Module Code and Title: STS101 Applied
Statistics
Programme(s): BSc Environmental
Management
Credit Value: 12
Module
Tutor(s): Somnath
Chaudhuri (Coordinator)
GP Sharma
Radhika
Chhetri
Leishipem
Khamrang
General objective(s) of the module:
This module
will provide students with an introduction to statistics and allow them to
directly begin applying statistical concepts to problems. In terms of covering
the theoretical and mathematical basis, the module aims to provide a basic
understanding of the concepts without extensively emphasizing the theoretical
math. Rather, the module takes the approach of allowing students to directly
discover statistics by applying and practicing statistics using the SPSS
software platform, with some supplementation using Microsoft Excel. Lessons are
geared towards reinforcing the theory with practical exercises. The module uses
environmental data, problems, and cases as the content to illustrate the
statistical analyses.
Learning outcomes – Upon successful completion of the
module, students will be able to:
- Describe how statistics can be used for
addressing research questions and analysing data.
- Define essential statistical concepts and
terms.
- Recall the theoretical basis for common
statistical tests and techniques.
- Apply statistical techniques for
analysing data using SPSS and Excel.
- Recognize which statistical techniques
are most suitable to address particular problems.
- Test hypotheses using appropriate
statistical tests and techniques.
- Correctly interpret the outputs of
statistical analyses, in numerical terms and through graphs.
- Identify environmental problems and data
that can be analysed with statistics.
Skills to be developed:
·
Students
should be able to perform statistical calculations using SPSS and Excel.
Learning and teaching approaches used:
The module will be conducted
over 15 teaching weeks as follows:
·
3
hrs/wk lecture & discussions.
·
2
hrs/wk practical work in computer lab.
·
3
hrs/wk outside of class, on average, for independent study and practice.
Assessment:
Semester-End
Examination (SE):40%
Continuous Assessment
(CA): 60%
|
CA
Assessment
|
Weight
|
Assessment
Detail
|
|
Weekly exercises (15 x 3%)
|
45%
|
Each written exercise will involve solving practice
problems covered on a weekly basis. The work will require use of the lab time
as well as effort outside of class.
|
|
Midterm Exam
|
15%
|
|
Pre-requisite knowledge:
Subject matter:
I.
Introduction
to using statistics
a. The research
process; making observations, generating theories and testing them
b. Introduction
to data collection and analysis
i. What to
measure: variables, measurement error, validity and reliability
ii. How to
measure: correlational research methods, experimental research methods,
randomization
iii. Analysing
data: frequency distributions (types, centre, dispersion), going beyond the
data, fitting statistical models
II.
Essentials
of statistics
a. Building
statistical models
b. Populations
and samples
c. Simple
statistical models: mean and variance
d. Going beyond
data: standard error, confidence intervals
e. Using
statistical models to test research questions
i. Test
statistics
ii. One- and two-tailed
tests
iii. Types I and
type II errors
iv. Effect size
v. Statistical
power
III.
Basics
of SPSS
a. Overview of
the SPSS environment
b. Data editor
c. Variable
view
d. Syntax
window, outputs
e. File
management
IV.
Exploring
data with graphs
a. ‘Art’ of
presenting data properly and reading graphs accurately
b. Chart making
in SPSS
c. Types of
charts, their uses and suitability for different purposes (column and bar
graphs, histograms, boxplots, line charts, scatterplots)
V.
Exploring
assumptions
a. Meaning and
effect of assumptions in statistics
b. Assumption
of normality
c. Homogeneity
of variance
d. Correcting
problems in data (outliers, non-normality, unequal variances)
VI.
Correlation
a. Introduction
to measuring relationships and establishing correlation
b. Types of
correlative analyses and different coefficients of correlation
c. Calculating
effect size
d. Reporting
correlation coefficients
VII. Regression
a. Introduction
to regression; least squares; goodness of fit
b. Simple
regression
c. Fitting,
assessing, and interpreting a regression model
VIII. Comparing
Means
a. Concept of
testing for differences between groups, samples
b. t-test
(dependent, independent)
c. Testing
between groups vs. between repeated measures
IX.
ANOVA
a. Theory,
principles and uses of ANOVA
b. Running
one-way ANOVA in SPSS and interpreting the output
X.
Categorical
data
a. Analysing categorical
data
b. Statistical
theories and tools for categorical data (Pearson’s chi-square test, Fisher’s
exact test, likelihood ratio, Yates’ correction)
c. Performing
chi-square analysis in SPSS
Essential Readings:
1. Field, A.
(2013). Discovering Statistics using IBM SPSS Statistics 4th
Edition. New Delhi: Sage Publications.
2. Manly,
B.F.J. (2009). Statistics for Environmental Science and Management. Boca Raton:
Chapman & Hall/CRC.
Additional Readings:
1. Rumsey, D.J.
(2011). Statistics for Dummies 2nd Edition. Hoboken: Wiley
Publishing.
2. Rumsey, D.J.
(2009). Statistics II for Dummies. Hoboken: Wiley Publishing.
3. Urdan, T.C.
(2005). Statistics in Plain English 2nd Edition. New Jersey:
Lawrence Erlbaum Associates, Inc.
Date last updated: May 30, 2015