This study examined the association between pesticide exposure and lympho-hematopoietic cancers (LHC) in central Greece. Data was collected through questionnaires administered to 354 LHC cases and 455 controls from two regional hospitals. Logistic regression found an independent association between pesticide exposure and LHC (OR 1.41, 95% CI 1.00-2.00), after controlling for potential confounders like age, sex, smoking, and family history of cancer or immunological disorders. The study also found associations between LHC and smoking during pesticide application, eating during application, handling pesticide-treated seeds, and lack of proper ventilation during use, but no association with type of pesticide used.
This document contains an assignment on foundational concepts in quantitative analysis for a course. It includes 6 questions analyzing statistics concepts like descriptive vs inferential, levels of measurement, sample types, and observational vs experimental studies. It also includes 2 case studies on the relationship between class attendance and grades, and transportation industry safety, with analysis questions for each. The assignment is due on September 21st, 2015 and should be submitted during class.
This document provides an overview of key concepts in statistics and experimental design. It defines statistics as the study of how to collect, organize, analyze, and interpret numerical data. Descriptive statistics involves organizing and summarizing data, while inferential statistics uses sample data to draw conclusions about populations. Experimental design involves identifying individuals and variables of interest, determining whether to use a population or sample, collecting data, and analyzing results. Random sampling and experiments are introduced as methods for producing unbiased data. Key considerations for survey design like question wording and sample representation are also discussed.
This document defines key concepts in statistics including descriptive statistics, inferential statistics, populations, samples, variables, and levels of measurement. It discusses random sampling and different sampling techniques. The key points are:
- Statistics involves collecting, organizing, analyzing, and interpreting numerical data.
- Descriptive statistics describes data while inferential statistics uses samples to draw conclusions about populations.
- Variables can be quantitative (having numerical values) or qualitative (categorical).
- Population data includes all individuals while sample data includes only a subset. Random samples are needed to draw inferences about populations.
- Levels of measurement include nominal, ordinal, interval, and ratio scales.
- Random sampling and different sampling techniques like stratified and cluster
Chapter 1 introduction to statistics for engineers 1 (1)abfisho
This document provides an introduction to statistics. It defines statistics as the science of collecting, analyzing, and presenting data systematically. Statistics has two main branches - descriptive statistics, which describes data through measures like averages without generalizing beyond the sample, and inferential statistics, which makes generalizations from samples to populations. The document lists important terms in statistics like data, variables, population, sample, and sample size. It also outlines the main steps in a statistical investigation, including collecting and organizing data. Statistics has many applications in fields like business, engineering, health, and economics.
Sampling-Concepts (Statistics and Probability).pptxmarigold32
This document discusses concepts in statistics and probability related to sampling, populations, parameters, and statistics. It defines key terms like population, sample, parameter, and statistic. It explains different types of sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. It provides examples and illustrations of these sampling methods. The document also discusses the differences between probability and non-probability sampling. Finally, it includes sample problems and assignments related to identifying parameters and statistics, classifying sampling methods, and applying stratified random sampling.
This document discusses the introduction and scope of statistics. It begins by defining statistics as relating to numerical facts and data. It notes that Florence Nightingale was the first nurse statistician, using statistical evidence to improve healthcare. Statistics is then defined as a branch of mathematics dealing with collecting, organizing, analyzing, and presenting numerical data to correctly interpret information. The scope of statistics in nursing is described for areas like anatomy, physiology, pharmacology, and public health. Finally, the scope of statistics is discussed in other fields such as social sciences, planning, mathematics, economics, and business management.
Statistics can be misused in several ways:
1) By using statistics to sell products or get attention through evoking fear or shock with selective or biased statistics.
2) By detaching statistics from their proper context or comparisons to make claims that are misleading.
3) By suggesting causal relationships through statistics that do not account for all variables or factors. Care must be taken to properly collect and interpret statistical data to avoid misleading conclusions.
This study examined the association between pesticide exposure and lympho-hematopoietic cancers (LHC) in central Greece. Data was collected through questionnaires administered to 354 LHC cases and 455 controls from two regional hospitals. Logistic regression found an independent association between pesticide exposure and LHC (OR 1.41, 95% CI 1.00-2.00), after controlling for potential confounders like age, sex, smoking, and family history of cancer or immunological disorders. The study also found associations between LHC and smoking during pesticide application, eating during application, handling pesticide-treated seeds, and lack of proper ventilation during use, but no association with type of pesticide used.
This document contains an assignment on foundational concepts in quantitative analysis for a course. It includes 6 questions analyzing statistics concepts like descriptive vs inferential, levels of measurement, sample types, and observational vs experimental studies. It also includes 2 case studies on the relationship between class attendance and grades, and transportation industry safety, with analysis questions for each. The assignment is due on September 21st, 2015 and should be submitted during class.
This document provides an overview of key concepts in statistics and experimental design. It defines statistics as the study of how to collect, organize, analyze, and interpret numerical data. Descriptive statistics involves organizing and summarizing data, while inferential statistics uses sample data to draw conclusions about populations. Experimental design involves identifying individuals and variables of interest, determining whether to use a population or sample, collecting data, and analyzing results. Random sampling and experiments are introduced as methods for producing unbiased data. Key considerations for survey design like question wording and sample representation are also discussed.
This document defines key concepts in statistics including descriptive statistics, inferential statistics, populations, samples, variables, and levels of measurement. It discusses random sampling and different sampling techniques. The key points are:
- Statistics involves collecting, organizing, analyzing, and interpreting numerical data.
- Descriptive statistics describes data while inferential statistics uses samples to draw conclusions about populations.
- Variables can be quantitative (having numerical values) or qualitative (categorical).
- Population data includes all individuals while sample data includes only a subset. Random samples are needed to draw inferences about populations.
- Levels of measurement include nominal, ordinal, interval, and ratio scales.
- Random sampling and different sampling techniques like stratified and cluster
Chapter 1 introduction to statistics for engineers 1 (1)abfisho
This document provides an introduction to statistics. It defines statistics as the science of collecting, analyzing, and presenting data systematically. Statistics has two main branches - descriptive statistics, which describes data through measures like averages without generalizing beyond the sample, and inferential statistics, which makes generalizations from samples to populations. The document lists important terms in statistics like data, variables, population, sample, and sample size. It also outlines the main steps in a statistical investigation, including collecting and organizing data. Statistics has many applications in fields like business, engineering, health, and economics.
Sampling-Concepts (Statistics and Probability).pptxmarigold32
This document discusses concepts in statistics and probability related to sampling, populations, parameters, and statistics. It defines key terms like population, sample, parameter, and statistic. It explains different types of sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. It provides examples and illustrations of these sampling methods. The document also discusses the differences between probability and non-probability sampling. Finally, it includes sample problems and assignments related to identifying parameters and statistics, classifying sampling methods, and applying stratified random sampling.
This document discusses the introduction and scope of statistics. It begins by defining statistics as relating to numerical facts and data. It notes that Florence Nightingale was the first nurse statistician, using statistical evidence to improve healthcare. Statistics is then defined as a branch of mathematics dealing with collecting, organizing, analyzing, and presenting numerical data to correctly interpret information. The scope of statistics in nursing is described for areas like anatomy, physiology, pharmacology, and public health. Finally, the scope of statistics is discussed in other fields such as social sciences, planning, mathematics, economics, and business management.
Statistics can be misused in several ways:
1) By using statistics to sell products or get attention through evoking fear or shock with selective or biased statistics.
2) By detaching statistics from their proper context or comparisons to make claims that are misleading.
3) By suggesting causal relationships through statistics that do not account for all variables or factors. Care must be taken to properly collect and interpret statistical data to avoid misleading conclusions.
This document provides an introduction to statistics and discusses key concepts including:
- Statistics involves collecting, presenting, analyzing, and interpreting data.
- There are two main branches of statistics: descriptive statistics and inferential statistics.
- Descriptive statistics involves organizing and summarizing data using tables, charts, and averages. Inferential statistics uses sample data to draw conclusions about populations.
- Population refers to the entire group being studied, while a sample is a subset of the population. Parameters describe populations and statistics describe samples.
- Data can come from primary sources collected for a specific study or secondary sources previously collected. Common methods for primary data collection include questionnaires, surveys, and investigations through enumerators.
The document provides information about statistics and related concepts:
1. It defines statistics and discusses its importance in various fields like agriculture, economics, and administration.
2. It outlines the characteristics of a satisfactory average and describes various measures of central tendency including arithmetic mean, median, and mode.
3. It discusses the steps involved in constructing a frequency distribution table from raw data for both grouped and ungrouped data.
This document provides an introduction to statistics. It defines statistics and discusses its importance, limitations, and application areas. It also outlines the main classifications of statistics including descriptive and inferential statistics. Descriptive statistics describes data without making conclusions while inferential statistics makes generalizations beyond the data. The document concludes by defining key statistical terms and outlining the typical steps in a statistical investigation.
This document discusses different types of sampling methods used in research. It begins by defining key terms like target population, sample, and sampling frame. It then covers different sampling techniques including probability sampling methods like simple random sampling, stratified random sampling, and cluster sampling as well as non-probability sampling methods. For each method, it provides examples and discusses their advantages and disadvantages for representing populations. The document aims to help medical students understand how to select appropriate sampling methods based on their research questions.
Statistics is widely used in agriculture for research, planning, and decision making. It involves collecting, organizing, analyzing, and interpreting numerical data to draw valid conclusions. In agricultural research, statistical techniques are used for experiments on crops, animals, and other areas. Researchers must have expertise in both statistics and their subject area to select the appropriate statistical procedure. Statistics helps farmers and organizations by informing decisions, evaluating policies, and underpinning agricultural planning processes.
The document discusses population and sampling methods used in research. It defines key terms like population, sampling, parameter, and statistic. It also describes different sampling procedures like simple random sampling, stratified random sampling, purposive sampling, and multi-stage sampling. The document emphasizes that the sampling method must reflect the unit of analysis in the study. It also explains that multi-stage sampling is commonly used in educational research where units are selected in multiple steps.
This document provides an overview of key concepts in statistics, including:
- Statistics involves collecting, organizing, analyzing, and interpreting numerical data.
- Descriptive statistics describes data while inferential statistics draws conclusions about populations from samples.
- Variables can be quantitative (having numerical values) or qualitative (categorical).
- Random sampling is important for drawing valid statistical inferences from a sample to a population.
- Experiments involve imposing treatments to observe changes in variables, while observational studies do not alter variables.
- Survey questions require careful design to avoid biases and obtain meaningful results.
The document discusses the difference between statistics and parameters. It states that a statistic describes a sample, while a parameter describes an entire population. It then provides examples of studies and identifies whether the numerical values provided are statistics or parameters. Statistics are values that describe characteristics of a sample only, while parameters describe the entire population.
The document provides information about normal distribution, Poisson distribution, and statistical tests. It defines:
- Normal distribution as a symmetric, bell-shaped curve where the mean, median, and mode are equal. Examples given include measures of human size and physiology.
- Poisson distribution as expressing the probability of events occurring at a constant rate over time or space. Examples include phone calls per hour. Conditions for its use are also outlined.
- Parametric tests assume normal distribution and make inferences about population parameters. Non-parametric tests don't assume normality. When individual variability is high, non-parametric tests are more appropriate.
Confidence intervals and the process for calculating them at a given confidence level are also
This document discusses techniques for collecting data through censuses and surveys. It defines censuses as counting all units in a population within an area, while surveys count only a sample of the population. It then covers topics like the differences between censuses and surveys, types of censuses and surveys conducted in Indonesia, steps in conducting a survey, parameters and statistics, sources of error in data collection, and ways to reduce errors.
This document discusses statistics and its applications in agriculture. It defines statistics as the collection, organization, analysis, and interpretation of numerical data to derive conclusions. Statistics has grown to be applied across many fields including agriculture, where different statistical techniques are used for crop, animal, and laboratory research. Choosing the correct statistical procedure depends on expertise in both statistics and the relevant subject matter. The document also provides examples of how statistics is used in agricultural research and development, including evaluating hypotheses about increasing crop yields.
Statistics is the collection, organization, analysis, interpretation and presentation of data. It deals with both descriptive statistics, which summarize and describe data, and inferential statistics, which are used to draw conclusions about populations based on sample data. The key aspects of statistics discussed in the document are:
- Populations and samples
- Parameters and statistics
- Quantitative and qualitative variables
- Levels of measurement including nominal, ordinal, interval and ratio scales
- Types of data including primary and secondary data
This document discusses the application of statistics in agriculture. It defines key statistical concepts like data, statistics, and information. It explains that statistics is used in agricultural research to design experiments and test hypotheses. Some examples of how statistics is used in agriculture include improving crop yields, monitoring pests and diseases, and evaluating policy impacts. The document also outlines the stages of a typical agricultural survey, from planning to dissemination. Maintaining coordination between different statistical agencies is important to improve efficiency and data quality.
This document provides an introduction to biostatistics. It defines biostatistics and explains its importance in biomedical research. Some key points covered include:
- Biostatistics is the application of statistics to medicine and health sciences. It involves the collection, organization, and analysis of numerical data.
- Understanding biostatistics is important for medical research, updating medical knowledge, and managing data and treatment.
- The document outlines the basic concepts of biostatistics like population and sample, and the different types of data. It also describes the typical steps involved in a research project and how biostatistics can be applied.
Statistics involves collecting, organizing, analyzing, and interpreting numerical data. Descriptive statistics describes data while inferential statistics draws conclusions from samples to populations. A study on coyotes concluded they were all dangerous based on ranchers' observations of those near ranches, but further study found most coyotes avoid sheep. Experiments aim to observe changes from treatments by comparing experimental and control groups, while observational studies make uncontrolled observations. Surveys can introduce biases from question wording, non-responses, and over-generalizing results.
1. The document discusses the introduction to statistics, providing definitions and explaining key concepts. It describes how statistics is used in various fields like education, business, medical research, and agriculture.
2. Statistics is defined as the science of collecting, organizing, summarizing, presenting, analyzing, and interpreting data. It can be used as both a science and an art. Statistics has various applications in fields like administration, business, education, and medical and agricultural research.
3. The document outlines the basic terminology used in statistics, including data, variables, observations, quantitative and qualitative data, continuous and discrete variables. It distinguishes between primary and secondary data and their characteristics.
This document discusses sampling design and different sampling techniques. It begins by defining key terms like population, sample, sampling, and sampling design. It then covers census surveys and explains the difference between a census and a sample survey. The document outlines the main steps in sample design including defining the type of universe, sampling unit, source list, sample size, and sampling procedure. It also describes different probability sampling techniques like simple random sampling and stratified sampling. Finally, it discusses characteristics of a good sample design and provides a formula for determining sample size.
The document provides information on survey design and quantitative data analysis methods. It discusses different sampling methods including probability sampling techniques like simple random sampling, systematic random sampling, and stratified random sampling. It also covers non-probability sampling and factors to consider when determining sample size. The document then outlines steps for designing a survey including the components of a survey method plan and instrumentation. It concludes with an overview of quantitative data analysis methods for surveys, specifically descriptive statistics like frequencies, measures of central tendency, and measures of dispersion.
- Record the findings on the proforma
- Assist the examiner as required
- Maintain the equipment and supplies
- Help in sterilization and disinfection
Examiner:
- Explain the procedure to the subject
- Conduct the examination
- Record the findings
- Refer cases requiring treatment
5. Analyzing the data:
- Data entry and cleaning
- Descriptive analysis - frequencies, percentages
- Inferential analysis - Chi square test, t test, ANOVA
- Graphs and tables
- Interpretation
6. Drawing conclusions:
- Compare findings with other studies
- Discuss limitations
- Suggest recommendations
- State implications for oral health policy
This document provides an introduction to statistics and discusses key concepts including:
- Statistics involves collecting, presenting, analyzing, and interpreting data.
- There are two main branches of statistics: descriptive statistics and inferential statistics.
- Descriptive statistics involves organizing and summarizing data using tables, charts, and averages. Inferential statistics uses sample data to draw conclusions about populations.
- Population refers to the entire group being studied, while a sample is a subset of the population. Parameters describe populations and statistics describe samples.
- Data can come from primary sources collected for a specific study or secondary sources previously collected. Common methods for primary data collection include questionnaires, surveys, and investigations through enumerators.
The document provides information about statistics and related concepts:
1. It defines statistics and discusses its importance in various fields like agriculture, economics, and administration.
2. It outlines the characteristics of a satisfactory average and describes various measures of central tendency including arithmetic mean, median, and mode.
3. It discusses the steps involved in constructing a frequency distribution table from raw data for both grouped and ungrouped data.
This document provides an introduction to statistics. It defines statistics and discusses its importance, limitations, and application areas. It also outlines the main classifications of statistics including descriptive and inferential statistics. Descriptive statistics describes data without making conclusions while inferential statistics makes generalizations beyond the data. The document concludes by defining key statistical terms and outlining the typical steps in a statistical investigation.
This document discusses different types of sampling methods used in research. It begins by defining key terms like target population, sample, and sampling frame. It then covers different sampling techniques including probability sampling methods like simple random sampling, stratified random sampling, and cluster sampling as well as non-probability sampling methods. For each method, it provides examples and discusses their advantages and disadvantages for representing populations. The document aims to help medical students understand how to select appropriate sampling methods based on their research questions.
Statistics is widely used in agriculture for research, planning, and decision making. It involves collecting, organizing, analyzing, and interpreting numerical data to draw valid conclusions. In agricultural research, statistical techniques are used for experiments on crops, animals, and other areas. Researchers must have expertise in both statistics and their subject area to select the appropriate statistical procedure. Statistics helps farmers and organizations by informing decisions, evaluating policies, and underpinning agricultural planning processes.
The document discusses population and sampling methods used in research. It defines key terms like population, sampling, parameter, and statistic. It also describes different sampling procedures like simple random sampling, stratified random sampling, purposive sampling, and multi-stage sampling. The document emphasizes that the sampling method must reflect the unit of analysis in the study. It also explains that multi-stage sampling is commonly used in educational research where units are selected in multiple steps.
This document provides an overview of key concepts in statistics, including:
- Statistics involves collecting, organizing, analyzing, and interpreting numerical data.
- Descriptive statistics describes data while inferential statistics draws conclusions about populations from samples.
- Variables can be quantitative (having numerical values) or qualitative (categorical).
- Random sampling is important for drawing valid statistical inferences from a sample to a population.
- Experiments involve imposing treatments to observe changes in variables, while observational studies do not alter variables.
- Survey questions require careful design to avoid biases and obtain meaningful results.
The document discusses the difference between statistics and parameters. It states that a statistic describes a sample, while a parameter describes an entire population. It then provides examples of studies and identifies whether the numerical values provided are statistics or parameters. Statistics are values that describe characteristics of a sample only, while parameters describe the entire population.
The document provides information about normal distribution, Poisson distribution, and statistical tests. It defines:
- Normal distribution as a symmetric, bell-shaped curve where the mean, median, and mode are equal. Examples given include measures of human size and physiology.
- Poisson distribution as expressing the probability of events occurring at a constant rate over time or space. Examples include phone calls per hour. Conditions for its use are also outlined.
- Parametric tests assume normal distribution and make inferences about population parameters. Non-parametric tests don't assume normality. When individual variability is high, non-parametric tests are more appropriate.
Confidence intervals and the process for calculating them at a given confidence level are also
This document discusses techniques for collecting data through censuses and surveys. It defines censuses as counting all units in a population within an area, while surveys count only a sample of the population. It then covers topics like the differences between censuses and surveys, types of censuses and surveys conducted in Indonesia, steps in conducting a survey, parameters and statistics, sources of error in data collection, and ways to reduce errors.
This document discusses statistics and its applications in agriculture. It defines statistics as the collection, organization, analysis, and interpretation of numerical data to derive conclusions. Statistics has grown to be applied across many fields including agriculture, where different statistical techniques are used for crop, animal, and laboratory research. Choosing the correct statistical procedure depends on expertise in both statistics and the relevant subject matter. The document also provides examples of how statistics is used in agricultural research and development, including evaluating hypotheses about increasing crop yields.
Statistics is the collection, organization, analysis, interpretation and presentation of data. It deals with both descriptive statistics, which summarize and describe data, and inferential statistics, which are used to draw conclusions about populations based on sample data. The key aspects of statistics discussed in the document are:
- Populations and samples
- Parameters and statistics
- Quantitative and qualitative variables
- Levels of measurement including nominal, ordinal, interval and ratio scales
- Types of data including primary and secondary data
This document discusses the application of statistics in agriculture. It defines key statistical concepts like data, statistics, and information. It explains that statistics is used in agricultural research to design experiments and test hypotheses. Some examples of how statistics is used in agriculture include improving crop yields, monitoring pests and diseases, and evaluating policy impacts. The document also outlines the stages of a typical agricultural survey, from planning to dissemination. Maintaining coordination between different statistical agencies is important to improve efficiency and data quality.
This document provides an introduction to biostatistics. It defines biostatistics and explains its importance in biomedical research. Some key points covered include:
- Biostatistics is the application of statistics to medicine and health sciences. It involves the collection, organization, and analysis of numerical data.
- Understanding biostatistics is important for medical research, updating medical knowledge, and managing data and treatment.
- The document outlines the basic concepts of biostatistics like population and sample, and the different types of data. It also describes the typical steps involved in a research project and how biostatistics can be applied.
Statistics involves collecting, organizing, analyzing, and interpreting numerical data. Descriptive statistics describes data while inferential statistics draws conclusions from samples to populations. A study on coyotes concluded they were all dangerous based on ranchers' observations of those near ranches, but further study found most coyotes avoid sheep. Experiments aim to observe changes from treatments by comparing experimental and control groups, while observational studies make uncontrolled observations. Surveys can introduce biases from question wording, non-responses, and over-generalizing results.
1. The document discusses the introduction to statistics, providing definitions and explaining key concepts. It describes how statistics is used in various fields like education, business, medical research, and agriculture.
2. Statistics is defined as the science of collecting, organizing, summarizing, presenting, analyzing, and interpreting data. It can be used as both a science and an art. Statistics has various applications in fields like administration, business, education, and medical and agricultural research.
3. The document outlines the basic terminology used in statistics, including data, variables, observations, quantitative and qualitative data, continuous and discrete variables. It distinguishes between primary and secondary data and their characteristics.
This document discusses sampling design and different sampling techniques. It begins by defining key terms like population, sample, sampling, and sampling design. It then covers census surveys and explains the difference between a census and a sample survey. The document outlines the main steps in sample design including defining the type of universe, sampling unit, source list, sample size, and sampling procedure. It also describes different probability sampling techniques like simple random sampling and stratified sampling. Finally, it discusses characteristics of a good sample design and provides a formula for determining sample size.
The document provides information on survey design and quantitative data analysis methods. It discusses different sampling methods including probability sampling techniques like simple random sampling, systematic random sampling, and stratified random sampling. It also covers non-probability sampling and factors to consider when determining sample size. The document then outlines steps for designing a survey including the components of a survey method plan and instrumentation. It concludes with an overview of quantitative data analysis methods for surveys, specifically descriptive statistics like frequencies, measures of central tendency, and measures of dispersion.
- Record the findings on the proforma
- Assist the examiner as required
- Maintain the equipment and supplies
- Help in sterilization and disinfection
Examiner:
- Explain the procedure to the subject
- Conduct the examination
- Record the findings
- Refer cases requiring treatment
5. Analyzing the data:
- Data entry and cleaning
- Descriptive analysis - frequencies, percentages
- Inferential analysis - Chi square test, t test, ANOVA
- Graphs and tables
- Interpretation
6. Drawing conclusions:
- Compare findings with other studies
- Discuss limitations
- Suggest recommendations
- State implications for oral health policy
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Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
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Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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2. •Definition of statistics
Statistics is the science of conducting
studies to collect, organize, summarize,
analyze, and draw conclusions from data.
How many
candies?
How many
lollipops?
How many
percent is
gummy
worm?
3. The five Basic Terms of
Statistics
1. Population
2. Sample
3. Parameter 4. Statistic
5. Variable
4. Ex. All Filipino citizens who are
currently registered to vote
1.Population – all the members of a group about
which you want to draw a conclusion.
5. 2. Sample – the part of the population selected for
analysis.
Ex: The registered voters selected to
participate in a recent survey concerning
their intention to vote in the next election.
6. 3. Parameter – a numerical measure that
describes a characteristic of a population.
Ex. The percentage of
all registered voters
who intend to vote in
the next election
7. 4. Statistic – a numerical measure that describes
a characteristic of a sample.
Ex. The percentage in a sample of
registered voters who intend to vote in the
next election.
8. Ex. Gender, the household income of the
citizens who voted in the last presidential
election.
5. Variable – a characteristic of an item or an
individual that will be analyzed using statistics.
11. 1.Descriptive statistics – consists of the
collection, organization, summarizations, and
presentation of data.
Ex: a. Nine out of ten on
the job fatalities are men
b. Expenditures for
the cable industry
were P5.66 billion in
1996
12. 1. Inferential statistics consists of generalizing from
samples to populations, performing estimations and
hypotheses tests, determining relationships among
variables, and making predictions.
Ex. a. By 2040 at least 3.5
billion people will run short
of water.
b. Allergy therapy
makes bees go away
13. 1. All patients treated at a particular hospital last year.
2. The entire daily output of a cereal factory’s
production line
3. The patients selected to fill out a particular-satisfaction
questionnaire.
4. 100 boxes of cereal selected from a factory’s production
line.
5. The percentage of all patients who are very satisfied with
the care they received.
6. The percentage in a sample of patients who are very
satisfied with the care they received.
7. The number of varieties of a brand of cereal.
A. Identify the basic terms in statistics which
represent in the following situations.
Situations:
(population)
(sample)
(parameter)
(statistic)
(variable)
(population)
(sample)
14. B. In each of the following, tell whether descriptive
or inferential statistics have been used.
1.The median household income for people aged
25-34 is P35,888.
2.Drinking decaffeinated coffee can raise
cholesterol levels by 7%.
3.The national average annual medicine
expenditure per person is P1052.
4.Expert say that mortgage rates may soon hit
bottom.
5.The average age of citizens who voted for the
winning candidates in the last presidential
election.
(descriptive)
(inferential)
(descriptive)
(inferential)
(descriptive)