Syllabus (GEOGRAPHY)

Course Type: MAJ-5

Semester: 4

Course Code: BGEOMAJ05S

Course Title: Statistical Techniques in Geography

(L-P-Tu): 0-6-0

Credit: 6

Practical/Theory: Practical

Course Objective: Obtain knowledge on fundamental concepts of statistics. Understand the computation of univariate and bivariate statistical analysis of geographical data. Acquire knowledge about the process of interpretation of quantitative data.

Learning Outcome: Students will be able to apply statistical techniques in geographical inquiry. Student will be able to understand the methods and techniques of data collection, organization, and analysis. Student will be able to present scientific arguments based on quan

Unit I: Univariate Analysis [70 Hours]
1.1 Data and Information, Data type, Scale of measurements, Sources of data, Data collection methods, Classification and Tabulation of data. (18 lectures)
1.2 Frequency distribution: Histogram, Frequency Curve, Frequency Polygon, Cumulative Frequency, Ogive. (18 lectures)
1.3 Central tendency: Mean (arithmetic, geometric, and harmonic), Median, Mode; Partition Values. (17 lectures)
1.4 Measures of Dispersion: Range, Mean Deviation, Standard Deviation, Coefficient of Variation and Moments. Shape and Spread: Skewness, Kurtosis. (17 lectures)

Unit II: Bivariate Analysis [86 Hours]
2.1 Association and correlation: Rank correlation, product moment correlation. (15 lectures)
2.2 Regression: Linear, Curvilinear, Parabolic and Geometric. (25 lectures)
2.3 Z-score, Residuals and Standard Error of Estimates. (20 lectures)
2.4 Time series analysis: Secular trend, Seasonal variation, Cyclical variation, Irregular variation, Semi-average, Moving average, Parabola. (26 lectures)

Project File
a) Frequency distribution table (ungrouped and grouped data): equal and unequal class; histogram;
frequency polygon; ogive; graphical representation of mean, median, mode, quartile and percentile.
b) Measures of dispersion: mean deviation, standard deviation and coefficient of variation.
c) Correlation (Pearson and Spearman); Scatter diagram and plotting best-fit line using least-square
method (linear); Z-score and Residual mapping.
d) Time series analysis using Semi average, Moving average and least square.

Selected References:
1. S. N. Pillai and Bagavathi (2007). Statistics: Theory and Practice. S. Chand Company Ltd.
2. N. G. Das (2017). Statistical Methods (Combined Edition). McGraw Hill Education Pvt. Ltd.
3. G. B. Wetherill (1972). Elementary Statistical Methods. Springer.

4. Harris Richard & Claire Jarvis 2011 Statistics for Geography and Environmental Science Paperback. Prentice Hall