Applied Statistics includes planning for the collection of data, managing data, analyzing, interpreting and drawing conclusions from data, and identifying problems, solutions and opportunities using the analysis. This course helps the learners to build critical thinking and problem solving skills in data analysis and empirical research. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. By leveraging the pandas and matplotlib python libraries, the learners will gain exposure to tools used in data analysis, visualization, and data science using Python programming Language. This course specifically explores inferential statistics - the science of applying statistical techniques to quantify and answer real-world data analysis questions.
COURSE DURATION: 4 Weeks
By the end of this course, the learners will be able to
Apply the basic concepts of distributions, charts and various types of measures.
Apply the concepts of estimation and its type in mean, proportion and variance.
Demonstrate the concept of testing of hypothesis for small and large samples by using various tests like t-test, z-test and chi-square test
Apply the concept of Correlation and regressions to engineering problems
Apply multiple regression and correlation analysis, Inferences about population parameters and Modeling techniques.
DESCRIPTIVE STATISTICS: Frequency distribution – Bar graphs and Pie charts – Histogram- Ogive – Simpson’s paradox – Measures of Location.
Measures of Variability – Measures of distribution shape, relative location and detecting outliers – Exploratory Data analysis, Stem-and-leaf display – Measures of Association between two variables.
Hypothesis Testing: General concepts - Errors in Hypothesis testing - One-and two-tailed tests - Tests concerning mean, proportion, and variance - Tests for Goodness of fit and independence of attributes.
CORRELATION AND REGRESSION: introduction - Estimation using the regression line - Correlation analysis -Limitations, errors.
Dr. T. Chandrakumar
Mrs. B. Suryadevi
Mrs. M. S. Sassirekha