Statistical Methods Lab ( R Language) PCCBL308 Course Details and Syllabus

 


STATISTICAL METHODS LAB(R LANGUAGE)

 

Course Code

PCCBL308

CIEMarks

50

Teaching Hours/Week(L:

T:P:R)

0:0:3:0

ESEMarks

50

Credits

2

Exam Hours

2Hrs.30Min.

Prerequisites(if any)

None/

(Coursecode)

Course Type

Lab

 

Course Objectives:

 

1.The statistical methods lab is intended to impart the elementary concepts of R and apply various statistical techniques to a variety of data. This course provides the learners with hands-on experience in R and do Statistical analysis, statistical testing, and graphical analysisandbuildspredictionmodels.Thecourseenablesthestudentstogetanexposure to R programming and use proper methods to analyze and interpret data effectively.

Expt.

No.

EXPERIMENTS

1

Familiarization of R environment and R Studio. Installing and using packages.

 

2

Practice basic R input/output commands and create simple R programs using variables

/mathematical operations.

3

 Learn control statements in R,(if,switch,for,while,repeat,break,next)

4

 Write R programs using functions (Functions, Recursive Functions)

 

5

Learn to use Data Structures in R(strings,vectors,lists,matrix,arrays,dataframes, factors)

 

6

 

Plotting in R(linegraph,scatterplots,barplots,piecharts,histogram,boxplots,strip charts)

 

7

Data Manipulation using R(Rdatasets,basic summary statistics,reading/writing csv and excel files)

 

8

 

Measures of variability and correlation/covariance in R(range,variance,standard deviation ,covariance/correlation)

9

 

Plotting of Probability Distribution Using R Functions(Normal,Binomial,Poisson)

10

Hypothesis testing using R(t-test,chisquare test,Wilcoxon Signed Rank Test)

 

11

 

Regression in R(linear,multiple,logistic)

12

Time series Analysis in R(ARIMA)

 

Course Assessment Method(CIE: 50 marks, ESE: 50 marks)

Continuous Internal Evaluation Marks(CIE):

 

 

 

 

Attendance

Preparation/Pre-Lab Work experiments, Viva and Timely completion of Lab Reports and  Record

(ContinuousAssessment)

 

Internal Examination

 

 

Total

5

25

20

50

 

 

End Semester Examination Marks(ESE):

 

Procedure/ Preparatory work/Design/

Algorithm

Conduct of experiment/ Execution of work/ troubleshooting/

Programming

Result with valid inference/ Quality of

Output

 

Viva voce

 

 

Record

 

 

Total

10

15

10

10

5

50

  •    Submission of Record:Students shall be allowed for the end semester examination only upon submitting the duly certified record.

•    Endorsement by External Examiner:The external examiner shall endorse the record

Course Outcomes(COs)

 

At the end of the course students should be able to:

 

 

Course Outcome

Bloom’s Knowledge

Level(KL)

 

CO1

Perform operations on data using various data structures and programming constructs within R

K3

CO2

Model graphical representation of data and analyze

K3

 

CO3

Perform and interpret different probability distribution and hypothesis testing using R

K3

CO4

Build Regression models for data analysis

K3

CO5

Build Timeseries models for data analysis

K3

Note:K1-Remember,K2-Understand,K3-Apply,K4-Analyse,K5-Evaluate,K6-Create

 

CO-POMapping(Mapping of Course Outcomes with Program Outcomes)

 

 

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO1

1

PO12

CO1

3

3

3

3

3

 

 

 

 

 

 

3

CO2

3

3

3

3

3

 

 

 

 

 

 

3

CO3

3

3

3

3

3

 

 

 

 

 

 

3

CO4

3

3

3

3

3

 

 

 

 

 

 

3

CO5

3

3

3

3

3

 

 

 

 

 

 

3

1:Slight(Low),2:Moderate(Medium),3:Substantial(High),-:NoCorrelation

 

TextBooks

Sl.No

TitleoftheBook

NameoftheAuthor/s

Nameofthe

Publisher

Edition

andYear

1

Hands-on Programming with R

Garrett Grolemund

O'ReillyMedia

 

 

2

R for Everyone:Advanced Analytics and Graphics

 

Jared P.Lander

Addison-Wesley

Data&Analytics Series

2nd Edition

ReferenceBooks

Sl.No

Title of the Book

Name of the Author/s

Name of the

Publisher

Edition

and Year

1

Probability and Statistics for

Engineers

I.R.Miller,J.E.Freund

and R.Johnson.

 

4th

Edition

2

The Analysis of TimeSeries:

An Introduction

Chris Chatfield

 

 

3

Introduction to Linear

Regression Analysis

D.C.Montgomery

&E.Peck

 

 

4

Introduction to the Theory of

Statistics

A.M.Mood,F.A.

Graybill &D.C. Boes.

 

 

5

Applied Regression Analysis

N.Draper&H.Smith

 

 

6

Fundamentals of

Statistics(Vol.I&Vol.II)

A.Goon,M.Gupta

and B.Dasgupta

 

 
Continuous Assessment(25Marks)

1.    Preparation and Pre-Lab Work(7Marks)

 

•  Pre-LabAssignments:Assessment of pre-lab assignments or quizzes that test understanding of the upcoming experiment.

•  UnderstandingofTheory:Evaluationbasedonstudents’preparationandunderstandingofthe theoretical background related to the experiments.

 

2.    Conduct of Experiments(7Marks)

 

• Procedure and Execution:Adherence to correct procedures,accurate execution of experiments, and following safety protocols.

•  Skill Proficiency: Proficiency in handling equipment, accuracy in observations, and troubleshooting skills during the experiments.

•  Teamwork:Collaboration and participation in group experiments.

 

3.    Lab Reports and Record Keeping(6Marks)

 

•  Quality ofReports:Clarity,completenessandaccuracyoflabreports.Properdocumentation of experiments, data analysis and conclusions.

•  TimelySubmission:Adheringtodeadlinesforsubmittinglabreports/roughrecordand maintaining a well-organized fair record.

4.    VivaVoce(5Marks)

 

•  Oral Examination: Ability to explain the experiment, results and underlying principles during a viva voce session.

FinalMarksAveraging:Thefinalmarksforpreparation,conductofexperiments,viva, and record are the average of all the specified experiments in the syllabus.

EvaluationPatternforEndSemesterExamination(50Marks)

 

1.      Procedure/PreliminaryWork/Design/Algorithm(10Marks)

 

• ProcedureUnderstandingandDescription:Clarityinexplainingtheprocedureand understanding each step involved.

• Preliminary Work and Planning:Thoroughness in planning and organizing materials/equipment.

• AlgorithmDevelopment:Correctnessandefficiencyofthealgorithmrelatedtothe experiment.

•  Creativityandlogicinalgorithmorexperimentaldesign.

 

                     2.      ConductofExperiment/ExecutionofWork/Programming(15Marks)

 

•  SetupandExecution:Propersetupandaccurateexecutionoftheexperimentorprogramming task.

3.      ResultwithValidInference/QualityofOutput(10Marks)

 

•  Accuracy of Results:Precisionand correctnessof theobtainedresults.

•  Analysis and Interpretation:Validityofinferencesdrawnfromtheexperimentorqualityof program output.

 

4.      Viva Voce(10Marks)

 

•  Ability to explain the experiment,procedure results and answer related questions

•  Proficiency in answering questions related to theoretical and practical aspects of the subject.

5.      Record(5Marks)

 

•  Completeness,clarity,and accuracy of the lab record submitted

 

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