Tiêu đề Statistics for Decision Making PG.pdf ✅

meter Element All rights reserved. No part of this document may be reproduced in any material form (including printing and photocopying or storing it in any medium by electronic or other means or not transiently or incidentally to some other use of this document) without the prior written permission of EBSC Technologies Pvt. Ltd. Application for written permission to reproduce any part of this document should be addressed to the CEO of EBSC Technologies Pvt Ltd. Module – Statistics for Decision-Making Module Objective Throughout this module, you will gain an understanding of key topics such as: • Introduction to Statistics • The Power of Probability • Introduction to Statistical Inference • The Power of Hypothesis Testing • Regression Analysis • Categorical Data Analysis Introduction to Statistics Let's explore different approaches for determining the average weight of women living in India. Option 1: Conduct individual weight measurements Option 2: A thousand women were chosen randomly from various regions of India. We can calculate the average weight of this sample. This average weight can then be used as an estimate for the average weight of the entire population of women in India. What is Statistics? Statistics can be explained as the category of mathematics that is used for summarizing, interpreting, and analyzing the things we observe to bring meaning or make sense to things we observe. This part of the population, which is selected randomly for the study, should be selected such that it represents all the characteristics of the population and is called the sample. Some use-cases of Statistics are : A family counselor might use statistics to describe the patient's behavior or the effect of his treatment. A Psychologist might apply statistics to summarize the peer pressure among youngsters to interpret the cause. A lecturer in the college might use statistics for surveys to summarize and interpret the interest of that typical course. The goals of Statistics are : • Describe Data: One of the fundamental goals of statistics is to describe data effectively. This involves summarizing and presenting data in a meaningful way. It includes measures like mean, median, mode, standard deviation, range, and other descriptive statistics that help in understanding the features of a dataset. Through descriptive statistics, analysts can gain insights into central tendencies, spread, and distributions of the data. • Inferential Analysis: Inferential statistics aims to draw conclusions or forecasts about a population by analyzing a sample of data. It involves using sample data to make conclusions or generalizations about a larger
meter Element All rights reserved. No part of this document may be reproduced in any material form (including printing and photocopying or storing it in any medium by electronic or other means or not transiently or incidentally to some other use of this document) without the prior written permission of EBSC Technologies Pvt. Ltd. Application for written permission to reproduce any part of this document should be addressed to the CEO of EBSC Technologies Pvt Ltd. population. Methods such as hypothesis testing, confidence intervals, and regression analysis are categorized under inferential statistics. The objective is to make population-based conclusions by analyzing a representative sample. • Data Visualization: Data visualization involves presenting data in graphical or visual forms. The goal is to present complex data in a way that is easily understandable and provides valuable insights. Charts, graphs, maps, and other visual representations are used to present data in a more accessible and interpretable manner. The objective is to communicate findings and patterns in the data efficiently. • Probability: Probability quantifies the likelihood of an event taking place. It plays a crucial role in statistics by providing a framework for quantifying uncertainty. It helps in predicting outcomes based on the likelihood of various events occurring. Probability theory is used in various statistical methods, including sampling, hypothesis testing, and decision-making under uncertainty. • Statistical Models: Statistical models are tools used to describe relationships between variables and make predictions based on data. These models help understand the data's structure and can be used to predict future outcomes or behaviors. They range from simple linear models to more complex models like regression, machine learning algorithms, and time series analysis. The goal is to create models that accurately represent the underlying patterns in the data. The applications of statistics in various fields: • Government Agencies: Government agencies extensively use statistics for various purposes, including policy- making, resource allocation, census data analysis, economic indicators, and social welfare planning. Statistics help in understanding demographics, identifying trends, and making informed decisions for the welfare and development of a nation. • Science and Medicine: Statistics is crucial in scientific research and medical studies. It's used to analyze experimental data, conduct clinical trials, assess treatment efficacy, and understand disease patterns. Statistical analysis helps in drawing conclusions and making evidence-based decisions in these fields. • Education: Statistics is applied in education for various purposes, such as evaluating educational programs, assessing student performance, conducting surveys, and analyzing test scores. It aids in identifying learning patterns, improving teaching methodologies, and guiding educational policies. • E-commerce and Trade: In the realm of e-commerce and trade, statistics is used for market analysis, forecasting consumer behavior, optimizing inventory management, understanding customer preferences, and making strategic business decisions. It helps businesses identify trends and target markets and improve efficiency. • Public Surveying: Public surveys often employ statistical techniques to gather, analyze, and interpret data from large populations. These surveys help in understanding public opinions, preferences, and behaviors, aiding in policy-making, market research, and social studies. • Weather Forecast: Weather forecasting heavily relies on statistical models. Meteorologists use historical weather data and statistical methods to predict future weather patterns. Statistics enable them to interpret and predict changes in climate and weather conditions.
meter Element All rights reserved. No part of this document may be reproduced in any material form (including printing and photocopying or storing it in any medium by electronic or other means or not transiently or incidentally to some other use of this document) without the prior written permission of EBSC Technologies Pvt. Ltd. Application for written permission to reproduce any part of this document should be addressed to the CEO of EBSC Technologies Pvt Ltd. • Political Campaigns: Statistics play a significant role in political campaigns. Campaign strategists use polling data, demographic analysis, and survey results to understand voter behavior, preferences, and sentiments. This data aids in shaping campaign strategies and targeting specific voter groups. Key statistical terms include: • Population • Sample • Parameter • Statistics • Universe • Error • Hypothesis Testing • Confidence Interval Let's delve into the detailed understanding of each. Population: The entire group of individuals or objects a researcher is interested in studying. Sample: A subset of the population that is selected for analysis. A sample is used to make inferences about the population. • Parameter: A parameter is a numeric representation that characterizes an attribute of a population, such as the mean or standard deviation. • Statistic: A numerical value that describes a characteristic of a sample, such as a sample mean or sample standard deviation. • Universe refers to the entire set of individuals, objects, or observations a researcher is interested in studying. It represents the population from which a sample is drawn. • Error: It refers to the difference between the observed value and the actual or expected value. • Hypothesis testing: A statistical procedure used to determine whether a hypothesis about a population parameter is supported by the sample data. • Confidence Interval: A confidence interval is an interval of values likely to encompass the true value of a population parameter with a specified level of confidence.
meter Element All rights reserved. No part of this document may be reproduced in any material form (including printing and photocopying or storing it in any medium by electronic or other means or not transiently or incidentally to some other use of this document) without the prior written permission of EBSC Technologies Pvt. Ltd. Application for written permission to reproduce any part of this document should be addressed to the CEO of EBSC Technologies Pvt Ltd. Statistics is categorized into two types: Descriptive Statistics Inferential Statistics Descriptive Statistics • Descriptive Statistics condense information from a sample using measures like the mean or standard deviation. • It provides us with the tools to define our data in the most understandable and appropriate way with the help of charts, tables, and graphs. • Example - We have marks from 1000 students, and we may be interested in their favorite car color and the distribution and spread of marks. Inferential Statistics Inferential statistics involves drawing conclusions and making predictions about a population based on sample data. It uses probability theory and hypothesis testing to make inferences. Statistics Descriptive Statistics Inferential Statistics