Statistical Methods Syllabus | MBS First Semester

Credits : 3, Lecture Hours: 48

Statistical Methods Syllabus | MBS First Semester

Course Objectives

The course aims to impart knowledge and skills of statistical techniques ad their applications in solving business problems

Course Details

Unit 1: Probability LH6

  • Concept and importance of probability,
  • approaches to probability.
  • Additive and multiplicative theorems,
  • conditional probability,
  • Baye’s theorem and decision tree.

Unit 2: Probability Distribution LH6

  • Discrete probability distribution: Binomial and Poisson,
  • Continuous probability distribution: Normal Distribution and their properties along with applications.

Unit 3: Sampling and Estimation LH6

  • Sampling techniques,
  • sampling and non-sampling errors,
  • sampling distribution,
  • standard error,
  • application of standard error,
  • concept of the central limit theorem
  • Estimation theory,
  • criteria of a good estimator,
  • point and interval estimate,
  • relationship among errors,
  • risk and sample size,
  • determination of sample size

Unit 4: Testing of Hypothesis LH18

  • Meaning of hypothesis testing,
  • types of error in hypothesis testing,
  • critical region,
  • one-tailed and two-tailed tests,
  • Parametric Test: large sample test of mean and proportions,
  • small sample test of mean,
  • paired t-test,
  • test of significance of correlation coefficient,
  • variance ratio test,
  • one-way and two-way Analysis of Variance (ANOVA),
  • Non-parametric test: Chi-square test of goodness of fit and independence of attributes,
  • chi-square test for population variance.

Unit 5: Correlation and Regression Analysis LH12

  • Partial and multiple correlations,
  • coefficient of determination,
  • concept of linear and non-linear regression,
  • multiple regression equation,
  • standard error of estimate for multiple regression,
  • test of regression model and regression coefficients,
  • auto-correlation and multicollinearity,
  • Residual analysis: Linearity of the regression model,
  • Homoscedasticity,
  • Normality of error.

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