• Other Usufull Resources

      • 1-Introduction to Statistics and Data collection


        •                                                                                       Lecture 1
        • What is Statistics, MEANINGS OF ‘STATISTICS’, CHARACTERISTICS OF THE SCIENCE OF STATISTICS, THE WAY IN WHICH STATISTICS WORK, IMPORTANCE OF STATISTICS IN VARIOUS FIELDS, OBSERVATIONS AND VARIABLES, VARIABLESQUANTITATIVE AND QUALITATIVE VARIABLES, DISCRETE AND CONTINUOUS VARIABLES, MEASUREMENT SCALES, NOMINAL SCALE, ORDINAL OR RANKING SCALE, INTERVAL SCALE, RATIO SCALE, ERRORS OF MEASUREMENT, BIASED AND RANDOM ERRORS


                                                                                           Lecture 2

        • COLLECTION OF DATA, PRIMARY AND SECONDARY DATA, COLLECTION OF PRIMARY DATA, DIRECT PERSONAL INVESTIGATION, INDIRECT INVESTIGATION, COLLECTION THROUGH QUESTIONNAIRES, COLLECTION THROUGH ENUMERATORS, COLLECTION THROUGH LOCAL SOURCES, COLLECTION OF SECONDARY DATA, SAMPLING FRAME, ADVANTAGES OF SAMPLING, SAMPLING & NON-SAMPLING ERRORS, NONRANDOM SAMPLING, QUOTA SAMPLING, ADVANTAGES OF QUOTA SAMPLING, RANDOM SAMPLING, TYPES OF RANDOM SAMPLING, SIMPLE RANDOM SAMPLING, OTHER TYPES OF RANDOM SAMPLING


                                                                                             Lecture 3

        • Tabulation, Simple bar chart, Component bar chart, Multiple bar chart, Pie chart

      • 2-Frequency Distribution and Frequency Curves

                                                                                              Lecture 4

        • CONSTRUCTION OF A FREQUENCY DISTRIBUTION, CLASS BOUNDARIES, HISTOGRAM, FREQUENCY POLYGON, FREQUENCY CURV


                                                                                            Lecture 5

        • FREQUENCY POLYGON, FREQUENCY CURVE, VARIOUS TYPES OF FREQUENCY CURVES, VARIOUS TYPES OF DISCRETE FREQUENCY DISTRIBUTION, CUMULATIVE FREQUENCY DISTRIBUTION, CUMULATIVE FREQUENCY POLYGON or OGIVE

      • 3-Measures of Central Tendency

                                                                                             Lecture 6

        • DATA IN THE FORM OF AN ARRAY (in ascending order), FREQUENCY DISTRIBUTION, DESCRIPTION OF VARIABLE DATA, MEASURES OF CENTRAL TENDENCY AND MEASURES OF DISPERSION, AVERAGES (I.E. MEASURES OF CENTRAL TENDENCY), VARIOUS TYPES OF AVERAGES, MODE IN CASE OF RAW DATA PERTAINING TO A CONTINUOUS VARIABLE , THE MODE IN CASE OF A DISCRETE FREQUENCY DISTRIBUTION, THE MODE IN CASE OF THE FREQUENCY DISTRIBUTION OF A CONTINUOUS VARIABLE 



                                                                                               Lecture 7

        • THE ARITHMETIC MEAN, DESIRABLE PROPERTIES OF THE MODE, CLASS-MARK (MID-POINT), GROUPING ERROR, DESIRABLE PROPERTIES OF THE ARITHMETIC MEAN, WEIGHTED MEAN, MEDIAN


                                                                                               Lecture 8

        • QUARTILES, FIRST QUARTILE, SECOND QUARTILE (I.E. MEDIAN), THIRD QUARTILE, DECILES & PERCENTILES

                                                                                               Lecture 9

        • GEOMETRIC MEAN, GEOMETRIC MEAN FOR GROUPED DATA, HARMONIC MEAN, HARMONIC MEAN RULES, RELATION BETWEEN ARITHMETIC, GEOMETRIC AND HARMONIC MEANS, MID-RANGE, MID-QUARTILE RANGE

                                                                                

      • 4-Measure of Dispersion

                                                                                               Lecture 10

        • CONCEPT OF DISPERSION, ABSOLUTE VERSUS RELATIVE MEASURES OF DISPERSION, RANGE, COEFFICIENT OF DISPERSION, QUARTILE DEVIATION

                                                                                                  Lecture 11

        • MEAN DEVIATION, MEAN DEVIATION FOR GROUPED DATA, CO-EFFICIENT OF M.D, Variance, STANDARD DEVIATION, SHORT CUT FORMULA FOR THE STANDARD DEVIATION, STANDARD DEVIATION IN CASE OF GROUPED DATA, SHORT CUT FORMULA OF THE STANDARD DEVIATION IN CASE OF GROUPED DATA

                                                                                                    Lecture 12

        • Chebychev’s Inequality, The Empirical Rule, The Five-Number Summary

      • 5-Measure of Dispersion

                                                                                  Lecture 13

        •  Box and Whisker Plot, Pearson’s Coefficient of Skewness


                                                                                         Lecture 14
        • Bowley’s coefficient of skewness, The Concept of Kurtosis, Percentile Coefficient of Kurtosis, Moments & Moment Ratios, Sheppard’s Corrections, The Role of Moments in Describing Frequency Distributions

      • 6-Regression and Correlation

                                                                                       Lecture 15

        • Simple Linear Regression, Standard Error of Estimate, Correlation