Basic Statistics STA154 provides a comprehensive overview of statistical concepts essential for students in the field of Information Technology. The syllabus covers key topics such as descriptive statistics, probability, random variables, sampling methods, and correlation and regression analysis. Designed for second-semester students, this course includes both theoretical knowledge and practical lab work using statistical software. Students will engage with various statistical methods and applications relevant to IT, enhancing their analytical skills and understanding of data interpretation.

Key Points

  • Covers fundamental concepts of statistics including descriptive and inferential statistics.
  • Includes practical lab work with statistical software like Excel and SPSS.
  • Explains probability distributions including Binomial, Poisson, and Normal distributions.
  • Focuses on correlation and regression analysis for bivariate data.
Sewang Rai.2
8 pages
Language:English
Type:Syllabus
Statistics
Sewang Rai.2
8 pages
Language:English
Type:Syllabus
Statistics
355
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DETAIL SYLLABUS
Course Title: Basic Statistics Full Marks: 60 + 20 + 20
Course No: STA154 Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab Credit Hrs. : 3
Semester: II
Course Description:
The course familiarizes students with the basic concepts of statistics including introduction,
diagrammatical and graphical representation, descriptive statistics, probability, random variables,
sampling, and correlation and regression.
Course Objective:
To impart the knowledge of descriptive statistics, correlation, regression, concept of sampling and
sampling distribution, theoretical as well as applied knowledge of probability and some probability
distributions.
Course Contents:
Unit 1: Introduction (5 Hrs.)
Basic concept of statistics(definitions, concept of descriptive and inferential statistics), application
of Statistics in different fields including information technology, limitations of statistics; scales of
measurement(nominal, ordinal, interval and ratio), variables(Discrete, continuous and
categorical), types of data(cross-sectional and longitudinal) and data source(primary and
secondary), data preparation- editing, coding, and transcribing.
Unit 2: Diagrammatical and Graphical Presentation of Data (3 Hrs.)
Bar diagrams; Pie diagrams; Pareto chart; Graph of frequency distribution (Histogram, frequency
polygon, frequency curve, less than ogive and more than ogive, stem and leaf display) and their
interpretations
Unit 3: Descriptive Statistics (7 Hrs)
Measures of central tendency: Definition of measures of central tendency, arithmetic mean and its
mathematical properties, weighted mean, median, mode, empirical relations between mean ,
median and mode; choice of measure of central tendency, interpretations
Measures of dispersion: Need of measures of dispersion, absolute and relative measures, range,
quartile deviation, mean deviation, standard deviation and their relative measures including
coefficient of variation, choice of appropriate measure of dispersion, interpretations
Measures of skewness: Concept of skewness, types of skewness, Pearson’s coefficient of
skewness, Bowley’s coefficient of skewness; Exploratory Data Analysis (EDA): Five number
summary, box and whisker plots, outliers, use of five number summary and boxplots to assess the
skewness of data distribution
Measures of kurtosis: Concept of kurtosis, types of kurtosis, measure of kurtosis based on
percentiles; overall assessment of nature of data distribution
Moments: Introduction of moments, central moments and raw moments, relations between central
moments and raw moments, measures of skewness and kurtosis based on moments
Problems and illustrative examples related to IT
Unit 4: Introduction to Probability (7 Hrs.)
Concepts of probability, definitions of probability (mathematical, statistical and subjective
approach), terminologies used in probability, laws of probability (additive and multiplicative),
conditional probabilities, Bayes theorem: prior and posterior probabilities
Problems and illustrative examples related to IT
Unit 5: Random Variables and Mathematical Expectation (3 Hrs.)
Concept of a random variable and its types, probability distribution of a random variable,
mathematical expectation of a discrete random variable, standard deviation and variance of
discrete random variable, addition and multiplication theorems of expectation and
variance(without proof).
Problems and illustrative examples related to IT
Unit 6: Probability Distributions (6 Hrs.)
Probability distribution function, Binomial distribution, Poisson distribution, Normal distribution
and their characteristic features; applications of these distributions in IT related data problems
Problems and illustrative examples related to computer Science and IT
Unit 7: Sampling and Sampling Distribution (7 Hrs.)
Definitions of population, sample survey vs. census survey, sampling error and non-sampling
error, concept of parameter and statistic, types of sampling(concept of simple random, stratified,
cluster and systematic sampling, concept of non-probability sampling), standard error of mean,
standard error of proportion, sampling distribution of mean and proportion, need of inferential
statistics, concept of central limit theorem, concept of estimation(point and interval), confidence
interval estimation for mean & proportion, problem specific interpretations of confidence interval
Problems and illustrative examples related to IT
Unit 8: Correlation and Linear Regression (7 Hrs.)
Bivariate data, bivariate frequency distribution, correlation between two variables, Karl Pearson’s
coefficient of correlation(r), assumptions of Pearson’s correlation coefficient, properties of correlation
coefficient, Spearman’s rank correlation including repeated ranks, interpretation of correlation
coefficient, need of regression analysis, fitting of lines of regression by the least squares method,
interpretation of regression coefficients, coefficient of determination(R
2
) and its interpretation, residual
plots for assessing the goodness of fit of the model
Problems and illustrative examples related to IT
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FAQs

What are the main topics covered in the Basic Statistics STA154 course?
The Basic Statistics STA154 course covers several key topics including descriptive statistics, probability, random variables, sampling, and correlation and regression. The syllabus is divided into eight units, starting with an introduction to basic statistical concepts and applications. It also includes diagrammatical and graphical presentation of data, measures of central tendency, and measures of dispersion. Additionally, the course explores probability distributions and sampling distributions, culminating in correlation and linear regression analysis.

What are the measures of central tendency discussed in the syllabus?
The syllabus outlines three primary measures of central tendency: arithmetic mean, median, and mode. It explains the mathematical properties of the arithmetic mean, including the weighted mean, and discusses the empirical relationships between the mean, median, and mode. The course emphasizes the importance of choosing the appropriate measure of central tendency based on the data set and its characteristics.

How is probability introduced in the STA154 course?
Probability is introduced in Unit 4 of the STA154 course, where it covers fundamental concepts, definitions, and terminologies used in probability. The syllabus discusses various laws of probability, including additive and multiplicative laws, as well as conditional probabilities. Bayes' theorem is also introduced, focusing on prior and posterior probabilities, providing a foundational understanding necessary for further statistical analysis.

What types of sampling methods are covered in the course?
The course syllabus details several types of sampling methods, including simple random sampling, stratified sampling, cluster sampling, and systematic sampling. It distinguishes between probability and non-probability sampling techniques and explains the concepts of sampling error and non-sampling error. Understanding these sampling methods is crucial for conducting surveys and interpreting statistical data accurately.

What practical problems are included in the lab component of STA154?
The lab component of STA154 includes a variety of practical problems that utilize statistical software such as Microsoft Excel, SPSS, or STATA. Students will engage in tasks such as diagrammatical and graphical presentation of data, computation of measures of central tendency and dispersion, and analysis of correlation coefficients. Other practical problems involve applying Bayes' theorem and working with probability distributions, ensuring a hands-on approach to the theoretical concepts learned in class.

What is the significance of the Central Limit Theorem in the course?
The Central Limit Theorem is a crucial concept covered in Unit 7 of the STA154 course. It explains the importance of sampling distribution and its relation to inferential statistics. The theorem states that the sampling distribution of the mean will tend to be normally distributed, regardless of the shape of the population distribution, as the sample size increases. This foundational concept is essential for understanding estimation and hypothesis testing in statistics.

What are the key features of probability distributions discussed in the syllabus?
The syllabus discusses several key probability distributions, including the Binomial, Poisson, and Normal distributions. Each distribution is characterized by its specific features and applications in statistical analysis. For instance, the Normal distribution is notable for its bell-shaped curve and is fundamental in inferential statistics, while the Binomial distribution is used for modeling the number of successes in a fixed number of trials. Understanding these distributions is vital for solving problems related to IT and data analysis.