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Course Description
Introductory Statistics and Applications (I) ( SEC1 ) introduces fundamental statistical concepts, including probability, distributions, sampling, and hypothesis testing. Emphasizing real-world applications, the course equips students with essential data analysis skills for interpreting and solving economic and business problems using statistical tools and methodologies.
Course Starting from 1st October 2024
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Past Year Paper & Solutions
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Experience our Introductory Statistics and Applications (I) ( SEC1 ) course before enrolling with our exclusive trial classes. These sessions provide a sneak peek into the engaging lectures and interactive teaching methods that define our program.
Combined Mean
Goodness of Fit
Introduction to Statistics
Probability Basics
Terminology
What are Various Descriptive Statistics ?
Introductory Statistics and Applications (I) Syllabus
Unit 2.1
Unit 2.2
Skewness and Kurtosis
Bivariate Analysis
Unit 2.1
Introduction and Overview
- Subject matter of Statistics
- Basic Steps in Statistical Methods – Collection, Presentation, and Analysis of Data
- Collection of Data – Primary and Secondary sources – their comparison, methods of data collection
- Concepts – Variable and Attribute (categorical variable) – Discrete, Continuous, and Categorical Variables, Complete Enumeration Survey, and Sample Survey, Population and Sample
- Presentation of data – Textual, Tabular, Diagrammatic
- Frequency Distribution – Construction of Ogives, Column diagram, Frequency Polygon, Histogram, Frequency Curve
- Analysis of Data – Univariate and Bivariate Analysis (Concepts only)
References: Gun, A. M., M.K. Gupta, and B. Dasgupta (GGDG) (2022), Fundamentals of Statistics, Volume One, World Press Private Limited Kolkata – Chapter on ‘Collection and Presentation of Data’; Chapter on ‘Frequency Distributions’
Unit 2.2
Descriptive Statistics
Central Tendency
- Measures of central tendency for ungrouped and grouped data – arithmetic mean, geometric mean, harmonic mean, median, and mode – Composite measures; Comparison of different measures, Quartiles, Deciles, and Percentiles
- Index numbers – Price Index Numbers – problems of construction, methods of construction – aggregative (simple and weighted) and averaging price-relatives (simple and weighted), Laspayre’s, Paasche’s index numbers, Fisher’s Index Number, Quantity Index Numbers, Tests of Index Numbers, Fixed Base and Chain Base, Wholesale price index, and cost of living index, Uses of index numbers
Dispersion
- Absolute measures of dispersion for ungrouped and grouped data – range, quartile deviation, mean deviation, standard deviation – Composite SD; Comparison of different measures
- Relative measures – coefficient of variation, coefficient of mean deviation, coefficient of quartile deviation
- Distribution of income and wealth – Lorenz curve, Gini Coefficient, Theil’s Index
Skewness and Kurtosis
Skewness and Kurtosis
- Moments – central and non-central – computation, conversion
- Measures of skewness – Bowley’s measure, coefficient of quartile deviation, measure based on moments
- Measure of kurtosis – measure based on moments
Bivariate Analysis
Bivariate Analysis
- Bivariate data – scatter diagram, Simple correlation coefficient – computation, limitations, and properties
- Simple linear regression – Least squares technique – Properties
Learning Objectives
- Understand the Basics of Statistics: To introduce students to the fundamental concepts of statistics, including the collection, presentation, and analysis of data.
- Data Handling Techniques: To teach students how to gather data from primary and secondary sources and methods to compare them.
- Variable Classification: Students will learn to classify variables into discrete, continuous, and categorical types.
- Graphical and Tabular Representation: Students will gain the ability to present data using textual, tabular, and diagrammatic methods such as frequency distributions, histograms, and ogives.
- Basic Analysis Techniques: Students will learn how to perform univariate and bivariate data analysis.
Learning Outcomes
- Proficiency in Data Collection Methods: By the end of the course, students will have an understanding of primary and secondary data collection techniques and their relative advantages and disadvantages.
- Ability to Classify Variables: Students will be able to differentiate between discrete, continuous, and categorical variables, and use this classification in statistical analysis.
- Competency in Data Presentation: Students will be able to present data using various formats including textual, tabular, and diagrammatic approaches like frequency polygons and histograms.
- Basic Analytical Skills: Students will be proficient in analyzing data sets using univariate and bivariate techniques, and will be able to interpret the results.
Examination Scheme
Total Marks: 75
Samkith Banthia
ISI Alumnus | Eco(H) St Xaviers Kolkata | FRM from GARP | Formerly with Barclay's Bank
Samkith Sir, an alumnus of ISI, transitioned from a successful banking career to pursue his passion for teaching and mentoring aspiring students. With expertise in mathematics and a nurturing demeanor, he stands out as a premier educator, dedicated to guiding students towards academic excellence.
Mahima Banthia
DSE Alumnus | Eco(H) SRCC Delhi | Former Lecturer St Stephen’s College Delhi | NET qualified
In Mahima Ma’am’s class at EduSure, we prioritize logical reasoning and emphasize conceptual understanding, ensuring mastery of macroeconomics. Mahima Ma’am, also handling administrative responsibilities at EduSure, is dedicated to providing the highest quality service to every student.
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