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Chapter 1: Introduction to Statistics.pptx
- 1. WEL COME Third Semester Ravi Singh Mahatra Statistics
- 2. Syllabus Unit 1: Descriptive Statistics Introduction to Statistics: descriptive statistics and inferential statistics; types of Data-Qualitative vs. Quantitative data, levels of measurements- nominal level data, ordinal level data, interval level data, and ratio level data and variables. Presentations of data: Frequency Distribution-Simple frequency distribution, relative frequency distribution, and percentage frequency distribution; Rule of constructing the frequency distribution, data array, stem and leaf display; Graphical presentations; bar char and pie chart, histogram, cumulative frequency distribution and Ogive. Descriptive measure of data: Quartiles, Deciles, and Percentiles; five number summary, box-and-Whisker plot, shape of the data-skewness and kurtosis; measure of central tendency-Mean, Median, and Mode; Measures of dispersion: Range, Average Deviation Measures, Standard Deviation, Variance, Relative Dispersion and Coefficient of Variation.
- 3. Chapter :1 Introduction Statistics is concerned with scientific methods for collecting, organizing, summarizing, presenting and analyzing data as well as deriving valid conclusions and making reasonable decisions on the basis of analysis. Statistics are aggregates of facts, Numerically Expressed, enumerated or estimated according to reasonable Standard of accuracy, collected in a systematic manner for a predetermined purpose and placed in relation to each other.
- 4. According to R.A. Fisher, ‘’ The Science of Statistics is essentially a branch of applied mathematics and may be regarded as mathematics applied to observational data.
- 5. Hence, statistics is an essential field that provides the necessary tools to analyze data effectively. It bridges the gap between data and meaningful insights, supporting decision-making across various domains. Statistics is vital for making informed decisions and predictions based on data. It helps in identifying trends, making comparisons, and testing hypotheses. The ability to understand and apply statistical concepts is increasingly important in the data-driven world.
- 6. Functions of Statistics To represent facts from numerical figures in a definite form. To condense the widely and voluminous data. To help classification of data. To provide methods for making comparison. To help formulating policies. To determine relationship between different phenomena. To help predicting future trends. To formulate and test the hypothesis. To have an idea about the occurrence or non-occurrence of certain events. To draw valid inferences or conclusions.
- 7. Scope of Statistics 1. Statistics and economics 2. Statistics and natural science 3. Statistics and physical science 4. Statistics and social science 5. Statistics and Research 6. Statistics and planning 7. Statistics and industrial management 8. Statistics and Banking 9. Statistics and insurance 10. Statistics and commerce
- 8. Limitation of statistics • Statistics does not deal with isolated measurement. • Statistics deals with only quantitative characteristics. • Statistics laws are true on average. It is only aggregate of facts. • Statistical methods are best applicable on quantitative data. • Statistical methods cannot be applied to heterogeneous data. • If sufficient care is not exercised in collecting, analyzing and interpretation of data, statistical results might be misleading. • Only a person who has an expert knowledge of statistics can handle statistical data efficiently. • There are always some possibilities of error in statistical decisions.
- 9. Classification of Statistics Descriptive Statistics Inferential Statistics
- 10. Descriptive Statistics Descriptive Statistics describes the data and consists of methods and techniques used in collection, organization, presentation of data using measure of central tendency, dispersion, skewness, kurtosis etc. Analysis of data in order to describe various features and characteristics of such data is called descriptive statistics. Hence summarized results is obtained from descriptive statistics which can describe the data but can not be used to generalized.
- 11. Information, that necessary for any study is achieved in different forms. The main forms of the information available are as following. 1. Qualitative Data 2. Quantitative Data 3. Cross Section Data 4. Time series Data DATA TYPES
- 12. Cross- Sectional Data Cross- Sectional data refers to data collected by observing many subjects at the one point or period of time. It is a snapshot of observation at a particular point. For example; Population of women in census year 2068. Time- Series Data The data which can be recorded over different periods of time is called time series data. In this case same measurements are recorded on regular basis. For example; population of Nepal in census year 2048, 2058, 2068.
- 13. Qualitative data This data is descriptive, interpretation-based, and related to language. It's used to answer questions about why, how, or what happened behind certain behaviors. Qualitative data is subjective and unique. It's analyzed by grouping the data into categories and themes. • Quantitative data • This data is numerical, countable, or measurable, and is expressed as numbers. It's used to answer questions about how many, how much, or how often. Quantitative data is fixed and universal. It's analyzed using statistical analysis.
- 14. Population A population can be defined as an aggregate observation of subjects grouped together by a common feature. Population is the entire pool from which a statistical sample is drawn. Census survey is conducted to enumerate all the population units. Based on the number of individuals belonging to the group, population can be divided into two types; I. Finite population II. Infinite Population
- 15. Finite Population The finite population is also known as a countable population in which the population can be counted. In other words, it is defined as the population of all the individuals or objects that are finite. For statistical analysis, the finite population is more advantageous than the infinite population. Examples of finite populations are employees of a company, potential consumer in a market. Infinite Population The infinite population is also known as an uncountable population in which the counting of units in the population is not possible. Example of an infinite population is the number of germs in the patient’s body is uncountable.
- 16. Based on the type of individuals in population, population can be divided into two types. I. Homogeneous Population Population consisting of individuals of same type is called homogeneous population. II. Heterogeneous Population Population consisting of individuals of different type is called heterogeneous population.