class test, 10 marks Class test/assignment. Attendance will carry 05 marks.**

**4. the question paper will include internal choice in each section with limited number of sub-parts.**

**5. In order to achieve uniformity in evaluation of final answer scripts, it was decided to include the following note in final question paper:**

**All questions within each section are to be answered in a contiguous manner on the answer sheet. Start each question on a new page, and all sub-parts of a question should follow one after the other.****All intermediate calculations should be rounded off to 3 decimal places. The values provided in statistical tables should not be rounded off. All final calculations should be rounded off to two decimal places.**

**Demo Lectures**

**Demo Lectures****Demo Mock Tests**

**Demo Mock Tests****The Lectures are as per Latest Syllabus for Academic Session 2022-23**

**The Lectures are as per Latest Syllabus for Academic Session 2022-23****Course Content**** of Our ****Video Lectures**

**Course Content****of Our****Video Lectures****Unit – I : Introduction & Overview**

**Chapter 1 : Population & Sample**

**Duration of Lectures : 113 Minutes**

**Based Upon J L Devore Chapter 1.1 & 1.2**

**Topics Covered**

**►Distinction between Population & Sample, Univariate Bivariate & Multivariate data, Descriptive & Inferential Statistics, Enumerative and Analytical Studies,**

**►Distinction between Population & Sample, Univariate Bivariate & Multivariate data, Descriptive & Inferential Statistics, Enumerative and Analytical Studies,**

**►Descriptive & Inferential Statistics,**

**►Descriptive & Inferential Statistics,**

**►Stem & Leaf Displays,**

**►Stem & Leaf Displays,**

**►Dotplots,**

**►Dotplots,**

**►Histogram**

**►Histogram**

**Chapter 2 : Measures of Location**

**Duration of Lectures : 250 Minutes**

**Based Upon J L Devore Chapter 1.3**

**Topics Covered**

**►Frequency Series,**

**►Frequency Series,**

**►Arithmetic Mean, Properties of Arithmetic Mean, Combined Mean, Corrected Mean,**

**►Arithmetic Mean, Properties of Arithmetic Mean, Combined Mean, Corrected Mean,**

**►Median,**

**►Median,**

**►Trimmed Median,**

**►Trimmed Median,**

**►Mode,**

**►Mode,**

**►Partition Values **

**►Partition Values**

**Chapter 3 : Measures of Variability**

**Duration of Lectures : 203 Minutes**

**Based Upon J L Devore Chapter 1.4**

**Topics Covered**

**►Absolute and Relative Measures of Dispersion,**

**►Absolute and Relative Measures of Dispersion,**

**►Range & Coefficient of Range,**

**►Range & Coefficient of Range,**

**►Quartile Deviation & Coefficient of Quartile Deviation,**

**►Quartile Deviation & Coefficient of Quartile Deviation,**

**►Mean Deviation & Coefficient of Mean Deviation,**

**►Mean Deviation & Coefficient of Mean Deviation,**

**►Standard Deviation, Variance & Coefficient of Variation,**

**►Standard Deviation, Variance & Coefficient of Variation,**

**►Sample & Population Standard Deviation,**

**►Sample & Population Standard Deviation,**

**►Properties of Standard Deviation, Combined Standard Deviation,**

**►Properties of Standard Deviation, Combined Standard Deviation,**

**►Fourth Spread & Outliers**

**►Fourth Spread & Outliers**

**Unit – 2 : Elementary Probability Theory**

**Chapter 4 : Probability**

**Duration of Lectures : 542 Minutes**

**Based Upon J L Devore Chapter 2**

**Based Upon Hogg, Tanis, Zimmerman Chapter 1**

**Topics Covered**

**►Sample Spaces and Events,**

**►Sample Spaces and Events,**

**►Properties of Probability,**

**►Properties of Probability,**

**►Use of Permutations,**

**►Use of Permutations,**

**►Use of Combinations,**

**►Use of Combinations,**

**►Addition Theorem,**

**►Addition Theorem,**

**►Conditional Probability,**

**►Conditional Probability,**

**►The Multiplication Rule & Independent Events,**

**►The Multiplication Rule & Independent Events,**

**►Sampling with & without replacements,**

**►Sampling with & without replacements,**

**►Law of Total Probability, Baye’s Theorem,**

**►Law of Total Probability, Baye’s Theorem,**

**►Mathematical Expectation**

**►Mathematical Expectation**

**Unit – 3 : Random Variables & Probability Distributions**

**Chapter 5 : Probability Distribution of Discrete Random Variables**

**Duration of Lectures : 312 Minutes**

**Based Upon J L Devore Chapter 3.1-3.3**

**Based Upon Hogg, Tanis, Zimmerman Chapter 2.1-2.2**

**Topics Covered**

**►Random Variables, Discrete & Continuous Random Variables,**

**►Random Variables, Discrete & Continuous Random Variables,**

**►Probability Distribution for Discrete Random Variables,**

**►Probability Distribution for Discrete Random Variables,**

**►The Probability Mass Function (PMF), The Cumulative Distribution Function,**

**►The Probability Mass Function (PMF), The Cumulative Distribution Function,**

**►Expected Values & Variance of a Discrete Random Variable,**

**►Expected Values & Variance of a Discrete Random Variable,**

**►Properties of Expected Values & Variance of a Discrete Random Variable**

**►Properties of Expected Values & Variance of a Discrete Random Variable**

**Chapter 6 : Probability Distribution of Continuous Random Variables**

**Duration of Lectures : 312 Minutes**

**Based Upon J L Devore Chapter 4.1-4.2**

**Based Upon Hogg, Tanis, Zimmerman Chapter 3.1**

**Topics Covered**

**►Probability Distribution for Continuous Random Variables,**

**►Probability Distribution for Continuous Random Variables,**

**►The Probability Density Function (PDF), The Cumulative Distribution Function,**

**►The Probability Density Function (PDF), The Cumulative Distribution Function,**

**►The Percentiles of a Continuous Distribution,**

**►The Percentiles of a Continuous Distribution,**

**►Expected Values & Variance of a Continuous Random Variable,**

**►Expected Values & Variance of a Continuous Random Variable,**

**►Properties of Expected Values & Variance of a Continuous Random Variable**

**►Properties of Expected Values & Variance of a Continuous Random Variable**

**Unit – 4 : Special Probability Distributions**

**Chapter 7 : Special Discrete Distributions**

**Duration of Lectures : 314 Minutes**

**Based Upon J L Devore Chapter 3.4-3.6**

**Based Upon Hogg, Tanis, Zimmerman Chapter 2.4, 2.5 & 2.7**

**Topics Covered**

**►The Bernoulli Distribution, **

**►The Bernoulli Distribution,**

**►The Binomial Probability Distribution, Using Binomial Tables, **

**►The Binomial Probability Distribution, Using Binomial Tables,**

**►Mean & Variance of a Binomial Distribution,**

**►Mean & Variance of a Binomial Distribution,**

**►The Hypergeometric Distribution,**

**►The Hypergeometric Distribution,**

**►The Poisson Probability Distribution,**

**►The Poisson Probability Distribution,**

**►Mean & Variance of a Poisson Distribution, The Poisson Process**

**►Mean & Variance of a Poisson Distribution, The Poisson Process**

**Chapter 8 : Special Continuous Distributions**

**Duration of Lectures : 302 Minutes**

**Based Upon J L Devore Chapter 4.3-4.4**

**Based Upon Hogg, Tanis, Zimmerman Chapter 3.2-3.3**

**Topics Covered**

**►The Uniform Distribution, **

**►The Uniform Distribution,**

**►The Normal Distribution,**

**►The Normal Distribution,**

**►The Standard Normal Distribution, Non Standard Normal Distribution,**

**►The Standard Normal Distribution, Non Standard Normal Distribution,**

**►Approximating Binomial Distribution,**

**►Approximating Binomial Distribution,**

**►The Percentiles of a Normal Distribution,**

**►The Percentiles of a Normal Distribution,**

**►The Exponential Distribution,**

**►The Exponential Distribution,**

**Unit – 5 : Random Sampling & Jointly Distributed Random Distributions**

**Chapter 9 : Jointly Distributed Random Variables**

**Duration of Lectures : 204 Minutes**

**Based Upon J L Devore Chapter 5.1-5.2**

**Based Upon Hogg, Tanis, Zimmerman Chapter 4.1-4.4**

**Topics Covered**

**►The Bivariate Distribution of Discrete Type, **

**►The Bivariate Distribution of Discrete Type,**

**►Joint Probability Mass Function, Marginal Probability Mass Function, **

**►Joint Probability Mass Function, Marginal Probability Mass Function,**

**►The Conditional Probability Mass Function,**

**►The Conditional Probability Mass Function,**

**►The Bivariate Distribution of Continuous Type, **

**►The Bivariate Distribution of Continuous Type,**

**►Joint Probability Density Function, Marginal Probability Density Function, **

**►Joint Probability Density Function, Marginal Probability Density Function,**

**►The Conditional Probability Density Function,**

**►The Conditional Probability Density Function,**

**►Expected Values, Covariance and Correlation **

**►Expected Values, Covariance and Correlation**

**Chapter 10 : Random Sampling**

**Duration of Lectures : 176 Minutes**

**Based Upon J L Devore Chapter 5.3, 5.4 & 5.5**

**Topics Covered**

**►Statistics & Their Distributions, **

**►Statistics & Their Distributions,**

**►The Distribution of Sample Mean, **

**►The Distribution of Sample Mean,**

**►The Central Limit Theorem,**

**►The Central Limit Theorem,**

**►The Distribution of a Linear Combination **

**►The Distribution of a Linear Combination**