Statistics

Institution Jomo Kenyatta University of Science and Technology
Course Information Technol...
Year 2nd Year
Semester Unknown
Posted By Jeff Odhiambo
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Description

Statistics is the branch of mathematics that involves the collection, analysis, interpretation, presentation, and organization of data. It provides tools and methods to summarize data, identify patterns, and draw meaningful conclusions, often under conditions of uncertainty. By using statistical techniques, researchers can make informed decisions, test hypotheses, and predict future trends. Statistics is broadly divided into two areas: descriptive statistics, which focuses on summarizing and visualizing data, and inferential statistics, which uses sample data to make generalizations or predictions about a larger population. It plays a critical role in various fields, including science, business, healthcare, and social sciences.
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SMA 2272/STA 2270 STATISTICS Trending!
Classical and axiomatic approaches to probability. Compound and conditional probability, including Bayes' theorem. Concept of discrete random variable: expectation and variance. Data: sources, collection, classification and processing. Frequency distributions and graphical representation of data, including bar diagrams, histograms and stem-and-leaf diagrams. Measures of central tendency and dispersion. Skewness and kurtosis. Correlation. Fitting data to a best straight line. Pre-Requisites: STA 2104 Calculus for statistics I, SMA 2104 Mathematics for Science.
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SMA 2104: Mathematics for Sciences - Lesson 8 Statistics Trending!
Upon completing this topic,you should be able to Collect, organize, and represent data, and be able to recognize and describe relationships. Apply the principles of statistics to solve problems in real world contexts. Demonstrate knowledge of statistical terms. Differentiate between the two branches of statistics. Identify types of data. Understand and use the basic measure of central tendency
No pages found 7320 Views 0 Downloads 282.84 KB
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A random variable is a rule that assigns a numerical value to each outcome in the sample space. It maps events from the sample space S to the real numbers X: X : S-R, that is, if S is the sample space with a probability measure and X is a function defined with real values over the elements of S, then X is called a random variable and associates a real number with each result in the sample space.
60 Pages 1902 Views 0 Downloads 984.39 KB
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