1. Introduction to Statistics
Introduction to Statistics, Importance of Statistics in modern business environment. Definition of Statistics, Importance, Scope and Applications of Statistics, Characteristics of Statistics,
Functions of Statistics, Limitations of Statistics.
2. Data Condensation
Need of Data, Types of Data, Principles of Measurement, Source of Data, Data Classification
3. Tabulation
4. Measures of Central Tendency
Introduction, Objectives of statistical average, Requisites of a Good Average, Statistical Averages – Arithmetic mean –
Properties of arithmetic mean – Merits and demerits of arithmetic mean ,Median – Merits and demerits of median , Mode – Merits and demerits of mode , Geometric Mean ,
Harmonic Mean , Appropriate Situations for the Use of Various Averages , Positional Averages
5. Measures of Dispersion
Dispersion – Range – Quartile deviations, Mean deviation ,Standard Deviation -Properties of standard deviation Coefficient of Variance
6. Sampling and Sampling Theory
Introduction , Population and Sample – Universe or Population – Types of Population – Sample , Advantages of Sampling,
Sampling Theory – Law of Statistical Regularity – Principle of Inertia of Large Numbers – Principle of Persistence of Small Numbers – Principle of Validity – Principle of Optimization , Terms Used in Sampling Theory, Errors in Statistics, Measures of Statistical Errors, Types of Sampling – Probability Sampling – NonProbability Sampling, Case let on Types of Sampling,
Determination of Sample Size, Central Limit Theorem
7. Test of Hypothesis
Introduction – Testing of Hypothesis in Case of Small Samples – Introduction –
small samples, ‘t’ Distribution , Uses of ‘t’ test
8. Large Sample Test
Large Samples – Assumptions , Testing Hypothesis – Null and alternate hypothesis – Interpreting the level of significance – Hypotheses are accepted and not proved , Selecting a Significance Level – Preference of type I error – Preference of type II error – Determine appropriate distribution, Two – Tailed Tests and One – Tailed Tests – Two – tailed tests –
Case study on two –tailed and one-tailed tests, Classification of Test Statistics – Statistics used for testing of hypothesis – Test procedure – How to identify the right statistics for the test.
9. X2-test and t-test
Introduction , Chi-Square as a Test of Independence – Characteristics of ⎟2 test – Degrees of freedom – Restrictions in applying ⎟2 test – Practical applications of ⎟2 test – Levels of
significance – Steps in solving problems related to Chi-Square test – Interpretation of Chi-Square values , Chi-Square Distribution – Properties of ⎟2 distribution – Conditions for applying the ChiSquare test – Uses of ⎟2 test , Applications of Chi-Square test – Tests for independence of attributes – Test of goodness of fit – Test for specified variance
10. Correlation and Regression
Introduction , Correlation – Causation and Correlation – Types of Correlation – Measures of Correlation – Scatter diagram – Karl
Pearson’s correlation coefficient – Properties of Karl Pearson’s correlation coefficient – Factors influencing the size of correlation
coefficient , Probable Error – Conditions under which probable error can be used , Spearman’s Rank Correlation Coefficient ,
11. Multiple and Partial Correlation
Partial Correlations , Multiple Correlations , Regression – Regression analysis – Regression lines – Regression coefficient , Standard Error of Estimate , Multiple Regression Analysis ,
Reliability of Estimates , Application of Multiple Regressions
12. Time Series
Introduction, Types of forecasts, Timing of forecast, Forecast methods-Quantitative and Qualitative Forecasting Methods,
Time Series Analysis -Introduction, Time Series Analysis , Utility of the Time Series , Components of Time Series – Long term trend or secular trend – Seasonal variations – Cyclic variations – Random variations, Methods of Measuring Trend – Free hand or graphic method – Semi-average method – Method of moving averages – Method of least squares, Mathematical Models for Time Series – Additive model – multiplicative model, Editing of Time Series, Measurement of Seasonal Variation – Seasonal average method – Seasonal variation through moving averages – Chain or link relative method – Ratio to trend method, Forecasting Methods Using Time Series – Mean forecast – Naive forecast – Linear trend
forecast – Non-linear trend forecast – Forecasting with exponential smoothing
13. Index Numbers
Introduction, Definition of an Index Number – Relative – Classification of index numbers , Base year and current year – Chief characteristics of index numbers – Main steps in the
construction of index numbers, Methods of Computation of Index Numbers – Un-weighted index numbers – Weighted index numbers, Tests for Adequacy of Index Number Formulae , Cost of Living Index Numbers of Consumer Price Index – Utility of consumer price index numbers – Assumptions of cost of living index number – Steps in construction of cost of living index numbers , Methods of Constructing Consumer Price Index – Aggregate expenditure method – Family budget method – Weight average of price relatives, Limitations of Index Numbers , Utility and Importance of Index Numbers
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