Online Classes
Available Lesson & Learning Object's
1. Applied Statistics › Hypothesis Testing › Limitations of test of significance
2. Applied Statistics › Power and Sample Size › Power of a statisticsal test
3. Applied Statistics › Power and Sample Size › Sample size importance
4. Applied Statistics › HT: 2 Sample Means › Matched pair test
5. Applied Statistics › HT: 2 Sample Means › Testing nariance Homogeniety
6. Applied Statistics › HT: 2 Sample Means › Effect Size
7. Applied Statistics › HT: 2 Sample Means › Two sample T test
8. Applied Statistics › Confidence Intervals/Margin of Error › Steps to computeCconfidence Interval with example
9. Applied Statistics › Confidence Intervals/Margin of Error › Definition: Confidence interval, Parameter and data requirement of confidence interval
10. Applied Statistics › Confidence Intervals/Margin of Error › Definition:- Margin of error, steps to compute margin of errors with example
11. Applied Statistics › Confidence Intervals/Margin of Error › Steps to compute standard deviation and standard error
12. Applied Statistics › Confidence Intervals/Margin of Error › Definition:- Critical Value, steps to compute critical value with example
13. Applied Statistics › Hypothesis Testing › List of formulae and Examples
14. Applied Statistics › Power and Sample Size › Sample size Calculation
15. Applied Statistics › Hypothesis Testing › Test of significance for large and small samples
16. Applied Statistics › Hypothesis Testing › Test of significance for three attributes:- Number of Successes, Proportion of successes and difference between proportion
17. Applied Statistics › Hypothesis Testing › Point estimates and Interval estimates
18. Applied Statistics › Hypothesis Testing › Meauring the Power of a Hypothesis and standard error
19. Applied Statistics › Hypothesis Testing › One tailed and Two tailes tests of hypothesis
20. Applied Statistics › Hypothesis Testing › Two types of error in testing of hypothesis
21. Applied Statistics › Hypothesis Testing › Procedure of Testing Hypothesis
22. Applied Statistics › Probability and Sampling/Distributions › Chart of Bernoulli; Binomial,Poisson, Normal; Chi - square and student's t distribution to compare parameters, Probability Function, Range, Mean andf Variance
23. Applied Statistics › Probability and Sampling/Distributions › Normal Approximation to Poisson Distribution with examples
24. Applied Statistics › Probability and Sampling/Distributions › Normal Approximation to Binomial Distribution with examples
25. Applied Statistics › Probability and Sampling/Distributions › Continuous distribution: Normal; Chi - square and student's t distribution with example
26. Applied Statistics › The Chi Square Frequency Test › Use of chi Square test with examples
27. Applied Statistics › More Nonparametrics › Spearman's lomitations of Non Parametric test
28. Applied Statistics › More Nonparametrics › Spearman's Rank Correlation with Examples
29. Applied Statistics › More Nonparametrics › The Kruskal - Wallis OR H - Test with examples
30. Applied Statistics › More Nonparametrics › The One Sample Runs Test with examples
31. Applied Statistics › More Nonparametrics › A RANK SUM TEST: MANN-WHITNEY U TEST with examples
32. Applied Statistics › More Nonparametrics › The Paired Sampled Sign test with example
33. Applied Statistics › More Nonparametrics › The Sign test with example
34. Applied Statistics › More Nonparametrics › Advantages of non parametric test
35. Applied Statistics › More Nonparametrics › Introduction and list of non- parametric test
36. Applied Statistics › The Chi Square Frequency Test › Misuse of chi square and limitations on the use of chi square test
37. Applied Statistics › The Chi Square Frequency Test › Chi square for specified value of population variance with examples
38. Applied Statistics › The Chi Square Frequency Test › Additive property of chi square
39. Applied Statistics › Probability and Sampling/Distributions › Discrete distributions: Bernoulli; Binomial and Poisson with examples
40. Applied Statistics › The Chi Square Frequency Test › Conditions for applying Chi-square and YATES' corrections
41. Applied Statistics › The Chi Square Frequency Test › Definition; Degree of freedom; Parameters
42. Applied Statistics › More Correlation and Regression Coefficients › Merits and demerits of Multiple correlation
43. Applied Statistics › More Correlation and Regression Coefficients › Multiple correlation:- definitation, Formula and examples
44. Applied Statistics › More Correlation and Regression Coefficients › Limitations and significance of partial correlation
45. Applied Statistics › More Correlation and Regression Coefficients › Zero order, first order, and second order partial correlation with examples.
46. Applied Statistics › More Correlation and Regression Coefficients › Partial correlation - introduction, formula and use
47. Applied Statistics › Testing/Using Linear Regression › List of formulae and Examples
48. Applied Statistics › Testing/Using Linear Regression › Examples of multiple regression
49. Applied Statistics › Testing/Using Linear Regression › Coefficient of multiple determination
50. Applied Statistics › Testing/Using Linear Regression › Formula of Multiple regression, deviation taken from actual mean, standard error of estimate
51. Applied Statistics › Organizing and Presenting Data › Precautions in the use of Primary and secondary data
52. Applied Statistics › Describing Distributions › Binomial Distribution
53. Applied Statistics › Describing Distributions › Beta Distribution
54. Applied Statistics › Describing Distributions › Bernoulli Distribution
55. Applied Statistics › Organizing and Presenting Data › Limitations of diagrams and graphs
56. Applied Statistics › Organizing and Presenting Data › Types of graphs (with examples)
57. Applied Statistics › Organizing and Presenting Data › Types of diagrams (with examples)
58. Applied Statistics › Organizing and Presenting Data › Diagrametic representation of data:-difference between Diagrams and graphs
59. Applied Statistics › Organizing and Presenting Data › Examples:- based on classification of data
60. Applied Statistics › Organizing and Presenting Data › To calculate number of classes from the given raw data
61. Applied Statistics › Organizing and Presenting Data › Definition:-(raw, discrete ..data,class limit, class interval,class mark, range)
62. Applied Statistics › Organizing and Presenting Data › Classification of data:- Geographical, Chronological, Qualitative, Quantitative
63. Applied Statistics › Organizing and Presenting Data › Examples:- Primary and secondary data
64. Applied Statistics › Describing Distributions › Cauchy Distribution
65. Applied Statistics › Organizing and Presenting Data › Editing:- Primary and secondary data
66. Applied Statistics › Organizing and Presenting Data › Merits and demarits:- Primary and secondary data
67. Applied Statistics › Organizing and Presenting Data › Sources:- Primary and secondary data
68. Applied Statistics › Organizing and Presenting Data › Definition:-Primary and secondary data
69. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Statistics and computers
70. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Statistical Method Vs Experimental methods
71. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Limitations of statistics
72. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Application (use) of statistics
73. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Functions of statistics:- (Definiteness;condensation,comparision,formulating and testing hypothesis,prediction, formulation of suitable policies)
74. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Definitions-1)statistical data 2)statistical methods: - collection, organisation,presentation,interpretation
75. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Origin and growth of statistics
76. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › Student's t Distribution - (formula, properties and examples)
77. Applied Statistics › Probability and Sampling/Distributions › Types of distribution: Discrete and continuous with example
78. Applied Statistics › Linear Regression › Transformations to Achieve Linearity
79. Applied Statistics › Linear Regression › Residual; Outeliers and Influantial Points
80. Applied Statistics › Linear Regression › Relation between regression coefficient and correlation coefficient with examples
81. Applied Statistics › Linear Regression › Explain Regression Coefficients and Regression Equations with examples
82. Applied Statistics › Linear Regression › Definition, Formulae ,Properties of regression coefficient, ,Properties of regression lines.
83. Applied Statistics › Measuring Correlation › Spearman's coefficient of Rank correlation with repeated Ranks, formula with examples
84. Applied Statistics › Measuring Correlation › Spearman's coefficient of Rank correlation definition, formula, examples
85. Applied Statistics › Measuring Correlation › Karl Pearson's coefficient of correlation (Product Moment) short cut method with examples
86. Applied Statistics › Measuring Correlation › Karl Pearson's coefficient of correlation (Product Moment) with examples
87. Applied Statistics › Measuring Correlation › Scatter Diagrams, Interpretation and properties of coefficient of correlation
88. Applied Statistics › Measuring Correlation › Definition and types of correlation,
89. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Introduction
90. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › F Distribution - (formula, coding of data and examples)
91. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › Normal Distribution - (properties, parameter, Area under the normal curve,examples)
92. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › Poisson Distribution - (formula, parameter and examples)
93. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › Binomial Distribution - introduction,Pascal's triangle, Properties, Parameters, Negative binomial distribution, examples)
94. Applied Statistics › Describing Distributions › Geometric Distribution
95. Applied Statistics › Describing Distributions › Gamma Distribution
96. Applied Statistics › Describing Distributions › Student's t Distribution
97. Applied Statistics › Describing Distributions › F Distribution
98. Applied Statistics › Describing Distributions › Chi-square Distribution
99. Applied Statistics › Describing Distributions › Poisson Distribution
100. Applied Statistics › Describing Distributions › Normal Distribution
2. Applied Statistics › Power and Sample Size › Power of a statisticsal test
3. Applied Statistics › Power and Sample Size › Sample size importance
4. Applied Statistics › HT: 2 Sample Means › Matched pair test
5. Applied Statistics › HT: 2 Sample Means › Testing nariance Homogeniety
6. Applied Statistics › HT: 2 Sample Means › Effect Size
7. Applied Statistics › HT: 2 Sample Means › Two sample T test
8. Applied Statistics › Confidence Intervals/Margin of Error › Steps to computeCconfidence Interval with example
9. Applied Statistics › Confidence Intervals/Margin of Error › Definition: Confidence interval, Parameter and data requirement of confidence interval
10. Applied Statistics › Confidence Intervals/Margin of Error › Definition:- Margin of error, steps to compute margin of errors with example
11. Applied Statistics › Confidence Intervals/Margin of Error › Steps to compute standard deviation and standard error
12. Applied Statistics › Confidence Intervals/Margin of Error › Definition:- Critical Value, steps to compute critical value with example
13. Applied Statistics › Hypothesis Testing › List of formulae and Examples
14. Applied Statistics › Power and Sample Size › Sample size Calculation
15. Applied Statistics › Hypothesis Testing › Test of significance for large and small samples
16. Applied Statistics › Hypothesis Testing › Test of significance for three attributes:- Number of Successes, Proportion of successes and difference between proportion
17. Applied Statistics › Hypothesis Testing › Point estimates and Interval estimates
18. Applied Statistics › Hypothesis Testing › Meauring the Power of a Hypothesis and standard error
19. Applied Statistics › Hypothesis Testing › One tailed and Two tailes tests of hypothesis
20. Applied Statistics › Hypothesis Testing › Two types of error in testing of hypothesis
21. Applied Statistics › Hypothesis Testing › Procedure of Testing Hypothesis
22. Applied Statistics › Probability and Sampling/Distributions › Chart of Bernoulli; Binomial,Poisson, Normal; Chi - square and student's t distribution to compare parameters, Probability Function, Range, Mean andf Variance
23. Applied Statistics › Probability and Sampling/Distributions › Normal Approximation to Poisson Distribution with examples
24. Applied Statistics › Probability and Sampling/Distributions › Normal Approximation to Binomial Distribution with examples
25. Applied Statistics › Probability and Sampling/Distributions › Continuous distribution: Normal; Chi - square and student's t distribution with example
26. Applied Statistics › The Chi Square Frequency Test › Use of chi Square test with examples
27. Applied Statistics › More Nonparametrics › Spearman's lomitations of Non Parametric test
28. Applied Statistics › More Nonparametrics › Spearman's Rank Correlation with Examples
29. Applied Statistics › More Nonparametrics › The Kruskal - Wallis OR H - Test with examples
30. Applied Statistics › More Nonparametrics › The One Sample Runs Test with examples
31. Applied Statistics › More Nonparametrics › A RANK SUM TEST: MANN-WHITNEY U TEST with examples
32. Applied Statistics › More Nonparametrics › The Paired Sampled Sign test with example
33. Applied Statistics › More Nonparametrics › The Sign test with example
34. Applied Statistics › More Nonparametrics › Advantages of non parametric test
35. Applied Statistics › More Nonparametrics › Introduction and list of non- parametric test
36. Applied Statistics › The Chi Square Frequency Test › Misuse of chi square and limitations on the use of chi square test
37. Applied Statistics › The Chi Square Frequency Test › Chi square for specified value of population variance with examples
38. Applied Statistics › The Chi Square Frequency Test › Additive property of chi square
39. Applied Statistics › Probability and Sampling/Distributions › Discrete distributions: Bernoulli; Binomial and Poisson with examples
40. Applied Statistics › The Chi Square Frequency Test › Conditions for applying Chi-square and YATES' corrections
41. Applied Statistics › The Chi Square Frequency Test › Definition; Degree of freedom; Parameters
42. Applied Statistics › More Correlation and Regression Coefficients › Merits and demerits of Multiple correlation
43. Applied Statistics › More Correlation and Regression Coefficients › Multiple correlation:- definitation, Formula and examples
44. Applied Statistics › More Correlation and Regression Coefficients › Limitations and significance of partial correlation
45. Applied Statistics › More Correlation and Regression Coefficients › Zero order, first order, and second order partial correlation with examples.
46. Applied Statistics › More Correlation and Regression Coefficients › Partial correlation - introduction, formula and use
47. Applied Statistics › Testing/Using Linear Regression › List of formulae and Examples
48. Applied Statistics › Testing/Using Linear Regression › Examples of multiple regression
49. Applied Statistics › Testing/Using Linear Regression › Coefficient of multiple determination
50. Applied Statistics › Testing/Using Linear Regression › Formula of Multiple regression, deviation taken from actual mean, standard error of estimate
51. Applied Statistics › Organizing and Presenting Data › Precautions in the use of Primary and secondary data
52. Applied Statistics › Describing Distributions › Binomial Distribution
53. Applied Statistics › Describing Distributions › Beta Distribution
54. Applied Statistics › Describing Distributions › Bernoulli Distribution
55. Applied Statistics › Organizing and Presenting Data › Limitations of diagrams and graphs
56. Applied Statistics › Organizing and Presenting Data › Types of graphs (with examples)
57. Applied Statistics › Organizing and Presenting Data › Types of diagrams (with examples)
58. Applied Statistics › Organizing and Presenting Data › Diagrametic representation of data:-difference between Diagrams and graphs
59. Applied Statistics › Organizing and Presenting Data › Examples:- based on classification of data
60. Applied Statistics › Organizing and Presenting Data › To calculate number of classes from the given raw data
61. Applied Statistics › Organizing and Presenting Data › Definition:-(raw, discrete ..data,class limit, class interval,class mark, range)
62. Applied Statistics › Organizing and Presenting Data › Classification of data:- Geographical, Chronological, Qualitative, Quantitative
63. Applied Statistics › Organizing and Presenting Data › Examples:- Primary and secondary data
64. Applied Statistics › Describing Distributions › Cauchy Distribution
65. Applied Statistics › Organizing and Presenting Data › Editing:- Primary and secondary data
66. Applied Statistics › Organizing and Presenting Data › Merits and demarits:- Primary and secondary data
67. Applied Statistics › Organizing and Presenting Data › Sources:- Primary and secondary data
68. Applied Statistics › Organizing and Presenting Data › Definition:-Primary and secondary data
69. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Statistics and computers
70. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Statistical Method Vs Experimental methods
71. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Limitations of statistics
72. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Application (use) of statistics
73. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Functions of statistics:- (Definiteness;condensation,comparision,formulating and testing hypothesis,prediction, formulation of suitable policies)
74. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Definitions-1)statistical data 2)statistical methods: - collection, organisation,presentation,interpretation
75. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Origin and growth of statistics
76. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › Student's t Distribution - (formula, properties and examples)
77. Applied Statistics › Probability and Sampling/Distributions › Types of distribution: Discrete and continuous with example
78. Applied Statistics › Linear Regression › Transformations to Achieve Linearity
79. Applied Statistics › Linear Regression › Residual; Outeliers and Influantial Points
80. Applied Statistics › Linear Regression › Relation between regression coefficient and correlation coefficient with examples
81. Applied Statistics › Linear Regression › Explain Regression Coefficients and Regression Equations with examples
82. Applied Statistics › Linear Regression › Definition, Formulae ,Properties of regression coefficient, ,Properties of regression lines.
83. Applied Statistics › Measuring Correlation › Spearman's coefficient of Rank correlation with repeated Ranks, formula with examples
84. Applied Statistics › Measuring Correlation › Spearman's coefficient of Rank correlation definition, formula, examples
85. Applied Statistics › Measuring Correlation › Karl Pearson's coefficient of correlation (Product Moment) short cut method with examples
86. Applied Statistics › Measuring Correlation › Karl Pearson's coefficient of correlation (Product Moment) with examples
87. Applied Statistics › Measuring Correlation › Scatter Diagrams, Interpretation and properties of coefficient of correlation
88. Applied Statistics › Measuring Correlation › Definition and types of correlation,
89. Applied Statistics › Definitions, Uses, Levels of Data Measurement › Introduction
90. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › F Distribution - (formula, coding of data and examples)
91. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › Normal Distribution - (properties, parameter, Area under the normal curve,examples)
92. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › Poisson Distribution - (formula, parameter and examples)
93. Applied Statistics › The Binomial, Poisson,Normal, F and t Distributions › Binomial Distribution - introduction,Pascal's triangle, Properties, Parameters, Negative binomial distribution, examples)
94. Applied Statistics › Describing Distributions › Geometric Distribution
95. Applied Statistics › Describing Distributions › Gamma Distribution
96. Applied Statistics › Describing Distributions › Student's t Distribution
97. Applied Statistics › Describing Distributions › F Distribution
98. Applied Statistics › Describing Distributions › Chi-square Distribution
99. Applied Statistics › Describing Distributions › Poisson Distribution
100. Applied Statistics › Describing Distributions › Normal Distribution