Pertemuan 4 untuk Materi BAB: STATISTIKA
By Bu Dina Ari Ani L
Review
Penilaian Harian 3. STATISTIKA (Shared: Walas 8 & Google Classroom)
Matematika
Kelas 8
TA 2020/2021
#pjj
#sn
This document provides an introduction to statistics. It discusses key concepts including the role of statistics in research, the typical research process, variables, scales of measurement, and descriptive and inferential statistics. Specifically, it describes how statistics is used for collecting, analyzing and interpreting data to answer research questions. It also outlines the typical steps in research including developing questions and hypotheses, choosing measures, designing the study, analyzing data, and drawing conclusions.
Statistics for the Health Scientist: Basic Statistics IDrLukeKane
This document provides an introduction to statistics. It defines key statistical concepts like variables, data, and different types of variables. Descriptive statistics are used to summarize raw data through tables and charts. Different types of charts are described that are suitable for categorical or quantitative variables. The goals are to classify variables, choose appropriate charts and tables, and understand how to describe and communicate data.
Fundamentals Of Statistics-Definition of statistics,Descriptive and Inferential Statistics,Major Types of Descriptive Statistics,Statistical data analysis
This document defines key statistical concepts:
- Statistics is the subject that deals with collecting, analyzing, and interpreting data. It can also refer to the data itself or summary measures calculated from samples.
- A population includes all individuals or objects with common characteristics being studied. A sample is a subset of a population selected to represent it.
- A parameter is a characteristic of the entire population, such as the population mean, which remains fixed. A statistic is a characteristic calculated from a sample, such as the sample mean, which can vary between samples.
Statistics is the collection, organization, analysis, and presentation of data. It has become important for professionals, scientists, and citizens to make sense of large amounts of data. Statistics are used across many disciplines from science to business. There are two main types of statistical methods - descriptive statistics which summarize data through measures like the mean and median, and inferential statistics which make inferences about populations based on samples. Descriptive statistics describe data through measures of central tendency and variability, while inferential statistics allow inferences to be made from samples to populations through techniques like hypothesis testing.
1.1-1.2 Descriptive and Inferential Statisticsmlong24
Statistics involves collecting, organizing, and analyzing data to draw conclusions. Descriptive statistics summarize and describe data, while inferential statistics generalize from samples to populations through estimation, hypothesis testing, and prediction. The chapter introduces key concepts including variables, data, random variables, data sets, data values, populations, samples, descriptive statistics, probability, and inferential statistics. Students are assigned problems applying these statistical concepts.
The document outlines 7 key characteristics of statistics:
1. Statistics are aggregates of facts that can be studied in relation to each other.
2. Statistics are affected by multiple causes rather than single causes due to their focus on social sciences.
3. Statistics are numerically expressed to allow for quantification and comparison.
4. Statistics are enumerated or estimated according to reasonable accuracy standards that may vary depending on the inquiry.
5. Statistics are collected systematically to ensure accurate conclusions can be drawn from the data.
Statistics is the collection and analysis of data. There are two main branches: descriptive statistics, which organizes and summarizes data, and inferential statistics, which uses descriptive statistics to make predictions. Statistics starts with a question and uses data to provide information to help make decisions. It is widely used in business, health, education, research, social sciences, and natural resources.
This document provides an introduction to statistics. It discusses key concepts including the role of statistics in research, the typical research process, variables, scales of measurement, and descriptive and inferential statistics. Specifically, it describes how statistics is used for collecting, analyzing and interpreting data to answer research questions. It also outlines the typical steps in research including developing questions and hypotheses, choosing measures, designing the study, analyzing data, and drawing conclusions.
Statistics for the Health Scientist: Basic Statistics IDrLukeKane
This document provides an introduction to statistics. It defines key statistical concepts like variables, data, and different types of variables. Descriptive statistics are used to summarize raw data through tables and charts. Different types of charts are described that are suitable for categorical or quantitative variables. The goals are to classify variables, choose appropriate charts and tables, and understand how to describe and communicate data.
Fundamentals Of Statistics-Definition of statistics,Descriptive and Inferential Statistics,Major Types of Descriptive Statistics,Statistical data analysis
This document defines key statistical concepts:
- Statistics is the subject that deals with collecting, analyzing, and interpreting data. It can also refer to the data itself or summary measures calculated from samples.
- A population includes all individuals or objects with common characteristics being studied. A sample is a subset of a population selected to represent it.
- A parameter is a characteristic of the entire population, such as the population mean, which remains fixed. A statistic is a characteristic calculated from a sample, such as the sample mean, which can vary between samples.
Statistics is the collection, organization, analysis, and presentation of data. It has become important for professionals, scientists, and citizens to make sense of large amounts of data. Statistics are used across many disciplines from science to business. There are two main types of statistical methods - descriptive statistics which summarize data through measures like the mean and median, and inferential statistics which make inferences about populations based on samples. Descriptive statistics describe data through measures of central tendency and variability, while inferential statistics allow inferences to be made from samples to populations through techniques like hypothesis testing.
1.1-1.2 Descriptive and Inferential Statisticsmlong24
Statistics involves collecting, organizing, and analyzing data to draw conclusions. Descriptive statistics summarize and describe data, while inferential statistics generalize from samples to populations through estimation, hypothesis testing, and prediction. The chapter introduces key concepts including variables, data, random variables, data sets, data values, populations, samples, descriptive statistics, probability, and inferential statistics. Students are assigned problems applying these statistical concepts.
The document outlines 7 key characteristics of statistics:
1. Statistics are aggregates of facts that can be studied in relation to each other.
2. Statistics are affected by multiple causes rather than single causes due to their focus on social sciences.
3. Statistics are numerically expressed to allow for quantification and comparison.
4. Statistics are enumerated or estimated according to reasonable accuracy standards that may vary depending on the inquiry.
5. Statistics are collected systematically to ensure accurate conclusions can be drawn from the data.
Statistics is the collection and analysis of data. There are two main branches: descriptive statistics, which organizes and summarizes data, and inferential statistics, which uses descriptive statistics to make predictions. Statistics starts with a question and uses data to provide information to help make decisions. It is widely used in business, health, education, research, social sciences, and natural resources.
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
Welcome to the series on Statistics for managers. In the beginner level, we'll cover basics of statistics, probability fundamentals and descriptive statistics.
We may have this question in mind that how the data is analysed, what are the ways in which we can summarize the data to make certain conclusions. In this lesson, we’ll go through a basic overview of the subject, Statistics. We’ll learn what is statistics, what do we mean by data and what are the types of statistical analysis which are done to understand the data. The video lesson is available on youtube-https://youtu.be/MupmSzG9NxI
Statistics is concerned with designing experiments, summarizing data to aid understanding, drawing conclusions from data, and predicting future outcomes. There are two main types of statistics: descriptive statistics which consolidate large amounts of information into numbers, and inferential statistics which allow researchers to demonstrate the probability that sample results reflect the overall population. Proper sampling is important for reliable data - the sample should be large enough, randomly selected, and give everyone an equal chance of being included.
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
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This document provides an introduction to statistics and biostatistics in healthcare. It defines statistics and biostatistics, outlines the basic steps of statistical work, and describes different types of variables and methods for collecting data. The document also discusses different types of descriptive and inferential statistics, including measures of central tendency, dispersion, frequency, t-tests, ANOVA, regression, and different types of plots/graphs. It explains how statistics is used in healthcare for areas like disease burden assessment, intervention effectiveness, cost considerations, evaluation frameworks, health care utilization, resource allocation, needs assessment, quality improvement, and product development.
This document provides an introduction to biostatistics. It discusses how statistics are important for precision in science and medicine. Biostatistics involves applying statistical tools to biological data from fields like medicine. Some key applications of biostatistics include defining normal ranges, comparing treatment effectiveness, and identifying disease associations. The document also outlines common statistical terms, data sources and types, methods for presenting data, measures of central tendency and variability.
This document provides a lecture note on statistics for physical sciences and engineering. It begins with an introduction to statistics and its importance in various fields such as physical sciences, engineering, and research. It then discusses descriptive and inferential statistics. The document also covers topics such as data collection methods, presentation of data through tables and diagrams, and some basic statistical definitions. Examples are provided to illustrate how to construct frequency tables from raw data. In summary, the document presents an overview of key statistical concepts and methods relevant for physical sciences and engineering.
This document provides an introduction to statistics and statistical concepts. It covers topics such as course objectives, purposes of statistics, population and sampling, types of data and variables, levels of measurement, and nominal level of measurement. The key points are that statistics can describe, summarize, predict and identify relationships in data, and that there are different levels of variables from nominal to ratio scales.
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 summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
This document discusses the scope and uses of statistics across various fields such as planning, economics, business, industry, mathematics, science, psychology, education, war, banking, government, sociology, and more. It outlines functions of statistics like presenting facts, testing hypotheses, forecasting, policymaking, enlarging knowledge, measuring uncertainty, simplifying data, deriving valid inferences, and drawing rational conclusions. It also covers characteristics, advantages, and limitations of statistics.
Level of Measurement, Frequency Distribution,Stem & Leaf Qasim Raza
This document discusses multivariate data analysis and techniques. It begins by defining qualitative and quantitative data, and the different levels of measurement - nominal, ordinal, interval, and ratio. It then discusses frequency distributions, stem and leaf plots, and demonstrates their use in SPSS. Finally, it defines multivariate data analysis as involving two or more variables, and provides examples of multivariate techniques such as multiple regression, discriminant analysis, MANOVA, and their appropriate uses depending on the level of measurement of the variables.
This document discusses analyzing research data through descriptive and analytical statistics. Descriptive statistics summarize variables one by one through measures like frequency, percentage, mean, median and standard deviation depending on the variable level. Analytical statistics examine relationships between two or more variables. The document demonstrates analyzing a hypertension study dataset in SPSS, including checking normality distribution through histograms, Shapiro-Wilk test and Q-Q plots to determine appropriate tests. Frequency is used to describe categorical gender variable while numerical age is described through mean, standard deviation and histogram with normal curve fitting.
This document discusses concepts related to data, including collection, organization, presentation, and analysis of data. It defines key terms like qualitative vs quantitative data and primary vs secondary data. It explains methods of collecting primary data through surveys, sampling techniques, and secondary data from published and unpublished sources. The document also covers organizing data through frequency distributions, statistical series, and presenting data in tabular, diagrammatic and graphical forms like pie charts, histograms, bar diagrams and ogives. It concludes with analyzing organized data through measures of central tendency, dispersion, correlation and regression.
This document discusses descriptive statistics used to analyze quantitative and qualitative data from epidemiological studies. It defines key terms like prevalence, incidence, measures of central tendency (mean, median, mode), and measures of dispersion (range, standard deviation). It also covers describing categorical variables through proportions, rates, ratios and graphs like bar charts and pie charts. Coding systems are explained for different variable types. The goals of univariate descriptive analysis are also summarized.
This document discusses methods of collecting statistical data. It describes census and sample investigation methods. The census method collects data from every unit of the population, while the sample method collects data from only a few representative units. The census method is more reliable but costly, while the sample method is less expensive but less accurate. Key differences between the two methods are also outlined.
This document provides an overview of biostatistics. It defines biostatistics as the application of statistics to biology, medicine, and public health. It discusses different types of data, measures of central tendency including mean, median and mode, and graphical representations such as line graphs, bar diagrams and pie charts. The document emphasizes the important role of biostatistics in medical research and clinical decision making. It acknowledges deficiencies in biostatistical literacy among medical students and professionals.
1) Statistics is the study of collecting, organizing, analyzing, and drawing conclusions from data. It involves sampling, hypothesis testing, and using statistical tests tailored to measurement scales and hypothesis types.
2) Descriptive statistics describe and summarize data quantitatively, while inferential statistics allow generalizing from samples to populations through statistical testing and other methods.
3) The document discusses differences between statistics and statistical data, types of data, levels of measurement, sampling techniques, and uses of statistics.
This document provides an introduction to basic statistics concepts. It instructs students to collect data on the ages of classmates, organize it into a frequency table or graph, and answer questions about the distribution of ages. The document explains that statistics involves gathering, arranging, and presenting numeric data systematically, such as through tables, graphs or by sorting data in ascending or descending order. It defines statistics as the study of collecting, analyzing and interpreting data to address research questions.
This document provides an overview of key terminology and concepts in statistics. It discusses topics like populations and samples, variables and their measurement, levels of measurement, research methods like correlational analysis and experiments, and mathematical notation used in statistics. The goal is to introduce readers to what statistics is about at a high level and prepare them for further study of important statistical concepts.
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
Welcome to the series on Statistics for managers. In the beginner level, we'll cover basics of statistics, probability fundamentals and descriptive statistics.
We may have this question in mind that how the data is analysed, what are the ways in which we can summarize the data to make certain conclusions. In this lesson, we’ll go through a basic overview of the subject, Statistics. We’ll learn what is statistics, what do we mean by data and what are the types of statistical analysis which are done to understand the data. The video lesson is available on youtube-https://youtu.be/MupmSzG9NxI
Statistics is concerned with designing experiments, summarizing data to aid understanding, drawing conclusions from data, and predicting future outcomes. There are two main types of statistics: descriptive statistics which consolidate large amounts of information into numbers, and inferential statistics which allow researchers to demonstrate the probability that sample results reflect the overall population. Proper sampling is important for reliable data - the sample should be large enough, randomly selected, and give everyone an equal chance of being included.
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
http://bit.ly/LETndNET
This document provides an introduction to statistics and biostatistics in healthcare. It defines statistics and biostatistics, outlines the basic steps of statistical work, and describes different types of variables and methods for collecting data. The document also discusses different types of descriptive and inferential statistics, including measures of central tendency, dispersion, frequency, t-tests, ANOVA, regression, and different types of plots/graphs. It explains how statistics is used in healthcare for areas like disease burden assessment, intervention effectiveness, cost considerations, evaluation frameworks, health care utilization, resource allocation, needs assessment, quality improvement, and product development.
This document provides an introduction to biostatistics. It discusses how statistics are important for precision in science and medicine. Biostatistics involves applying statistical tools to biological data from fields like medicine. Some key applications of biostatistics include defining normal ranges, comparing treatment effectiveness, and identifying disease associations. The document also outlines common statistical terms, data sources and types, methods for presenting data, measures of central tendency and variability.
This document provides a lecture note on statistics for physical sciences and engineering. It begins with an introduction to statistics and its importance in various fields such as physical sciences, engineering, and research. It then discusses descriptive and inferential statistics. The document also covers topics such as data collection methods, presentation of data through tables and diagrams, and some basic statistical definitions. Examples are provided to illustrate how to construct frequency tables from raw data. In summary, the document presents an overview of key statistical concepts and methods relevant for physical sciences and engineering.
This document provides an introduction to statistics and statistical concepts. It covers topics such as course objectives, purposes of statistics, population and sampling, types of data and variables, levels of measurement, and nominal level of measurement. The key points are that statistics can describe, summarize, predict and identify relationships in data, and that there are different levels of variables from nominal to ratio scales.
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 summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
This document discusses the scope and uses of statistics across various fields such as planning, economics, business, industry, mathematics, science, psychology, education, war, banking, government, sociology, and more. It outlines functions of statistics like presenting facts, testing hypotheses, forecasting, policymaking, enlarging knowledge, measuring uncertainty, simplifying data, deriving valid inferences, and drawing rational conclusions. It also covers characteristics, advantages, and limitations of statistics.
Level of Measurement, Frequency Distribution,Stem & Leaf Qasim Raza
This document discusses multivariate data analysis and techniques. It begins by defining qualitative and quantitative data, and the different levels of measurement - nominal, ordinal, interval, and ratio. It then discusses frequency distributions, stem and leaf plots, and demonstrates their use in SPSS. Finally, it defines multivariate data analysis as involving two or more variables, and provides examples of multivariate techniques such as multiple regression, discriminant analysis, MANOVA, and their appropriate uses depending on the level of measurement of the variables.
This document discusses analyzing research data through descriptive and analytical statistics. Descriptive statistics summarize variables one by one through measures like frequency, percentage, mean, median and standard deviation depending on the variable level. Analytical statistics examine relationships between two or more variables. The document demonstrates analyzing a hypertension study dataset in SPSS, including checking normality distribution through histograms, Shapiro-Wilk test and Q-Q plots to determine appropriate tests. Frequency is used to describe categorical gender variable while numerical age is described through mean, standard deviation and histogram with normal curve fitting.
This document discusses concepts related to data, including collection, organization, presentation, and analysis of data. It defines key terms like qualitative vs quantitative data and primary vs secondary data. It explains methods of collecting primary data through surveys, sampling techniques, and secondary data from published and unpublished sources. The document also covers organizing data through frequency distributions, statistical series, and presenting data in tabular, diagrammatic and graphical forms like pie charts, histograms, bar diagrams and ogives. It concludes with analyzing organized data through measures of central tendency, dispersion, correlation and regression.
This document discusses descriptive statistics used to analyze quantitative and qualitative data from epidemiological studies. It defines key terms like prevalence, incidence, measures of central tendency (mean, median, mode), and measures of dispersion (range, standard deviation). It also covers describing categorical variables through proportions, rates, ratios and graphs like bar charts and pie charts. Coding systems are explained for different variable types. The goals of univariate descriptive analysis are also summarized.
This document discusses methods of collecting statistical data. It describes census and sample investigation methods. The census method collects data from every unit of the population, while the sample method collects data from only a few representative units. The census method is more reliable but costly, while the sample method is less expensive but less accurate. Key differences between the two methods are also outlined.
This document provides an overview of biostatistics. It defines biostatistics as the application of statistics to biology, medicine, and public health. It discusses different types of data, measures of central tendency including mean, median and mode, and graphical representations such as line graphs, bar diagrams and pie charts. The document emphasizes the important role of biostatistics in medical research and clinical decision making. It acknowledges deficiencies in biostatistical literacy among medical students and professionals.
1) Statistics is the study of collecting, organizing, analyzing, and drawing conclusions from data. It involves sampling, hypothesis testing, and using statistical tests tailored to measurement scales and hypothesis types.
2) Descriptive statistics describe and summarize data quantitatively, while inferential statistics allow generalizing from samples to populations through statistical testing and other methods.
3) The document discusses differences between statistics and statistical data, types of data, levels of measurement, sampling techniques, and uses of statistics.
This document provides an introduction to basic statistics concepts. It instructs students to collect data on the ages of classmates, organize it into a frequency table or graph, and answer questions about the distribution of ages. The document explains that statistics involves gathering, arranging, and presenting numeric data systematically, such as through tables, graphs or by sorting data in ascending or descending order. It defines statistics as the study of collecting, analyzing and interpreting data to address research questions.
This document provides an overview of key terminology and concepts in statistics. It discusses topics like populations and samples, variables and their measurement, levels of measurement, research methods like correlational analysis and experiments, and mathematical notation used in statistics. The goal is to introduce readers to what statistics is about at a high level and prepare them for further study of important statistical concepts.
Please acknowledge my work and I hope you like it. This is not boring like other ppts you see, I have tried my best to make it extremely informative with lots of pictures and images, I am sure if you choose this as your presentation for statistics topic in your office or school, you are surely going to appreciated by all including your teachers, friends, your interviewer or your manager.
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 provides information about statistical methods for summarizing data, including measures of central tendency, variability, and position. It discusses the mean, median, mode, range, variance, standard deviation, z-scores, and percentiles. The mean is the average value and considers all data points. The median divides the data in half. The mode is the most frequent value. Variance and standard deviation measure how spread out values are around the mean. Percentiles and z-scores indicate a value's position relative to others in the data set.
Recapitulation of Basic Statistical Concepts .pptxFranCis850707
The document provides definitions and explanations of basic statistical concepts. It defines statistics as concerning the collection, organization, analysis, interpretation and presentation of data. It distinguishes between populations, which are entire sets of items from which data is drawn, and samples, which are subsets of populations that are used when a population is too large. It describes descriptive statistics, which describe properties of sample and population data, and inferential statistics, which use descriptive statistics to test hypotheses and draw conclusions about populations from samples.
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.
This document provides an overview of key concepts in data management and statistics. It defines statistics as the study of collecting, organizing, and interpreting data to make inferences about populations. The main branches are descriptive statistics, which summarizes data, and inferential statistics, which generalizes from samples to populations. It also defines key terms like population, sample, parameter, statistic, variable, data, levels of measurement, and measures of central tendency and dispersion. Measures of central tendency like mean, median, and mode are used to describe the center of data, while measures of dispersion like range and standard deviation describe how spread out data are.
ANALYSIS ANDINTERPRETATION OF DATA Analysis and Interpr.docxcullenrjzsme
ANALYSIS AND
INTERPRETATION
OF DATA
Analysis and Interpretation of Data
https://my.visme.co/render/1454658672/www.erau.edu
Slide 1 Transcript
In a qualitative design, the information gathered and studied often is nominal or narrative in form. Finding trends, patterns, and relationships is discovered inductively and upon
reflection. Some describe this as an intuitive process. In Module 4, qualitative research designs were explained along with the process of how information gained shape the inquiry as it
progresses. For the most part, qualitative designs do not use numerical data, unless a mixed approach is adopted. So, in this module the focus is on how numerical data collected in either
a qualitative mixed design or a quantitative research design are evaluated. In quantitative studies, typically there is a hypothesis or particular research question. Measures used to assess
the value of the hypothesis involve numerical data, usually organized in sets and analyzed using various statistical approaches. Which statistical applications are appropriate for the data of
interest will be the focus for this module.
Data and Statistics
Match the data with an
appropriate statistic
Approaches based on data
characteristics
Collected for single or multiple
groups
Involve continuous or discrete
variables
Data are nominal, ordinal,
interval, or ratio
Normal or non-normal distribution
Statistics serve two
functions
Descriptive: Describe what
data look like
Inferential: Use samples
to estimate population
characteristics
Slide 3 Transcript
There are, of course, far too many statistical concepts to consider than time allows for us here. So, we will limit ourselves to just a few basic ones and a brief overview of the more
common applications in use. It is vitally important to select the proper statistical tool for analysis, otherwise, interpretation of the data is incomplete or inaccurate. Since different
statistics are suitable for different kinds of data, we can begin sorting out which approach to use by considering four characteristics:
1. Have data been collected for a single group or multiple groups
2. Do the data involve continuous or discrete variables
3. Are the data nominal, ordinal, interval, or ratio, and
4. Do the data represent a normal or non-normal distribution.
We will address each of these approaches in the slides that follow. Statistics can serve two main functions – one is to describe what the data look like, which is called descriptive statistics.
The other is known as inferential statistics which typically uses a small sample to estimate characteristics of the larger population. Let’s begin with descriptive statistics and the measures
of central tendency.
Descriptive Statistics and Central Measures
Descriptive statistics
organize and present data
Mode
The number occurring most
frequently; nominal data
Quickest or rough estimate
Most typical value
Measures of central
tendenc.
The document discusses various measures of central tendency used in statistics. The three most common measures are the mean, median, and mode. The mean is the sum of all values divided by the number of values and is affected by outliers. The median is the middle value when data is arranged from lowest to highest. The mode is the most frequently occurring value in a data set. Each measure has advantages and disadvantages depending on the type of data distribution. The mean is the most reliable while the mode can be undefined. In symmetrical distributions, the mean, median and mode are equal, but the mean is higher than the median for positively skewed data and lower for negatively skewed data.
This document provides an introduction to quantitative techniques and statistics. It discusses that statistics is the science of collecting, analyzing, and presenting numerical data to draw conclusions about populations based on samples. Descriptive statistics can summarize both population and sample data using measures of central tendency and dispersion. Inferential statistics is then used to draw inferences about the overall population based on patterns in sample data while accounting for randomness. The objectives, types (descriptive and inferential), advantages, and disadvantages of statistics are also outlined. Key terms are introduced but not defined in detail.
The document discusses three common measures used to find the middle of a data set: the mode, median, and mean. The mode is the most frequently occurring value, the median is the middle value when data is ordered, and if there is an even number of data points we take the average of the two middle values. The mean is calculated by adding all the numbers and dividing by the sample size.
Measures of Central Tendencies (2).pptxRishabh332761
Measures of central tendency are values that describe the middle or center of a data set. The three main measures are the mean, median, and mode. The mean is the average and is calculated by adding all values and dividing by the total number. The median is the middle number in a sorted list and divides the data set in half. The mode is the most frequent or common value in the data set. These measures can help summarize numerical data by identifying a single representative value.
Measures of central tendency are values that describe the middle or center of a data set. The three main measures are the mean, median, and mode. The mean is the average and is calculated by adding all values and dividing by the total number. The median is the middle number in a sorted list and divides the data set in half. The mode is the most frequent or common value in the data set. These measures can help summarize numerical data by identifying a single representative value.
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied.This presentation will be helpful to learn much on satistics.
The document discusses statistical measures including measures of central tendency, position, dispersion, and skewness. It defines common measures such as mean, median, mode, percentiles, standard deviation, and kurtosis. Examples are provided for calculating various statistical measures using both grouped and ungrouped data. Formulas are given for measures including the arithmetic mean, median, percentiles, standard deviation, and kurtosis. The document is intended as teaching material for a class on applied statistics in research.
Measure of central tendency grouped data.pptxSandeAlotaBoco
A measure of central tendency describes the middle or center of a data set and includes the mean, median, and mode. The mean is the average value calculated by dividing the sum of all values by the total number of values. The median is the middle number when values are arranged from lowest to highest. The mode is the value that occurs most frequently in the data set.
Dokumen tersebut membahas tentang statistika dan analisis data. Menguraikan tujuan pembelajaran untuk menganalisis data berdasarkan distribusi, nilai rata-rata, median, modus dan sebaran data. Juga menjelaskan jenis-jenis data kualitatif dan kuantitatif serta cara membaca dan menganalisis data yang disajikan dalam bentuk tabel, diagram batang dan diagram garis.
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Sub Materi: Menentukan Sudut Yang Bertolak Belakang
MATEMATIKA 7
Kelas 7
TP 2021/2022
#smp #jhs #pjj #daring #online
#sn
GARIS & SUDUT (Mengukur Besar Sudut Dengan Busur Derajat) - Pertemuan 3Shinta Novianti
Materi: BAB 6. GARIS & SUDUT
Sub Materi: Mengukur Besar Sudut Dengan Busur Derajat
Pertemuan 3
MATEMATIKA 7
Kelas 7
TP 2021/2022
#smp #jhs #pjj #daring
#sn
GARIS & SUDUT (Membagi Ruas Garis Menjadi Beberapa Bagian & Perbandingan Ruas...Shinta Novianti
Dokumen tersebut membahas tentang membagi garis menjadi beberapa bagian sama panjang dengan menggunakan jangka dan penggaris. Langkah-langkahnya adalah dengan membuat tanda pada garis sesuai dengan jumlah bagian yang diinginkan, kemudian menghubungkan tanda-tanda tersebut untuk membentuk bagian-bagian baru yang panjangnya sama. Dokumen tersebut juga menjelaskan contoh perhitungan panjang bagian garis ber
GARIS & SUDUT (Titik, Garis & Bidang) - Pertemuan 1Shinta Novianti
Pertemuan 1
PPT by Bu Meli Fitriani, S.Pd
Materi: BAB 6. GARIS & SUDUT
Sub Materi: Titik, Garis & Bidang
MATEMATIKA 7
Kelas 7
TP 2021/2022
#smp #jhs #pjj #daring
#sn
TEOREMA PYTHAGORAS (Menentukan Perbandingan Sisi Segitiga Bersudut Istimewa) ...Shinta Novianti
Ringkasan dokumen tersebut adalah:
1. Mengjelaskan teorema Pythagoras dan tripel Pythagoras serta menentukan perbandingan sisi-sisi pada segitiga siku-siku sama kaki dan bersudut istimewa.
2. Memberikan contoh soal dan penyelesaian mengenai penentuan panjang sisi pada segitiga siku-siku.
3. Menyimpulkan perbandingan sisi-siku siku dan hipotenusa pada segitiga siku-s
TEOREMA PYTHAGORAS (Menerapkan Teorema Pythgoras Untuk Menyelesaikan Masalah ...Shinta Novianti
Dokumen tersebut membahas penjelasan dan contoh soal penerapan teorema Pythagoras untuk menyelesaikan masalah nyata, termasuk 5 contoh soal beserta pembahasannya. Dokumen tersebut juga memberikan tujuan pembelajaran yaitu mampu menerapkan teorema Pythagoras untuk menyelesaikan masalah nyata.
TEOREMA PYTHAGORAS (Menentukan Panjang Sisi Segitiga Siku-Siku) - Pertemuan 2Shinta Novianti
Pertemuan 2
Materi: TEOREMA PYTHAGORAS
Sub Materi: Menentukan Panjang Sisi Segitiga Siku-Siku
MATEMATIKA
Kelas 8
TP 2021/2022
#smp
#jhs
#pjj
#daring
#sn
Ringkasan singkat dokumen tersebut adalah:
1) Dokumen tersebut membahas tentang Teorema Pythagoras dan unsur-unsurnya seperti penentuan luas persegi dan panjang sisi persegi.
2) Juga membahas tentang tripel Pythagoras yang merupakan rangkaian tiga bilangan bulat positif yang masing-masing merupakan sisi segitiga siku-siku.
3) Memberikan contoh soal dan penyelesaian masalah yang berkaitan dengan Teore
Ringkasan dokumen tersebut adalah:
1. Dokumen tersebut membahas tentang pembelajaran Sistem Persamaan Linear Dua Variabel menggunakan berbagai metode penyelesaian dan contoh soal.
2. Metode-metode penyelesaian yang dibahas antara lain metode grafik, eliminasi, substitusi, dan campuran beserta penjelasannya.
3. Terdapat pula contoh soal berupa pilihan ganda kompleks, menjodohkan, isian sing
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
4. Kompetensi Dasar dan Tujuan Pembelajaran
KOMPETENSI DASAR TUJUAN PEMBELAJARAN
• Melalui pemberian materi perserta didik
menganalisis data dari distribusi data nilai rata-
rata, median, modus, dan sebaran data, yang
diberikan dengan benar.
• Melalui penilaian harian peserta didik dapat
mengetahui kemampuan menganalisis data dari
distribusi data nilai rata-rata, median, modus, dan
sebaran data, yang diberikan dengan
menunjukkan sikap jujur, mandiri dan
bertanggung jawab.
3.8 Menganalisis data berdasarkan
distribusi data, nilai rata-rata, median,
modus, dan sebaran data untuk
mengambil kesimpulan, membuat
keputusan, dan membuat prediksi
5. Pengertian statistika, Populasi dan Sampel
• Statistika adalah ilmu yang
mempelajari semua hal
tentang data, mulai
pengumpulan, penyajian,
analisis, sampai terbentuk
suatu kesimpulan.
• Populasi adalah objek yang dijadikan
penelitian. Misalnya menghitung
tingkat kepatuhan siswa pada
peraturan sekolah jadi populasi
adalah semua siswa yang ada di
sekolah.
• Sampel adalah bagian dari populasi
yang bisa dijadikan sumber informasi.
Misalnya dari semua siswa yang
ada disekolah itu di ambil siswa
kelas 7 saja.
7. 2. Grafik garis (line chart)
Grafik garis dalah grafik berupa
garis, diperoleh dari beberapa ruas
garis yang menghubungkan titik-titik
pada bidang bilangan..
Contohnya tentang perkembangan
volume jumlah kendaraan yang
melintasi jalan A
8. 3. Digram batang
• Diagram batang adalah grafik data
berbentuk persegi panjang yang
lebarnya sama dan dilengkapi dengan
skala atau ukuran sesuai dengan data
yang bersangkutan. Setiap batang
tidak boleh saling menempel atau
melekat antara satu dengan lainnya dan
jarak antara setiap batang yang
berdekatan harus sama.
9. 4. Diagram lingkaran
• Diagram lingkaran adalah grafik
yang menggambarkan perbandingan
nilai-nilai dari suatu karakteristik.
Untuk mengetahui perbandingan
suatu data terhadap keseluruhan,
suatu data lebih tepat disajikan
dalam bentuk diagram lingkaran.
10. Rata-rata (Mean)
Rata-rata (Mean) adalah suatu ukuran pemusatan data
dengan Menjumlahkan semua bilangan pada data,
kemudian membaginya dengan banyak data.
Rumus:
11. Nilai Tengah (Median)
Nilai Tengah (Median) adalah Nilai yang berada di tengah
dari sekumpulan data yang telah diurutkan terlebih dahulu.
Nilai Tengah dilambangkan dengan Me
14. ‘’Jangkauan merupakan selisih nilai tertinggi dengan
nilai terendah dari suatu data’’
Jangkauan ( Range ) = nilai tertinggi – nilai
terendah
JANGKAUAN
15. Kuartil adalah suatu nilai yang membagi data menjadi empat
bagian yang sama, sehingga akan terdapat 3 Kuartil.
Kuartil Bawah (Q1) adalah Data yang berada dalam batas
pengelompokkan pertama.
Kuartil Tengah/Median (Q2) adalah Data yang berada dalam batas
pengelompokkan kedua. Mencari Q2
bisa menggunakan rumus Median.
Kuartil Atas (Q3) adalah Data yang berada dalam batas
pengelompokkan ketiga
16. Simpangan quartil adalah setengah dari jangkauan interquartil
SIMPANGAN QUARTIL
Simpangan Quartil =
𝟏
𝟐
(Q3 – Q1)
18. Penyelesaian
a. TV merek D paling banyak terjual
TV merek A paling sedikit terjual
b Total TV terjual selama bulan januari
adalah 12 + 20 + 16 + 22 + 12 +18 + 14 =
144 buah
19. CONTOH 2 :
Coba kamu amati data berat badan 9 siswa laki-laki
kelas VIII-F SMP ANTICOV-19 berikut ini (dalam kg):
45 57 53 50 45 48 52 49 55
Tentukan mean, median dan modus dari data tersebut!
21. INTERAKTIF
Berapakah banyak dari panen sayur B yang di usahakan pada bb ulan November agar
rata-rata pendapatan hasil panen sayur B selama bulan Juli sampai November menjadi
Rp800.000,00 ?
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