Statistics 101: A Clear and Visual Path to Mastery.pptx

Explore the essential building blocks of statistics with 'Statistics 101: A Clear and Visual Path to Mastery.' Tailored for beginners and those looking to strengthen their understanding, this guide breaks down complex statistical concepts into easy-to-follow, visually engaging lessons. You'll gain a solid grasp of fundamental topics such as population vs. sample, variables (including independent, dependent, continuous, and discrete), data collection techniques, sampling size determination, and the difference between parametric and non-parametric data.

This presentation emphasizes clarity and simplicity, offering step-by-step explanations and illustrative graphics to demystify the core elements of statistics. Learn how to categorize data—whether it’s primary or secondary, qualitative or quantitative—and how to present this data effectively using textual, graphical, and tabular methods. Understand the significance of measures of central tendency, including mean, median, and mode.

Whether you’re preparing for exams, conducting research, or simply interested in understanding the role of statistics in decision-making, 'Statistics 101' provides the foundational knowledge you need. Master statistical analysis with confidence and apply these concepts to real-world scenarios. This guide is not just a learning tool but a visual journey through the fascinating world of data.

1. Presenter a : Abdullah Al Nafees Creators b : Mahbub Hassan, Abdullah Al Nafees, Hridoy Deb Mahin , Saikat Sarkar Shraban , Arpita Paul Affiliations: a Faculty of Civil Engineering & Technology, Universiti Malaysia Perlis ( UniMAP ), Arau, 02600, Perlis, Malaysia. b Sylhet Engineering College (SEC), School of Applied Sciences & Technology, Shahjalal University of Science and Technology, Tilagarh , Alurtol Road, Sylhet 3100, Bangladesh. For Contact: [email protected] “Statistics 101: A Clear and Visual Path to Mastery”

2. What is Statistics? Statistics is the study of data .

3. Application of Statistics Statistics Is Everywhere!

4. Main Purposes of Statistics

5. Major Use

6. How Accurate is Statistics? Benamin Disraeli Statistics Is Accurate For A Large Data Set

7. Population Vs Sample Sample is a subset of population

8. Criteria For Sample Sample Represtentative Random

9. Sampling Size Formula

10. Sampling Size Determination

11. Variables A variable is something that can change or vary. It's a characteristic, number, or quantity that can be measured or observed in a study or experiment. Variable Numerical Categorical Represent quantities and can be measured on a numerical scale. Examples include age, height, weight, temperature, and income. Represent categories or groups and cannot be measured on a numerical scale. Examples include gender, marital status, ethnicity, and type of car.

12. Variables Variables can also be further categorized based on their nature: Variable Continuous Discrete A variable that can take any value within a certain range. Examples include height, weight, temperature, and time A variable that can only take specific, distinct values and cannot be subdivided further. Examples include the number of children in a family, the number of cars owned by households, and the number of defects in a product.

13. Variables Variables can also be grouped into categories based on their characteristics. Variable Independent Dependent This is something we can control or change, like how much fertilizer we give to plants in an experiment. This is what we measure or observe, like how tall the plants grow after receiving different amounts of fertilizer.

14. Data Data in statistics means information collected for analysis, including numbers and observations. Data Primary Secondary Primary data is collected firsthand Secondary data is gathered from existing sources like books or databases

15. Data Data in statistics means information collected for analysis, including numbers and observations. Data Parametric Non-Parametric Follows a normal distribution and has specific characteristics, such as a known mean and standard deviation Does not require such assumptions and is often used for categorical or ordinal variables

16. Data Data Based on Scenarios Data Geographical Quality Based Chronological Qualitative Quantitative Time Based Location Based Quantity Based

17. Data Presentation Presentation Types Textual Graphical Tabular This is a textual Presentation

18. Student's t-test Analysis of Variance (ANOVA) Pearson correlation coefficient Linear regression analysis Chi-square test for goodness of fit (when applicable) Mann-Whitney U test (Wilcoxon rank-sum test) Kruskal-Wallis test (non-parametric equivalent of ANOVA) Spearman rank correlation coefficient Friedman test (non-parametric equivalent of repeated measures ANOVA) Chi-square test of independence (for contingency tables) Tests for analyzing Parametric Data Tests for analyzing Non-Parametric Data Data Analysis Tests

19. Measures Of Central Tendency