This document provides an introduction to key concepts in statistics. It discusses various statistical measures such as measures of central tendency (mean, median, mode), measures of dispersion (range, standard deviation), correlation, and different types of correlation (simple, partial, multiple). It also outlines common statistical methods like scatter diagrams, Karl Pearson's method, and rank correlation method. The role of computer technology in statistics is mentioned.
Introduction to statistics for social sciences 1Minal Jadeja
This document provides an introduction to statistics. It defines statistics as the collection, presentation, analysis, and interpretation of numerical data. Statistics can refer to either quantitative information or a method of dealing with quantitative or qualitative information. There are two main approaches in statistics - descriptive statistics, which deals with presenting data in tables or graphs to get a general picture of a sample, and inferential statistics, which involves techniques for making inferences about a whole population based on a sample. Some key uses and applications of statistics include showing how samples differ from normal distributions, facilitating comparisons, simplifying messages in data, helping to formulate and test hypotheses, and aiding in prediction and inference. However, there are also some limitations to consider with statistics, such
This document provides an overview of data analysis and statistics concepts for a training session. It begins with an agenda outlining topics like descriptive statistics, inferential statistics, and independent vs dependent samples. Descriptive statistics concepts covered include measures of central tendency (mean, median, mode), measures of variability (range, standard deviation), and charts. Inferential statistics discusses estimating population parameters, hypothesis testing, and statistical tests like t-tests, ANOVA, and chi-squared. The document provides examples and online simulation tools. It concludes with some practical tips for data analysis like checking for errors, reviewing findings early, and consulting a statistician on analysis plans.
The document provides information on the course content and practical components of a statistics course.
The theory section covers topics like introduction to statistics, measures of central tendency and dispersion, probability, normal distribution, correlation, regression, tests of significance, analysis of variance, and sampling methods.
The practical section involves applying concepts like graphical representation of data, measures of central tendency and dispersion, moments, correlation and regression analysis, t-tests, chi-square tests, and analysis of variance to real data. Reference books on agricultural statistics are also listed.
Correlation analysis measures the relationship between two or more variables. The correlation coefficient ranges from -1 to 1, indicating the strength and direction of the linear relationship. A positive correlation means the variables increase together, while a negative correlation means they change in opposite directions. Correlation only measures association and does not imply causation. Common methods for calculating correlation include Pearson's correlation coefficient, Spearman's rank correlation coefficient, and scatter plots.
This document provides an overview of descriptive statistics. It discusses different types of descriptive statistics including measures of central tendency like mean, median and mode, and measures of variability. It also describes various ways of organizing and summarizing data, such as frequency distributions, histograms, stem-and-leaf plots and pie charts. The goal of descriptive statistics is to describe key characteristics of a data set in a simple and easy to understand way.
This document discusses key concepts in statistical estimation including:
- Estimation involves using sample data to infer properties of the population by calculating point estimates and interval estimates.
- A point estimate is a single value that estimates an unknown population parameter, while an interval estimate provides a range of plausible values for the parameter.
- A confidence interval gives the probability that the interval calculated from the sample data contains the true population parameter. Common confidence intervals are 95% confidence intervals.
- Formulas for confidence intervals depend on whether the population standard deviation is known or unknown, and the sample size.
This document discusses regression analysis techniques. It defines regression as the tendency for estimated values to be close to actual values. Regression analysis investigates the relationship between variables, with the independent variable influencing the dependent variable. There are three main types of regression: linear regression which uses a linear equation to model the relationship between one independent and one dependent variable; logistic regression which predicts the probability of a binary outcome using multiple independent variables; and nonlinear regression which models any non-linear relationship between variables. The document provides examples of using linear and logistic regression and discusses their key assumptions and calculations.
The document discusses different measures of central tendency:
- The mode is the most frequent score in a data set and is used for nominal data.
- The median is the middle score when data is arranged from lowest to highest and is not influenced by outliers.
- The mean is the average of all scores, calculated by summing all scores and dividing by the total number, and is preferred for interval/ratio scaled and non-skewed data.
Basic statistics is the science of collecting, organizing, summarizing, and interpreting data. It allows researchers to gain insights from data through graphical or numerical summaries, regardless of the amount of data. Descriptive statistics can be used to describe single variables through frequencies, percentages, means, and standard deviations. Inferential statistics make inferences about phenomena through hypothesis testing, correlations, and predicting relationships between variables.
HERE I'VE GIVE THE DESCRIPTION ABOUT OUTLIERS AND IT'S TYPES WITH SOME EXAMPLE.
My Facebook handle _ https://www.facebook.com/profile.php?id=100066529451772
hello, friends. i'm humaira jahan. and this is my presentation on statistics. you can find a overall concept of statistics in this presentation. and i hope it will help you enough to know about statistics.
Statistics is the methodology used to interpret and draw conclusions from collected data. It provides methods for designing research studies, summarizing and exploring data, and making predictions about phenomena represented by the data. A population is the set of all individuals of interest, while a sample is a subset of individuals from the population used for measurements. Parameters describe characteristics of the entire population, while statistics describe characteristics of a sample and can be used to infer parameters. Basic descriptive statistics used to summarize samples include the mean, standard deviation, and variance, which measure central tendency, spread, and how far data points are from the mean, respectively. The goal of statistical data analysis is to gain understanding from data through defined steps.
This document discusses inferential statistics and different types of samples that can be drawn from a population. Inferential statistics involves making inferences about a population based on a sample. It consists of generalizing from samples to populations, estimating parameters, hypothesis testing, and determining relationships. Two main methods are estimation, which uses a sample to estimate a parameter and construct a confidence interval, and hypothesis testing, which involves assuming and testing a null hypothesis against collected data. Types of samples discussed are simple random samples, systematic samples, and stratified random samples.
This document provides an introduction to statistics. It defines statistics as the scientific methods for collecting, organizing, summarizing, presenting and analyzing data to derive valid conclusions. Statistics is useful across many fields and careers as it helps make informed decisions based on data. The document outlines descriptive and inferential statistics, and notes that descriptive statistics simplifies complexity while inferential statistics allows for conclusions to be drawn. It also discusses types of data sources, including primary data collected directly and secondary data that has already been collected.
This document provides an introduction to statistics. It discusses why statistics is important and required for many programs. Reasons include the prevalence of numerical data in daily life, the use of statistical techniques to make decisions that affect people, and the need to understand how data is used to make informed decisions. The document also defines key statistical concepts such as population, parameter, sample, statistic, descriptive statistics, inferential statistics, variables, and different types of variables.
The ppt gives an idea about basic concept of Estimation. point and interval. Properties of good estimate is also covered. Confidence interval for single means, difference between two means, proportion and difference of two proportion for different sample sizes are included along with case studies.
This document defines data and different types of data presentation. It discusses quantitative and qualitative data, and different scales for qualitative data. The document also covers different ways to present data scientifically, including through tables, graphs, charts and diagrams. Key types of visual presentation covered are bar charts, histograms, pie charts and line diagrams. Presentation should aim to clearly convey information in a concise and systematic manner.
This document provides an introduction to business statistics for a 4th semester BBA course. It defines statistics as the collection, analysis, and interpretation of numerical data. Descriptive statistics are used to summarize data through measures of central tendency, dispersion, graphs and tables. Inferential statistics allow generalization from samples to populations through estimation of parameters and hypothesis testing. The key terms of population, sample, parameter, and statistic are defined. Variables are characteristics that can take on different values and are classified as qualitative or quantitative. Quantitative variables are further divided into discrete and continuous types. Descriptive statistics simply describe data while inferential statistics make inferences about unknown population characteristics based on samples.
1. The document discusses descriptive statistics, which is the study of how to collect, organize, analyze, and interpret numerical data.
2. Descriptive statistics can be used to describe data through measures of central tendency like the mean, median, and mode as well as measures of variability like the range.
3. These statistical techniques help summarize and communicate patterns in data in a concise manner.
This document provides an introduction to business statistics. It defines statistics as the science of collecting, organizing, analyzing, and interpreting numerical data. The document notes that statistics can refer to both quantitative information and the methods used to analyze that information. It describes the key stages of a statistical analysis: data collection, organization, presentation, analysis, and interpretation. The document also discusses whether statistics is a science or an art and the important functions of statistics like providing definiteness, enabling comparison, and aiding in prediction.
Statistics can be defined in both a singular and plural sense. In the singular sense, it refers to statistical methods for collecting, analyzing, and interpreting numerical data. In the plural sense, it refers to the actual numerical facts or data collected. Statistics involves systematically collecting, organizing, presenting, analyzing, and interpreting numerical data to describe features and characteristics. It allows for comparing facts, establishing relationships, and facilitating policymaking and decision making. However, statistics only studies aggregates and averages, not individual cases, and results are true only on average. It also requires properly contextualizing and referencing results.
This document discusses measures of central tendency including mean, median, and mode. It provides definitions and formulas for calculating each measure from both grouped and ungrouped data. For mean, it is the sum of all values divided by the number of values. Median is the middle value when values are arranged in order. Mode is the most frequently occurring value. The document also discusses requirements for a good measure of central tendency and provides examples calculating the measures from sample medical data sets to illustrate their use and assess skewness.
1) The document introduces basic concepts of probability such as sample spaces, events, outcomes, and how to calculate classical and empirical probabilities.
2) It discusses approaches to determining probability including classical, empirical, and subjective probabilities. Simulations can also be used to estimate probabilities.
3) Examples are provided to illustrate calculating probabilities using classical and empirical approaches for single and compound events with different sample spaces.
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
According to Wikipedia point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population means).
This document discusses descriptive statistics and analysis. It provides definitions of key terms like data, variable, statistic, and parameter. It also describes common measures of central tendency like mean, median and mode. Additionally, it covers measures of variability such as range, variance and standard deviation. Various graphical and numerical methods for summarizing and presenting sample data are presented, including tables, charts and distributions.
This document provides an introduction and overview of key concepts in statistics. It defines important statistical terms like population, sample, parameter, statistic, variable and constant. It distinguishes between discrete and continuous variables and quantitative and qualitative data. It describes the different meanings and definitions of statistics and outlines key characteristics of statistical data. It also discusses topics like collection of data, accuracy, significant figures and mathematical notation used in statistics.
The document discusses different measures of central tendency:
- The mode is the most frequent score in a data set and is used for nominal data.
- The median is the middle score when data is arranged from lowest to highest and is not influenced by outliers.
- The mean is the average of all scores, calculated by summing all scores and dividing by the total number, and is preferred for interval/ratio scaled and non-skewed data.
Basic statistics is the science of collecting, organizing, summarizing, and interpreting data. It allows researchers to gain insights from data through graphical or numerical summaries, regardless of the amount of data. Descriptive statistics can be used to describe single variables through frequencies, percentages, means, and standard deviations. Inferential statistics make inferences about phenomena through hypothesis testing, correlations, and predicting relationships between variables.
HERE I'VE GIVE THE DESCRIPTION ABOUT OUTLIERS AND IT'S TYPES WITH SOME EXAMPLE.
My Facebook handle _ https://www.facebook.com/profile.php?id=100066529451772
hello, friends. i'm humaira jahan. and this is my presentation on statistics. you can find a overall concept of statistics in this presentation. and i hope it will help you enough to know about statistics.
Statistics is the methodology used to interpret and draw conclusions from collected data. It provides methods for designing research studies, summarizing and exploring data, and making predictions about phenomena represented by the data. A population is the set of all individuals of interest, while a sample is a subset of individuals from the population used for measurements. Parameters describe characteristics of the entire population, while statistics describe characteristics of a sample and can be used to infer parameters. Basic descriptive statistics used to summarize samples include the mean, standard deviation, and variance, which measure central tendency, spread, and how far data points are from the mean, respectively. The goal of statistical data analysis is to gain understanding from data through defined steps.
This document discusses inferential statistics and different types of samples that can be drawn from a population. Inferential statistics involves making inferences about a population based on a sample. It consists of generalizing from samples to populations, estimating parameters, hypothesis testing, and determining relationships. Two main methods are estimation, which uses a sample to estimate a parameter and construct a confidence interval, and hypothesis testing, which involves assuming and testing a null hypothesis against collected data. Types of samples discussed are simple random samples, systematic samples, and stratified random samples.
This document provides an introduction to statistics. It defines statistics as the scientific methods for collecting, organizing, summarizing, presenting and analyzing data to derive valid conclusions. Statistics is useful across many fields and careers as it helps make informed decisions based on data. The document outlines descriptive and inferential statistics, and notes that descriptive statistics simplifies complexity while inferential statistics allows for conclusions to be drawn. It also discusses types of data sources, including primary data collected directly and secondary data that has already been collected.
This document provides an introduction to statistics. It discusses why statistics is important and required for many programs. Reasons include the prevalence of numerical data in daily life, the use of statistical techniques to make decisions that affect people, and the need to understand how data is used to make informed decisions. The document also defines key statistical concepts such as population, parameter, sample, statistic, descriptive statistics, inferential statistics, variables, and different types of variables.
The ppt gives an idea about basic concept of Estimation. point and interval. Properties of good estimate is also covered. Confidence interval for single means, difference between two means, proportion and difference of two proportion for different sample sizes are included along with case studies.
This document defines data and different types of data presentation. It discusses quantitative and qualitative data, and different scales for qualitative data. The document also covers different ways to present data scientifically, including through tables, graphs, charts and diagrams. Key types of visual presentation covered are bar charts, histograms, pie charts and line diagrams. Presentation should aim to clearly convey information in a concise and systematic manner.
This document provides an introduction to business statistics for a 4th semester BBA course. It defines statistics as the collection, analysis, and interpretation of numerical data. Descriptive statistics are used to summarize data through measures of central tendency, dispersion, graphs and tables. Inferential statistics allow generalization from samples to populations through estimation of parameters and hypothesis testing. The key terms of population, sample, parameter, and statistic are defined. Variables are characteristics that can take on different values and are classified as qualitative or quantitative. Quantitative variables are further divided into discrete and continuous types. Descriptive statistics simply describe data while inferential statistics make inferences about unknown population characteristics based on samples.
1. The document discusses descriptive statistics, which is the study of how to collect, organize, analyze, and interpret numerical data.
2. Descriptive statistics can be used to describe data through measures of central tendency like the mean, median, and mode as well as measures of variability like the range.
3. These statistical techniques help summarize and communicate patterns in data in a concise manner.
This document provides an introduction to business statistics. It defines statistics as the science of collecting, organizing, analyzing, and interpreting numerical data. The document notes that statistics can refer to both quantitative information and the methods used to analyze that information. It describes the key stages of a statistical analysis: data collection, organization, presentation, analysis, and interpretation. The document also discusses whether statistics is a science or an art and the important functions of statistics like providing definiteness, enabling comparison, and aiding in prediction.
Statistics can be defined in both a singular and plural sense. In the singular sense, it refers to statistical methods for collecting, analyzing, and interpreting numerical data. In the plural sense, it refers to the actual numerical facts or data collected. Statistics involves systematically collecting, organizing, presenting, analyzing, and interpreting numerical data to describe features and characteristics. It allows for comparing facts, establishing relationships, and facilitating policymaking and decision making. However, statistics only studies aggregates and averages, not individual cases, and results are true only on average. It also requires properly contextualizing and referencing results.
This document discusses measures of central tendency including mean, median, and mode. It provides definitions and formulas for calculating each measure from both grouped and ungrouped data. For mean, it is the sum of all values divided by the number of values. Median is the middle value when values are arranged in order. Mode is the most frequently occurring value. The document also discusses requirements for a good measure of central tendency and provides examples calculating the measures from sample medical data sets to illustrate their use and assess skewness.
1) The document introduces basic concepts of probability such as sample spaces, events, outcomes, and how to calculate classical and empirical probabilities.
2) It discusses approaches to determining probability including classical, empirical, and subjective probabilities. Simulations can also be used to estimate probabilities.
3) Examples are provided to illustrate calculating probabilities using classical and empirical approaches for single and compound events with different sample spaces.
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
According to Wikipedia point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population means).
This document discusses descriptive statistics and analysis. It provides definitions of key terms like data, variable, statistic, and parameter. It also describes common measures of central tendency like mean, median and mode. Additionally, it covers measures of variability such as range, variance and standard deviation. Various graphical and numerical methods for summarizing and presenting sample data are presented, including tables, charts and distributions.
This document provides an introduction and overview of key concepts in statistics. It defines important statistical terms like population, sample, parameter, statistic, variable and constant. It distinguishes between discrete and continuous variables and quantitative and qualitative data. It describes the different meanings and definitions of statistics and outlines key characteristics of statistical data. It also discusses topics like collection of data, accuracy, significant figures and mathematical notation used in statistics.
A power point presentation on statisticsKriace Ward
Statistics originated from Latin, Italian, and German words referring to organized states. Gottfried Achenwall is considered the "father of statistics" for coining the term to describe a specialized branch of knowledge. Modern statistics is defined as the science of judging collective phenomena through analysis and enumeration. While statistics can be an art and a science, its successful application depends on the skill of the statistician and their knowledge of the field being studied. Statistics are important across many domains from business, economics, and planning to the sciences. However, statistics also have limitations such as only studying aggregates, not individuals, and results being valid only on average and in the long run.
Presentation is made by the student of M.phil Jameel Ahmed Qureshi Faculty of Education Elsa Kazi campus Hyderabad UoS Jamshoron, This presentation is an assignment assign by the Dr. Mumtaz Khwaja
- Descriptive statistics are used to describe and summarize key characteristics of a data set.
- They include measures such as counts, means, ranges, and standard deviations.
- Descriptive statistics provide simple summaries about the sample and the measures, but do not make any claims about the population.
- The document provides examples of how descriptive statistics could be used to summarize caseload data from public defender offices.
1. The document discusses the meaning, uses, functions, importance and limitations of statistics. It defines statistics as the collection, presentation, analysis and interpretation of numerical data.
2. Statistics has various uses across different fields such as policy planning, management, education, commerce and accounts. It helps present facts precisely and enables comparison, correlation, formulation and testing of hypotheses, and forecasting.
3. While statistics is important for planning, administration, economics and more, it also has limitations such as only studying aggregates, numerical data, and being an average. Statistics can also be misused if not used carefully by experts.
CHAPTER 1.pdf Probability and Statistics for Engineersbraveset14
Mainly concerned with the methods and techniques used in the collection,
organization, presentation, and analysis of a set of data without making any
conclusions or inferences.
Gathering data
Editing and classifying
Presenting data
Drawing diagrams and graphs
Calculating averages and measures of dispersions.
Remark: Descriptive statistics doesn‟t go beyond describing the data
themselves.
CHAPTER 1.pdfProbability and Statistics for Engineersbraveset14
Plural form
Numerical facts and figures collected for certain purposes
Aggregates of numerical expressed facts (figures) collected in a systematic
manner for a predetermined purpose
Singular form
Systematic collection and interpretation of numerical data to make a decision
The science of collecting, organizing, presenting, analyzing, and interpreting
numerical data to make decisions on the basis of such analysis
This document provides an introduction and overview of biostatistics. It defines key biostatistics terms like population, sample, parameter, statistic, quantitative vs. qualitative data, levels of measurement, descriptive vs. inferential biostatistics, and common statistical notations. It also discusses sources of health information and how computerized health management information systems are used to collect, analyze and report data.
Types of Statistics Descriptive and Inferential StatisticsDr. Amjad Ali Arain
Topic: Types of Statistics Descriptive and Inferential Statistics
Student Name: Bushra
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Statistics are used by organizations to measure and analyze business performance. American Express uses statistics such as total returns to shareholders, numbers of cardholders by age group, and cardholder spending by age to analyze business units, identify targeted customer groups, and inform marketing campaigns. Statistics on labor force characteristics by gender help conclude that male monthly incomes are typically higher than females, though this does not necessarily mean males spend more.
The document provides an overview of data analysis concepts and methods for qualitative and quantitative data. It discusses topics such as descriptive statistics, measures of central tendency and spread. It also covers inferential statistics concepts like ANOVA, ANCOVA, regression, and correlation. Both the advantages and disadvantages of qualitative data analysis are presented. The document is a presentation on research methodology focusing on data analysis.
Statistics is a basic and important tool for professionals in all fields all over the worlds. This document provides the importance and scope of Statistics in major fields of study like a business, management, planning etc.
a) Experimental
b) Observational
The study in a) manipulates one variable (giving one group a herb vs placebo) and observes the effect on another variable (respiratory tract infections), making it an experimental study.
The study in b) passively observes behaviors or events without manipulation, making it an observational study.
Notes of BBA /B.Com as well as BCA. It will help average students to learn Business Statistics. It will help MBA and PGDM students in Quantitative Analysis.
This document discusses using statistics to analyze bottlenecks in a manufacturing process. It begins by introducing the purpose of using statistics to understand relationships between variables like population and country size. Later sections discuss using hypothesis testing to analyze sample data and determine if it represents the overall population parameters. Descriptive statistics are calculated on the sample data and a regression analysis is run to determine the relationship between population and sample size.
The meeting date of C.T.E. is 19/12/2013. Dr. S.P. Shah will chair the meeting. Issues related to syllabus, examinations and new courses will be discussed. The meeting in Gandhinagar chaired by Dr. Shah discussed various academic matters.
On 17/12/2013, a meeting was held in Moti Bagh to discuss the formation of new committees of L.N.K.C.E. Dr. A.P. Shah could not attend due to illness. Issues related to syllabus and examinations were discussed. It was decided that regular meetings will be held and status reports presented.
L.N.K.C.E.
The meeting date of CTE is 19/12/2013. Dr. S.P. Shah will chair the meeting. Important matters like new syllabus, results, and budget will be discussed. On 17/12/2013, a conference was held in Gandhinagar where Dr. S.P. Shah represented CTE. Many important CTE officials could not attend due to prior commitments. The officials discussed following up on pending matters. They also discussed streamlining the functioning of CTE and improving standards.
The EOTC conference of CTE college was held from 9/12/2013 to 14/12/2013. Students gained knowledge on subjects like environment, health, rural development through practical and theoretical learning
This slide is an exercise for the inquisitive students preparing for the competitive examinations of the undergraduate and postgraduate students. An attempt is being made to present the slide keeping in mind the New Education Policy (NEP). An attempt has been made to give the references of the facts at the end of the slide. If new facts are discovered in the near future, this slide will be revised.
This presentation is related to the brief History of Kashmir (Part-I) with special reference to Karkota Dynasty. In the seventh century a person named Durlabhvardhan founded the Karkot dynasty in Kashmir. He was a functionary of Baladitya, the last king of the Gonanda dynasty. This dynasty ruled Kashmir before the Karkot dynasty. He was a powerful king. Huansang tells us that in his time Taxila, Singhpur, Ursha, Punch and Rajputana were parts of the Kashmir state.
What makes space feel generous, and how architecture address this generosity in terms of atmosphere, metrics, and the implications of its scale? This edition of #Untagged explores these and other questions in its presentation of the 2024 edition of the Master in Collective Housing. The Master of Architecture in Collective Housing, MCH, is a postgraduate full-time international professional program of advanced architecture design in collective housing presented by Universidad Politécnica of Madrid (UPM) and Swiss Federal Institute of Technology (ETH).
Yearbook MCH 2024. Master in Advanced Studies in Collective Housing UPM - ETH
How to Add Customer Note in Odoo 18 POS - Odoo SlidesCeline George
In this slide, we’ll discuss on how to add customer note in Odoo 18 POS module. Customer Notes in Odoo 18 POS allow you to add specific instructions or information related to individual order lines or the entire order.
How to Manage Upselling in Odoo 18 SalesCeline George
In this slide, we’ll discuss on how to manage upselling in Odoo 18 Sales module. Upselling in Odoo is a powerful sales technique that allows you to increase the average order value by suggesting additional or more premium products or services to your customers.
All About the 990 Unlocking Its Mysteries and Its Power.pdfTechSoup
In this webinar, nonprofit CPA Gregg S. Bossen shares some of the mysteries of the 990, IRS requirements — which form to file (990N, 990EZ, 990PF, or 990), and what it says about your organization, and how to leverage it to make your organization shine.
Lecture 1 Introduction history and institutes of entomology_1.pptxArshad Shaikh
*Entomology* is the scientific study of insects, including their behavior, ecology, evolution, classification, and management.
Entomology continues to evolve, incorporating new technologies and approaches to understand and manage insect populations.
How to Create A Todo List In Todo of Odoo 18Celine George
In this slide, we’ll discuss on how to create a Todo List In Todo of Odoo 18. Odoo 18’s Todo module provides a simple yet powerful way to create and manage your to-do lists, ensuring that no task is overlooked.
Learn about the APGAR SCORE , a simple yet effective method to evaluate a newborn's physical condition immediately after birth ....this presentation covers .....
what is apgar score ?
Components of apgar score.
Scoring system
Indications of apgar score........
Redesigning Education as a Cognitive Ecosystem: Practical Insights into Emerg...Leonel Morgado
Slides used at the Invited Talk at the Harvard - Education University of Hong Kong - Stanford Joint Symposium, "Emerging Technologies and Future Talents", 2025-05-10, Hong Kong, China.
In this concise presentation, Dr. G.S. Virdi (Former Chief Scientist, CSIR-CEERI, Pilani) introduces the Junction Field-Effect Transistor (JFET)—a cornerstone of modern analog electronics. You’ll discover:
Why JFETs? Learn how their high input impedance and low noise solve the drawbacks of bipolar transistors.
JFET vs. MOSFET: Understand the core differences between JFET and MOSFET devices.
Internal Structure: See how source, drain, gate, and the depletion region form a controllable semiconductor channel.
Real-World Applications: Explore where JFETs power amplifiers, sensors, and precision circuits.
Perfect for electronics students, hobbyists, and practicing engineers looking for a clear, practical guide to JFET technology.
The insect cuticle is a tough, external exoskeleton composed of chitin and proteins, providing protection and support. However, as insects grow, they need to shed this cuticle periodically through a process called moulting. During moulting, a new cuticle is prepared underneath, and the old one is shed, allowing the insect to grow, repair damaged cuticle, and change form. This process is crucial for insect development and growth, enabling them to transition from one stage to another, such as from larva to pupa or adult.
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Lecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptxArshad Shaikh
*Phylum Arthropoda* includes animals with jointed appendages, segmented bodies, and exoskeletons. It's divided into subphyla like Chelicerata (spiders), Crustacea (crabs), Hexapoda (insects), and Myriapoda (millipedes, centipedes). This phylum is one of the most diverse groups of animals.
2. 2
⊸ The word statistics conveys a variety of meaning to people in
different walks of life.
⊸ The word statistics comes from the Italian words
Statista = "statesman"
Or
⊸ Latin Word ‘Status’ = State
Or
⊸ The German word Statistik = "Collection of data involving the
State"
INTRODUCTION
3. ⊸ The word Statistics today refers to :
⊸ either quantitative information or a method of dealing
with quantitative or qualitative information.
⊸ Statistical Science or Statistics
3
5. 5
Three different context of Statistics
Numerical
Data
Numerical
Facts
Collection of
Data and
Facts
Use of
Theory,
Methods, and
Procedures
in analyzing
interpreting
collected
data
6. 6
⊸ DEFINITION
⊸ “Statistics is defined as collection, Presentation,
analysis and interpretation of numerical data”.
Croxton & cowden
⊸ Statistics is a science of probability and Inference
Boudington
7. 7
⊸ Statistics is a Science of
averages
⊸ Statistics is a science of
measurement in Social
Sciences
⊸ Statistics is the sciences and
art of dealing with figure and
facts.
9. Approaches
⊸ Descriptive Statistics –
It deals with the presentation of
numerical facts, or data, in either
tables or graphs form.
It give techniques to get general
picture of sample.
Example :- Mean , Median , Mode,
Standard Deviation
etc…
9
10. Approaches
⊸ Inferential Statistics –
It involves techniques for making
inferences about the whole
population on the basis of
observations obtained from samples.
Examples :- t test, F test etc…
10
11. USE & APPLICATION OF STATISTICS
⊸ It shows how sample differs from normal
distribution
⊸ It facilitates comparisons
⊸ It simplifies the message of figure
⊸ It helps in formulating and testing
hypothesis
⊸ It help in prediction
⊸ It helps in inference
12. Limitations and Statistics
⊸ To ascribe unrealistic data by
misuse of an average
⊸ To compare unrelated and
irrelevant data in order to mislead
people
12
13. Students marks out of 100
( passing marks 35) :-
Average :- 42
13
Roll No Marks
1 30
2 25
3 25
4 30
5 30
6 25
7 35
8 25
9 96
10 99
Actually 7 Students failed but for advertisement only
average will be presented
15. Limitations and Statistics
⊸ To commit an error of generalizing
the conclusion on the basis of small
samples
⊸ In the above mentioned example
the result of only brilliant class is
displayed for advertisement of the
school
15
16. Limitations and Statistics
⊸ To predict changes in two variables
without taking cognizance of causal
relationship between them
⊸ To mislead people by making
unnecessary and clumsy
presentation of statistical data
16