Applied Statistics – BSc 2nd Year.

Crack your exam and take your statistical skills to the next level with our Applied Statistics course for BSc 2nd Year students. This course covers essential topics including data analysis, probability distributions, hypothesis testing, correlation, regression, and much more.

Perfectly tailored to align with your BSc curriculum given below:

Full marks:50

Pass Marks:17.5

Time:2 hrs.

1. Methods of Data Summarization: Review of basic concept of Statistics, Scales of measurement,

data distribution, diagrammatical and graphical presentation of data, measures of central tendency, measures of dispersion, measures of skewness, measures of kurtosis. Numerical problems related to physical and biological sciences. [10]

2. Correlation: Karl Pearson’s correlation. Spearman rank correlation, Kendal Tau correlation. Numerical problems related to physical and biological sciences.

3. Methods of Data Modeling: Principles of Ordinary Least Squares (OLS), linear regression up to three variables, methods of fitting of first and second degree equations, exponential curves, partial and multiple correlations, analysis of residuals, Fisher decomposition of total sum of squares, coefficient of determination an and its interpretation. Numerical problems related to to physical and biological sciences. [13]

4. Analysis of Categorical Data: Class frequencies, relation between class frequencies, consistence of data, condition for consistency of data, independence and association of attributes, Yule’s method and coefficient of contingency. Yule’s coefficient of colligation. Pearson’s coefficient of contingency and their interpretation. Numerical problems related to physical and biological sciences. [8]

5. Introduction to Probability: Basic concept of probability, fundamental rules of probability, marginal, joint and conditional probabilities (Concepts and applications only focusing on numerical problems related to physical and biological sciences) [5]

6. Probability distributions: Binomial distribution, Poisson distribution, Normal distribution(characteristics and applications without derivation focusing on numerical problems related to physical and biological sciences)

7.Estimation: Point & interval estimation, confidence interval for mean and proportion, determination of sample size, relationship of sample size with desired level of error Numerical problems related to physical and biological sciences

8.Hypothesis Testing: Types of statistical hypotheses – null and alternative hypothesis, type I and type II errors, level of significance, critical value and critical region, concept of p-value and use of p-value in hypothesis testing, steps used in testing of hypothesis, one sample tests for mean of normal population (for known and unknown variance), test for proportion, test for difference between two means and two proportions, paired sample t-test, two independent sample tests for variances of normal populations, relationship between hypothesis testing and confidence interval, one way and two way ANOVA, test of significance of simple correlation and regression coefficients. Numerical problems related to physical and biological sciences [20]

9.Nonparametric tests: Needs of applying non-parametric tests, short introduction of the alternative tests of parametric tests, Chi-square test for independence of attributes and test for goodness of fit (Focusing on numerical problems related to physical and biological sciences).

[5]

This Course provides:

Clear explanations of complex concepts

Step-by-step examples and practical applications

Exercises to reinforce learning and ensure mastery

Whether you’re aiming to ace your exams or build a strong foundation for research and data analysis, this course is your gateway to understanding statisties in a practical and meaningful way