Course Outcomes - Statistics
| CO No. | Course Outcome |
|---|---|
| CO 1 | Students will be able to identify and classify data based on nominal, ordinal, interval, and ratio scales of measurement. |
| CO 2 | Students will understand the concepts of primary and secondary data and their respective sources. |
| CO 3 | Students will be able to compare census and sample survey methods, and understand the principal steps involved in a sample survey. |
| CO 4 | Students will understand and be able to apply simple random sampling, stratified random sampling, and systematic random sampling for data collection. |
| CO 5 | Students will be able to compute measures of central tendency (mean, median, mode, etc.), dispersion (range, quartiles, standard deviation, etc.), moments, skewness, and kurtosis, and interpret their properties and significance in data analysis. |
| CO No. | Course Outcome |
|---|---|
| CO 1 | Understand the concepts and properties of numbers including integers, rational and irrational numbers. |
| CO 2 | Apply ratio and proportion concepts in solving real-world problems. |
| CO 3 | Demonstrate proficiency in using laws of indices and logarithms in mathematical calculations. |
| CO 4 | Solve linear and quadratic equations and apply arithmetic and geometric progressions to practical situations. |
| CO 5 | Analyse and interpret statistical data, including differentiating between quantitative and qualitative data types and understanding various measurement scales. |
| CO No. | Course Outcome |
|---|---|
| CO 1 | Students will grasp the concepts of random experiments and probability, including frequency, classical, and axiomatic definitions. |
| CO 2 | Students will comprehend the definitions of discrete and continuous random variables and their probability mass and density functions. |
| CO 3 | Students will understand the concept of bivariate random variables and they will be able to compute conditional distributions and determine the independence of random variables. |
| CO 4 | Students will understand the concepts of correlation and its different types, and able to perform simple linear regression, including fitting regression lines and understanding regression coefficients. |
| CO 5 | Students will be able to apply correlation and regression analysis, probability theory, random variables, and bivariate random variables to analyse and solve real-world problems in various fields such as business, economics, and social sciences. |
| CO No. | Course Outcome |
|---|---|
| CO 1 | Understand and calculate various measures of central tendency including the arithmetic mean, median, mode, and quartiles, deciles, and percentiles. |
| CO 2 | Calculate and interpret measures of dispersion such as range, quartile deviation, mean deviation, standard deviation, and coefficient of variation to assess the spread of data. |
| CO 3 | Analyse relationships between variables using correlation techniques including scatter diagrams, Karl Pearson’s correlation coefficient, and Spearman’s rank correlation coefficient. |
| CO 4 | Apply regression analysis techniques to model relationships between variables, including understanding regression types, fitting regression lines, and interpreting regression coefficients. |
| CO 5 | Evaluate and interpret statistical summaries obtained from measures of central tendency, dispersion, correlation, and regression analysis to draw meaningful conclusions from data. |
| CO No. | Course Outcome |
|---|---|
| CO 1 | Students will understand the definition and properties of mathematical expectation, including linearity and additivity. |
| CO 2 | Students will be able to calculate conditional means and variances for bivariate random variables. |
| CO 3 | Students will understand various discrete probability distributions, including uniform, binomial, Poisson, and geometric distributions. |
| CO 4 | Students will learn about common continuous probability distributions, such as rectangular, exponential, and normal distributions. |
| CO 5 | Students will gain practical skills in using spreadsheets to perform calculations related to diagrams, graphs, measures of central tendency, dispersion, moments, correlation, regression, and probability. |
| CO No. | Course Outcome |
|---|---|
| CO 1 | Understand and distinguish between different data types (quantitative, qualitative, time-series, and corss-sectional) and their appropriate scaling techniques. |
| CO 2 | Apply census and sampling methodologies effectively, including the collection of primary and secondary data, and utilize various graphical representations for data presentation. |
| CO 3 | Calculate and interpret measures of central tendency (mean, median, and mode) and dispersion (range, mean deviation, standard deviation) along with the coefficient of variation. |
| CO 4 | Analyse bivariate data through correlation techniques, including the understanding of different types of correlation and the application of scatter diagrams and Karl Pearson’s correlation coefficient. |
| CO 5 | Perform simple linear regression analysis, interpret regression coefficients, and understand the properties of regression models for predictive modelling. |
| CO No. | Course Outcome |
|---|---|
| CO 1 | Understand the fundamental definition and significance of statistics in various fields. |
| CO 2 | Demonstrate proficiency in differentiating and categorizing various types of data. |
| CO 3 | Acquire skills in employing data collection techniques and presenting data effectively using Excel. |
| CO 4 | Apply different methods for summarizing data, including measures of central tendency and dispersion. |
| CO 5 | Analyse and interpret data representations such as histograms, scatter plots, and pie charts, and perform basic statistical analyses like correlation and regression. |