Topic outline

  • Welcome Note video with Applications




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    • MKH

    • Teacher's Information

      Name:  Dr. Md. Kamrul Hossain

      Designation:  Associate Professor & Head         

      Department:   General Educational Development

      Desk:   806, AB 4, DSC

      Contact Number:   01711069636

      Mail:   kamrul.ged@diu.edu.bd

      		          


      For more information click here ....

    • Course Rationale 

      Statistics is the science that deals with the collection, description, analysis, interpretation, and presentation of data. Statistical methods in Statistics are applied in the solution of different problems in real life and help the researcher to make an effective decision that affect our daily lives. Statistical methods are used in development of planning, commerce, industry, business, formation of development policy, agricultural sector, social science etc. By studying this course, you will learn the fundamental knowledge about descriptive statistics and their applications.

    • Course Objectives

      This course intends to provide the students with the basic concepts of Statistics. Upon completion of this course, the student should be able -
      1. to provide the students with a broad application of the statistical methods along with probability theory.
      2. to have a working knowledge of most of the statistical concepts.
      3. to understand the theoretical and practical knowledge of statistics.
      4. to apply their statistical knowledge and skills throughout their future studies.


    •  Course Outcomes (CO’s)


       Program Outcomes (PO’s)



    •  Assessment Plan


    • Communication

    • Reference Text Book

      Introductory Statistics


    • Schedule

    • General Instruction for Assessment

      1. Quiz - There will be 3 quiz. Average weight of the quiz for the course is 15%. Before mid term, you have to sit for Quiz 1 and 2.  
      2. Presentation - Presentation topic is open, that is you can choose any topic for presentation but there must be data. Prepared at least 3 minutes video and upload  within due data in the presentation section. weight of presentation is 8%.
      3. Assignment - There will be several assignment and activity  and the weight is 5%.
      4. For attending class, you will get 7% mark of the course. 

    • Telegream Chat

      Please contact in telegram/whatsapp in 01915641745


  • Class 01 

    (Data Presentation)

    Class 02 

    (Data Presentation &

    Measures of Central Tendency)

    Class 03 

    (Mean, Median, Mode)

    Class 04 

    (Mean, Median, Mode)

    Class 05 

    (Mean, Median, Mode)

    Class 06 

    (Measures of Dispersion) 

    Class 07 

    (Measures of Dispersion)


    • Lecture 1

      • Introduction

        At the very beginning of each semester, it needs to introduce myself to the students. It's also important to get a clear understanding of the students' abilities, performance, strengths, and weaknesses at the very beginning of the semester. As a result, it's easy to reach the students. So a nice introductory class help to buildup connection between students and teacher which make easier to continue the whole course.
        When the students are starting a new course, they have some common questions like that,
        a) Why they read this course?
        b) Have any real-life application of this course?
        I think these are very important questions and we must do fulfill their demand.
        Hence, in my introductory class, I always try to buildup a clear understanding among the students and try to reach their emotion, their feelings, their strengths, and weaknesses. Clearly I try to show the necessity of this course and real-life application of this course.
        So, I think at the very beginning of the semester, a good introductory class helps to make my course easier than the other classes.

    • Lecture 2 -3 (Introduction)


      • Introduction

        You are thinking this a new course which you have never read. However, you have learn statistics as integrated part of mathematics during SSC and HSC. That is why we are going to recall few related terminology of Statistics.  

      • Chapter Outcome

        After reading this chapter, you should be able to:
        1. Know about the Statistics, Population, Sample, Variable
        2. Classify variables into qualitative and quantitative
        3. Importance of data and its use

      • Lesson Plan

          • Discussion from a lecture slide
          • Group work and also individual classwork
          • Question and Answering

      • Lecture Related Supportive Materials:

                       PowerPoint Slide on This Lecture is HERE


                      The following video tutorial on this topic can be helpful




    • This topic

      Lecture 4 (Level of Measurement)


      • Introduction

        In this chapter we will learn to assign measurement to the variables.

      • Chapter Outcome

        After reading this chapter, you should be able to:
        1.     Know about classification of level of measurement
        2.    Classify the variables to level of measurement 

      • Lesson Plan:

          • Discussion from a lecture slide
          • Group work and also individual classwork
          • Question and Answering

      • Lecture Related Supportive Materials:

        PowerPoint Slide on This Lecture is HERE



        Video Lecture on Level of Measurement
        Video Lecture Supporting Document from External Source

    • Data Presentation

      • Lecture 5 & 6 (Data Presentation)


        Introduction

        In lecture 5 & 6, we will discuss and learn about construction of frequency distribution and drawing various types of charts and diagram based on type of data.

        Learning Outcomes:

        Know the appropriate tool for data presentation
        Exploring fact from data

        Lesson Plan:

                 *Question and Answering Forum
                 *Discussion from a lecture slide
                  *Presentation

        Lecture Related Supportive Materials:

        PowerPoint Slide on This Lecture is Here: Lecture 1 & Lecture 2



        Video Lecture on Data Presentation

         Part 1

        Part 2

    • Lecture 7 and 8 (Measures of Central Tendency)


      • Introduction

        In this class, I will discuss the different types of central tendency and what are the applications and importance in real life.

      • Chapter Outcome

        After reading this chapter, you should be able to:
        1.     Know about central tendency
        2.     Calculate central tendency

      • Lesson Plan

          • Discussion from a lecture slide
          • Group work and also individual classwork
          • Question and Answering

      • Lecture Related Supportive Materials

        PowerPoint Slide on This Lecture is Part 1  & Part 2
        Lecture Related Supportive Materials
        Part 1 Part 2 Funny Video from External Source

    • Lecture 9 (Measures of Location)


      • Introduction:

        Summarizing data can help us understand them, especially when the number of data is large. Measures of location try to capture with a single number what is typical of the data. Without defining "representative" more precisely, it is not possible to get information from data.

      • Chapter Outcome:

        After reading this chapter, you should be able to:

        1.     Know about calculate quartiles, deciles and percentiles
        2.     Detect outlier 
        3.     Draw boxplot

      • Lesson Plan:

          • Discussion from a lecture slide
          • Group work and also individual classwork
          • Question and Answering

      • Lecture Related Supportive Materials:

        PowerPoint Slide on This Lecture is  Part 1 and Part 2


        Video Class Material
        Part 1 Part 2  External Link

    • Lecture 10 - 11 (Measures of Dispersion)

      Lecture 10 - 11 (Measures of Dispersion)

      • Introduction

        Correlation measures the relationship among the variables. In this chapter, you will learn to calculate correlation between the variables. And you will know which method is appropriate for which type of data.  


      • Objectives

        Dispersion refers to measure the spreadness of a dataset. From this chapter you will learn to

                                         Measure the absolute and relative measures of dispersion

                                         Application of the different type of dispersion

      • Lesson Plan:

          • Discussion from a lecture slide
          • Group work and also individual classwork
          • Question and Answering

      • PowerPoint Part 1 and Part 2

        Lecture Related Supportive Materials:

    • Week 8-Module (Chapter 8):Regression Analysis


      • Introduction

        The shape of the data can be understood by considering how the data points are distributed in the space. This distribution can be categorized into Symmetrical Distribution. Two numerical measures of shape give a more precise evaluation: 
        skewness tells you the amount and direction of skew (departure from horizontal symmetry)
        kurtosis tells you how tall and sharp the central peak is, relative to a standard bell curve.


      • Learning Outcomes

        •Measures the Shape of the distribution
        •Computation process of Skewness and Kurtosis
        •Interpreting Skewness and Kurtosis


      • Objectives

        Understand the shape of distribution
        Measure the lack of symmetry of the distribution

      • For chapter materials,  the learning material click HERE

    • Correlation

      Lecture 12-13(Correlation)

      • Introduction

        Correlation measures the relationship among the variables. In this chapter, you will learn to 

        1. Identify the direction and strength of a linear correlation between two factors 

        2. Interpret the Pearson correlation coefficient and how to determine the coefficient

        3. Draw scatter diagram and its explanation in aspect of correlation

      • Objectives

        1. Measures the relationship among variables

        2. Explanation of scatter diagram

      • Lesson Plan

          1. Discussion from a lecture slide
          2. Group work and also individual classwork
          3. Question and Answering

        • Lecture Related Supportive Materials:

        • PDF  1                               PDF  2

          Video


        • Correlation Interactive Content
          Complete the activity


      • Lecture 14- 15(Regression)

        • Introduction

          At the end of the chapter students will learn 
          1. Distinguish between a independent variable and a dependent variable
          2. Measuring influence of independent variable on dependent variable
           3.  Explaining a line from the view of regression

        • Objectives

          1. Define the explanatory variable as the independent variable
          2. Predict the value of the predict variable for a given value of the explanatory variable

        • Lesson Plan:

            • Discussion from a lecture slide
            • Group work and also individual classwork
            • Question and Answering


        • Lecture Related Supportive Materials:

        • PDF 1       PDF 2



        • Class work Forum

      • Lecture 16- 18(Test of Hypothesis)

        • Introduction

          Hypothesis is the statement about population. In every research, without testing hypothesis is not valid or worthy. 



        • Objectives

          1. Learn to set hypothesis

          2. Know how to test hypothesis

        • Lesson Plan:

            • Discussion from a lecture slide
            • Groupwork and also individual classwork
            • Question and Answering

        • Lecture Related Supportive Materials:  HERE

        • Hypothesis testing


      • Lecture 19- 21 (Probability) 

        • Introduction

          Probability measures and quantifies "how likely" an event, related to these types of experiment, will happen. The value of a probability is a number between 0 and 1 inclusive. An event that cannot occur has a probability (of happening) equal to 0 and the probability of an event that is certain to occur has a probability equal to 1. 


        • Objectives

          1. Introduce to Probability
          2. Application of Probability in Real Life

        • Lesson Plan:

            • Discussion from a lecture slide
            • Group work and also individual classwork
            • Question and Answering

        • Lecture Related Supportive Materials:        


          PPT


      • Lecture 22- 24(Probability  Distribution)

        • Introduction

          A probability distribution is a list of all of the possible outcomes of a random variable along with their corresponding probability values.


        • Objectives

          Know the application of probability distribution

        • Lesson Plan:

            • Discussion from a lecture slide
            • Groupwork and also individual classwork
            • Question and Answering

        • Lecture Related Supportive Materials:


        • Assignment
          View Make a submission

          What do you learn from DEV 532? How will you apply in real life? Discus in details. 

      • Quiz

        • Quiz 2
          View

          You are allow to use 25 there will be 2 attempts. Sequential movement

          Content: i) Measures of Central Tendency
                         ii) Measures of Location
                         iii) Measures of Dispersion
                         iv) Shape of the Distribution

        • Quiz 3
          View

          Content: Correlation, Regression and Probability

      • Final Exam