Practical-V (Statistical Methods in Geography) (B.A Geography)

Measures of central tendency, dispersion and correlation applied to geographical data.

6 MonthsDuration 4Chapters16Topics

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Syllabus · पाठ्यक्रम

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01Experiment Group 1: Data and Frequency Distribution 4 topics

Types and sources of geographical data
Tabulation and class intervals
Frequency distribution and cumulative frequency
Histogram, frequency polygon and ogive

02Experiment Group 2: Measures of Central Tendency 4 topics

Arithmetic mean - direct and assumed mean methods
Median and quartiles
Mode
Relationship between mean, median and mode

03Experiment Group 3: Measures of Dispersion 4 topics

Range and quartile deviation
Mean deviation
Standard deviation
Coefficient of variation - comparing rainfall variability

04Experiment Group 4: Correlation 4 topics

Meaning and types of correlation
Scatter diagram
Karl Pearson's coefficient of correlation
Spearman's rank correlation

About

You will be able to

Organise raw geographical data into frequency distributions and graphs
Calculate mean, median and mode for ungrouped and grouped data
Calculate range, quartile deviation, mean deviation, standard deviation and coefficient of variation
Compute Karl Pearson's and Spearman's rank correlation and interpret them
Present statistical results neatly in the practical record

Who it is for

B.A Geography students of Semester 5 (UP state universities, NEP 2020 syllabus)
Students who find statistics difficult and need step-by-step practice

What you need

Simple calculator, graph paper and practical file
Class 10 level arithmetic

Questions

Which syllabus is followed?

The NEP 2020 common minimum syllabus of UP state universities. Where your university's list of exercises differs, the centre follows your own university syllabus.

Is a computer used?

Exercises are first solved by hand as required in the exam; students may check answers with a spreadsheet.

I am weak in maths. Can I manage?

Yes, only basic arithmetic is needed and every formula is practised with small geographical data sets.