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BCS 040 Statistical Techniques BCS 040...
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| Category | : BACHELOR‘S DEGREE PROGRAMMES |
| Sub Category | : Bachelor of Computer Applications (BCA) |
| Products Code | : BCA-S4-3.06 |
| HSN Code | : 490110 |
| Language | : English |
| Author | : BMA PUBLICATION PVT LTD |
| Publisher | : BMAP EDUSERVICES PVT LTD |
| University | : IGNOU (Indira Gandhi National Open University) |
| Pages | : 300 |
| Weight | : 199 GM |
| Dimensions | : 21.0 x 29.7 cm (A4 Size Pages) |
BCS 040 Statistical Techniques is a foundational course designed for Bachelor of Computer Applications (BCA) students, providing a comprehensive introduction to statistical methods and techniques used in data analysis. This course aims to equip students with the essential statistical knowledge and skills necessary for analyzing and interpreting data in various domains.
The course begins by covering descriptive statistics. Students learn how to summarize and present data effectively using measures such as mean, median, mode, standard deviation, and graphical representations (histograms, box plots, scatter plots). They understand the importance of descriptive statistics in data analysis and learn how to interpret and communicate findings from descriptive analyses.
Probability distributions are a central focus of the course. Students explore the concepts of probability and probability distributions, including discrete and continuous distributions. They learn about commonly used probability distributions such as the binomial, Poisson, and normal distributions, and understand how to calculate probabilities and use distribution properties for data analysis.
The course also covers hypothesis testing techniques. Students gain proficiency in hypothesis testing methods, including z-tests, t-tests, chi-square tests, and analysis of variance (ANOVA). They learn how to formulate null and alternative hypotheses, conduct hypothesis tests, interpret test results, and make inferences about population parameters based on sample data.
Regression analysis is another essential aspect of the course. Students learn how to analyze relationships between variables using regression analysis techniques. They understand the principles of simple linear regression and multiple regression and learn how to estimate regression parameters, assess model fit, and make predictions based on regression models.
Throughout the course, students engage in practical exercises and data analysis projects to reinforce their learning. They work with real-world datasets, apply statistical techniques using statistical software tools (such as R or Python), and interpret results to draw meaningful conclusions from data. By the end of the course, students develop strong analytical and problem-solving skills essential for data-driven decision-making in various domains.
In addition to its educational value, this study guide serves as a valuable resource for students preparing for exams. Covering the entire syllabus comprehensively and spanning approximately 300-350 pages, it provides in-depth coverage of all statistical techniques topics, ensuring thorough preparation for exams.
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