Bi variate frequency distribution table 2
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This document discusses a quantitative techniques course for a B.Com program. It covers bi-variate frequency distribution tables, including two practice problems. The first problem provides marks from 20 students in two subjects and requires students to create a bi-variate frequency distribution table with class intervals of 10. The second problem provides ages of 22 husbands and wives and requires a similar table to be made with class intervals of 5 years. The document concludes with multiple choice questions to test understanding of frequency distributions and classifications.
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This document summarizes a session on tabulation of data from a quantitative techniques course. It defines tabulation as systematically organizing data in rows and columns in a table. The objectives of tabulation are to simplify complex data, highlight essential features, facilitate comparison and statistical analysis, and save space. Rules for effective tabulation include using an appropriate table size, arranging column and row headers systematically, clearly defining units of measurement, rounding figures, avoiding unnecessary details, making the table self-explanatory, and avoiding abbreviations. Multiple choice questions are provided to test understanding.
Blank Table 1
This document discusses presenting data in textual and tabular formats. It provides two examples of presenting comparative data from two towns about coffee drinking habits in a table. It also gives an example of drafting a blank table to show the number of candidates by sex, faculty, class and year appearing for degree examinations at a university. The document concludes with multiple choice questions about statistical tables.
Mean, median & mode 2
This document discusses measures of central tendency and dispersion such as mean, median, mode, range, standard deviation, and quartile deviation. It provides examples of calculating the mean, median, and mode for continuous data series. One example calculates the mean as 36.31, median as 34.5, and mode using the formula Z = 3Me - 2X as 30.88. The document also includes multiple choice questions related to relationships between averages and identifying averages based on values given. It recommends statistics textbooks for additional reference on topics discussed.
KPC skewness 1
This document discusses Karl Pearson's Coefficient of Skewness, a method for measuring the skewness of a data distribution. It provides examples of calculating the coefficient under individual and discrete series. For an individual series with data points ranging from 35 to 75 and a mean of 55, the coefficient is calculated to be 0.77, indicating positive skewness. For a discrete series with wage data, the coefficient is calculated to be -0.26, indicating negative skewness. The document also includes multiple choice questions to test understanding of skewness and the relationships between the mean, median and mode of a distribution.
mean median mode 3
This document outlines a course on quantitative techniques for a B.Com program. It discusses key concepts students will learn, including different measures of central tendency (mean, median, mode) and dispersion. It covers the merits and demerits of each measure of central tendency. Example problems are provided to demonstrate calculating and comparing means, medians, and modes. Multiple choice questions at the end test understanding of these concepts.
Mean, median & mode 1
This document discusses a quantitative techniques course for a B.Com program. It covers measures of central tendency and dispersion, including the mean, median, mode, and standard deviation. The learning objectives are to understand these concepts and apply them to sample data. The session discusses calculating the mean, median, and mode for individual and discrete data series. It provides an example of calculating these values for a set of wage and employee data. Multiple choice questions review key aspects of the measures discussed.
Range, Q.D and Co-efficient 2
This document outlines the key topics and objectives of a Quantitative Techniques course for a B.Com program. The learning objectives are to understand measures of central tendency and dispersion such as mean, median, mode, range, mean deviation, standard deviation, and quartile deviation. Example problems are provided to calculate range, quartile deviation, and the coefficient of quartile deviation from wage distribution data. Multiple choice questions are also included as a review of the concepts taught.
graphical representation 1
This document discusses graphical representation of quantitative data. It explains that graphs are better suited than diagrams for statistical analysis as they allow interpolation, extrapolation and locating values like the median and quartiles. Diagrams are only for comparison. The document then contrasts key differences between diagrams and graphs. Finally, it provides general rules for creating graphs and examples of questions to test comprehension.
KPC skewness 2
This document discusses Karl Pearson's Coefficient of Skewness. It provides the formula for Karl Pearson's Coefficient of Skewness as SK= Mean - Mode/ S.D. An example calculation is shown to demonstrate computing the coefficient from sample data. Multiple choice questions are also provided to test understanding. The learning objectives are to understand measures of skewness including Karl Pearson's Coefficient of Skewness and how to apply it to sample data to analyze the normalcy and skewness of distributions.
graphical representation 4
This document discusses constructing cumulative frequency (ogive) curves from data. It begins by recapping how to create histograms, frequency polygons, and pie charts. Then it provides learning objectives and outcomes around analyzing and presenting data graphically using these visualizations. The main content explains how to calculate less than and more than cumulative frequencies and draw the corresponding ogive curves. It gives examples of drawing less than ogive curves for two data sets listing number of workers by daily wage and number of pensioners by age group. Finally, it presents multiple choice questions to test understanding of ogive curves.
graphical representation 2
The document discusses different methods of graphically representing frequency distributions: histograms, frequency polygons, and frequency curves. It provides examples of how to construct each type of graph using sample data on times taken to travel to school, net worth over years, and ages of pensioners. Students will learn how to analyze and interpret these graphs as well as how to present data using histograms, frequency polygons, frequency curves, and pie charts. The document concludes with sample multiple choice questions to test comprehension.
Measures of Dispersion
This document discusses quantitative techniques as part of a B.Com program. It covers measures of central tendency and dispersion, including the mean, median, mode, range, mean deviation, and standard deviation. The learning objectives are to understand different measures of central tendency and dispersion. The learning outcomes are to differentiate, determine, and identify relationships between averages, and apply measures of dispersion to data by comparing standard deviation to mean deviation. The document also provides examples and practice questions on these topics.
Missing frequency 1
This document discusses a quantitative techniques course for a B.Com program. It covers measures of central tendency like mean, median, and mode. The learning objectives are to understand different measures of central tendency and dispersion. The learning outcomes are to differentiate, determine, and identify relationships between averages, and apply measures of dispersion to sample data. The session covers problems calculating missing frequencies and variables in the mean under discrete series. It provides two examples of finding missing values when the mean is given. The document concludes with multiple choice questions reviewing the material.
diagrams theory
This document discusses the presentation of quantitative data through diagrams. It begins by stating the learning objectives, which are for students to learn how to present data graphically using various charts and diagrams and calculate cumulative frequency. It then discusses different types of diagrams used for data presentation, including bar diagrams, pie charts, and two-dimensional diagrams. The key advantages of diagrams over tables are that they can more quickly and easily convey complex ideas in numbers and trends may be more noticeable. Guidelines for effective diagrams include giving them a clear title, using an appropriate size and scale, including a legend if multiple variables are shown, and selecting the correct type of diagram for the data. Common one-dimensional and two-dimensional diagram types are also listed.
KPC skewness 2
This document discusses measures of skewness, including Bowley's coefficient of skewness. It provides examples of calculating Bowley's coefficient using quartiles to determine if a distribution is skewed or symmetrical. The document also includes review questions related to skewness, Bowley's coefficient formula, and examples of its calculation using quartile data from continuous distributions.
Diagrams part 2
This document discusses one dimensional bar diagrams, including simple and subdivided bar diagrams. It provides examples of how to construct simple and component bar diagrams to present statistical data comparing production costs between companies and expenditures across categories for two schools. Multiple choice questions are also included at the end to test understanding of diagrammatic data presentation.
Mean Deviation and Co- efficient
This document discusses quantitative techniques for a B.Com program. It covers measures of central tendency and dispersion such as mean, median, mode, range, mean deviation, standard deviation, and quartile deviation. The learning objectives are to understand these concepts and apply them to sample data. An example is provided to calculate the mean deviation and coefficient of mean deviation from the mean, median, and mode for household income data. Multiple choice questions review key concepts like the definition of mean deviation and properties of variance. References for further reading on statistics are also included.
graphical representation 6
This document discusses quantitative techniques and constructing graphs from data. It covers constructing less than and more than ogive curves, which are cumulative frequency curves. An ogive curve shows the cumulative frequency distribution and can be used to find the median. Two problems are presented on drawing less than and more than ogive curves from data on weights of items and ages of pensioners. Multiple choice questions are also provided to test understanding of ogives and cumulative frequency polygons.
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Bi variate frequency distribution table 2
- 1. PROGRAMME B.COM SUBJECT QUANTITATIVE TECHNIQUE – I SEMESTER III UNIVERSITY VIJAYANAGAR SRI KRISHNADEVARAYA UNIVERSITY, BALLARI SESSION 14
- 2. RECAP • Bi-Variate Frequency Distribution Table Meaning & Problems-------- 01
- 3. LEARNING OBJECTIVES • The aim of the chapter is to make students to present data in textual and Tabular format including the technique of creating frequency distribution and working out bi-variate distribution table
- 4. LEARNING OUTCOMES • After the Chapter, The Students Shall be able to Describe and Understand the Rules & Types of Classification, Frequency Distribution, Class Interval & its Types, Basic Principles Tabulation and The Sorting of Data.
- 5. SESSION - 14 • Bi-Variate Frequency Distribution Table Problems - 02
- 6. EXAMPLE 1 Prepare a bivariate frequency distribution table with regard to marks scored by 20 students in two subjects i.e., Marks in F.A and Q.T, With class interval 10 F.A 36 29 31 19 41 46 28 37 53 39 Q.T 57 43 41 31 61 63 19 53 75 61 F.A 41 39 36 36 71 18 43 47 36 36 Q.T 61 57 53 49 81 19 59 66 49 51
- 7. CONTD BI-VARIATE FREQUENCY DISTRIBUTION Q.T F.A 15-25 25-35 35-45 45-55 55-65 65-75 TOTAL 15-25 I I -- -- -- -- 2 25-35 I -- -- -- -- -- 1 35-45 -- II -- -- -- -- 2 45-55 -- -- IIII I -- 5 55-65 -- -- IIII I I -- 7 65-75 -- -- -- I -- 1 75-85 -- -- -- I -- I 2 TOTAL 2 3 11 3 -- 1 20
- 8. EXAMPLE 2 Prepare a bivariate frequency distribution table with regard to the ages of 22 husbands and wives are given below, taking class intervals for both ages, 20-25,25-30, 30-35 etc. Age of husband as X , age of wives as Y X 29 38 43 26 30 47 38 36 24 42 43 Y 24 35 40 27 26 46 36 36 23 40 33 X 28 40 24 34 37 33 23 30 39 49 36 Y 25 35 21 32 30 36 24 28 35 48 20
- 9. CONTD Classification of Ages of Husbands and Wives Y X 20-25 25-30 30-35 35-40 40-45 45-50 TOTAL 20-25 III I -- I -- -- 5 25-30 -- II II -- -- -- 4 30-35 -- -- I I I -- 3 35-40 -- -- I IIII I -- 6 40-45 -- -- -- -- II -- 2 45-50 -- -- -- -- -- II 2 TOTAL 3 3 4 6 4 2 22
- 10. SUMMARY As we already discussed and learnt today on Classifications and Tabulation as below • Bi-Variate Frequency Distribution Table Problems - 02
- 11. MCQs 1 . A grouping of data into mutually exclusive classes showing the number of observations in each class is; (a) Frequency polygon (b) Relative frequency (c) Frequency distribution (d) Cumulative frequency 2 . The class frequency divided by the total number of observations is called: (a) Percentage frequency (b) Relative frequency (c) Cumulative frequency (d) Bivariate frequency
- 12. MCQs 3 . The following frequency distribution Classes 10----20, 20----30, 30----40, 40----50, 50----60 Frequency 2 4 6 4 2 is classified as: (a) Exclusive classification (b) Inclusive classification (c) Geographical classification (d) Two-way classification
- 13. MCQs 4 . The following frequency distribution Classes 0----4, 5----9, 10----14, 15----19, 20----24 Frequency 2 3 7 5 3 is classified as: (a) Multiple classification (b) Qualitative classification (c) Inclusive classification (d) Exclusive classification
- 14. MCQs 5 . The following frequency distribution Classes; Less than 2, Less than 4, Less than 6, Less than 8 Frequency; 2 6 16 19 is classified as: (a) Inclusive classification (b) Exclusive classification (c) Discrete classification (d) Cross classification
- 15. CONTD ANSWERS 1. C 2. B 3. A 4. C 5. B
- 16. REFERENCES • S.P. Gupta, Sultan Chand and Sons Publications, 2017 • S. C. Gupta, Himalaya Publishing House, Fundamentals of Statistics, 2018 • R.S.N Pillai and Bagavathi, S.Chand publications, 2010
- 17. THANK YOU