1. The mean is 4.58x10^9, the mode is 4877694675, and the median is 4877694675.
2. Dinda's mark must be 7 to make the mean 8.7.
3. There were originally 30 children with a mean of 38 kg.
4. A pie chart is made based on the occupations data, with slices for public servants, private employees, farmers, traders, and workers.
5. The number of classes is 5 and the class interval is 5 (30-34, 35-39, etc.).
This document presents information about statistics from the perspective of students in class IX E. It begins with definitions of statistics and data. Statistics is defined as the scientific study of collecting, organizing, and analyzing data to draw conclusions. There are two main types of data: qualitative and quantitative. The document then discusses key aspects of statistics including collecting and ordering data, measures of central tendency (mean, median, mode), and ways of presenting data through tables, charts and graphs. It provides examples of observing data from classes VII C and VII G on number of siblings and class rank. The summary aims to highlight the main topics and examples covered in the original lengthy document.
Data mining 5 klasifikasi decision tree dan random forestIrwansyahSaputra1
The document discusses decision trees and random forests. It begins with an introduction to decision trees, including how they are used in everyday life to make decisions. It then covers key concepts such as entropy, information gain, and how decision trees use these concepts to build tree structures by recursively splitting nodes based on predictor variables that maximize information gain. The document provides examples to illustrate entropy, information gain, and how they are used to select the root node and build the tree structure.
The document discusses the Naive Bayes classifier. It begins with an introduction to probability and defines the formula for Naive Bayes classification. It then provides an example dataset to demonstrate how to calculate the probabilities of each attribute value belonging to each class. The example shows calculating the probabilities for attributes like major, gender, school origin, GPA, and assistant status to predict whether a student's study duration will be on time or late.
Detailed Lesson Plan on Measures of Variability of Grouped and Ungrouped DataJunila Tejada
The document provides a detailed lesson plan for teaching measures of variability of grouped and ungrouped data to 7th grade mathematics students. The objectives are for students to be able to identify and calculate measures of variability, apply the concepts to real-life contexts, and solve problems involving grouped and ungrouped data. The lesson plan outlines teacher and student activities including an introductory activity to review key concepts, a lesson on different measures of variability, and a group activity for students to practice calculating various measures of variability from tables of grouped and ungrouped data.
Chapter 8 Measure of Dispersion of DataMISS ESTHER
This document discusses measures of dispersion for ungrouped data. It defines dispersion as how scattered the values in a data set are. Measures of dispersion include range, interquartile range, variance and standard deviation. These measures quantify how spread out the data is by looking at the differences between values. Examples are provided to demonstrate calculating the range and comparing the dispersion of two data sets using dot plots.
Linear regression is a statistical method used to analyze and understand the relationship between two or more variables. It predicts a numeric target variable based on one or more independent variables. Single linear regression uses one independent variable to predict the dependent variable based on a linear equation. The document provides examples of calculating linear regression coefficients and making predictions using the linear regression equation. It also discusses evaluating linear regression models using metrics like MAE, MSE, and RMSE.
This document provides guidance on writing a persuasive essay, including determining the purpose, audience, and thesis statement. It outlines the typical five-paragraph structure, with an introduction, three body paragraphs presenting main points, and a conclusion. Each body paragraph includes a topic sentence, supporting examples, and a transition to the next point. The conclusion restates the thesis and main ideas. Sample essay prompts are provided on the topics of fame, lying, and petitioning parents for a privilege.
El documento describe objetos virtuales de aprendizaje (OVA), ambientes virtuales de aprendizaje (AVA), tecnología, la historia de Internet, páginas web y la web 2.0. Los OVA son contenidos digitales autocontenibles y reutilizables que incluyen actividades educativas. Los AVA son sistemas de software que facilitan la gestión de cursos virtuales y apoyan el aprendizaje colaborativo a través de herramientas de comunicación. La tecnología se define y el proceso tecnológico se describe en cinco
This document presents information about statistics from the perspective of students in class IX E. It begins with definitions of statistics and data. Statistics is defined as the scientific study of collecting, organizing, and analyzing data to draw conclusions. There are two main types of data: qualitative and quantitative. The document then discusses key aspects of statistics including collecting and ordering data, measures of central tendency (mean, median, mode), and ways of presenting data through tables, charts and graphs. It provides examples of observing data from classes VII C and VII G on number of siblings and class rank. The summary aims to highlight the main topics and examples covered in the original lengthy document.
Data mining 5 klasifikasi decision tree dan random forestIrwansyahSaputra1
The document discusses decision trees and random forests. It begins with an introduction to decision trees, including how they are used in everyday life to make decisions. It then covers key concepts such as entropy, information gain, and how decision trees use these concepts to build tree structures by recursively splitting nodes based on predictor variables that maximize information gain. The document provides examples to illustrate entropy, information gain, and how they are used to select the root node and build the tree structure.
The document discusses the Naive Bayes classifier. It begins with an introduction to probability and defines the formula for Naive Bayes classification. It then provides an example dataset to demonstrate how to calculate the probabilities of each attribute value belonging to each class. The example shows calculating the probabilities for attributes like major, gender, school origin, GPA, and assistant status to predict whether a student's study duration will be on time or late.
Detailed Lesson Plan on Measures of Variability of Grouped and Ungrouped DataJunila Tejada
The document provides a detailed lesson plan for teaching measures of variability of grouped and ungrouped data to 7th grade mathematics students. The objectives are for students to be able to identify and calculate measures of variability, apply the concepts to real-life contexts, and solve problems involving grouped and ungrouped data. The lesson plan outlines teacher and student activities including an introductory activity to review key concepts, a lesson on different measures of variability, and a group activity for students to practice calculating various measures of variability from tables of grouped and ungrouped data.
Chapter 8 Measure of Dispersion of DataMISS ESTHER
This document discusses measures of dispersion for ungrouped data. It defines dispersion as how scattered the values in a data set are. Measures of dispersion include range, interquartile range, variance and standard deviation. These measures quantify how spread out the data is by looking at the differences between values. Examples are provided to demonstrate calculating the range and comparing the dispersion of two data sets using dot plots.
Linear regression is a statistical method used to analyze and understand the relationship between two or more variables. It predicts a numeric target variable based on one or more independent variables. Single linear regression uses one independent variable to predict the dependent variable based on a linear equation. The document provides examples of calculating linear regression coefficients and making predictions using the linear regression equation. It also discusses evaluating linear regression models using metrics like MAE, MSE, and RMSE.
This document provides guidance on writing a persuasive essay, including determining the purpose, audience, and thesis statement. It outlines the typical five-paragraph structure, with an introduction, three body paragraphs presenting main points, and a conclusion. Each body paragraph includes a topic sentence, supporting examples, and a transition to the next point. The conclusion restates the thesis and main ideas. Sample essay prompts are provided on the topics of fame, lying, and petitioning parents for a privilege.
El documento describe objetos virtuales de aprendizaje (OVA), ambientes virtuales de aprendizaje (AVA), tecnología, la historia de Internet, páginas web y la web 2.0. Los OVA son contenidos digitales autocontenibles y reutilizables que incluyen actividades educativas. Los AVA son sistemas de software que facilitan la gestión de cursos virtuales y apoyan el aprendizaje colaborativo a través de herramientas de comunicación. La tecnología se define y el proceso tecnológico se describe en cinco
The document discusses collecting and analyzing data through surveys. It defines key terms like variables, category data, and discrete data. Category data is described in words, while discrete data uses numbers. Data can be organized and presented in tables, tally charts, frequency tables, dot plots, and bar graphs. Bar graphs can show and compare data, with either a horizontal or vertical orientation. The document provides examples of each term and type of data presentation. It concludes with a summary of the key points and practice questions.
This document discusses various methods of collecting and presenting data. It describes how data is collected through direct observation, experiments, and surveys. Common data collection methods include surveys, interviews, observations, and existing databases. The document also discusses how to ensure data is reliable and valid. Additionally, it covers different ways of presenting data through tabulation and diagrams. Tabulation methods include classification by space, time, attributes, and size of observations. Common diagrams for presenting data include histograms, bar diagrams, pie charts, scatter plots, and maps.
This document provides an overview of statistics as a subject. It begins by stating the competencies students are expected to have after learning about statistics, including collecting and processing data, calculating measures of central tendency, and presenting data visually. It then defines statistics as the scientific study of planning, collecting, organizing, analyzing, and drawing conclusions from data. It discusses the difference between qualitative and quantitative data and explains the process of collecting data, arranging it, analyzing it, and drawing conclusions. It also covers topics like measures of central tendency, data presentation methods, and the difference between populations and samples. The overall document serves as a high-level introduction to statistics, its key concepts, and how it is used.
- There are three main methods for collecting data: direct observation, experiments, and surveys. Some common ways to conduct surveys include mailing questionnaires, telephone interviews, and face-to-face interviews.
- Data can be either qualitative (characterized by words) or quantitative (characterized by numbers). Quantitative data can further be classified as discrete or continuous.
- It is important to ensure data is both valid, meaning it accurately measures what it intends to, and reliable, meaning consistent results are produced. Primary data collection directly by the researcher allows for more control but takes more time and resources than using secondary sources.
This document provides an overview of key topics in statistics for management. It covers statistical surveys, classification and presentation of data, measures used to summarize data, probabilities, theoretical distributions, sampling and sampling distributions, estimation, hypothesis testing for large and small samples, and chi-square, F-distribution, analysis of variance, correlation, regression, business forecasting, and time series analysis. The document serves as an introduction to important statistical concepts and methods relevant for management.
The document provides guidance on analyzing and interpreting data in teaching elementary science. It discusses the objectives of interpreting data, which include analyzing given data, making interpretations based on evidence, organizing data in different formats, making inferences, and understanding dependent and independent variables. Examples are given of different types of graphs like pie charts, line graphs, and bar graphs that can be used to visualize and analyze data. Steps for interpreting data involve organizing it, creating a graph, looking for trends, making inferences, and checking inferences against existing knowledge. The document emphasizes that interpreting data relies on human judgment and cognition.
This document discusses outliers, exploratory data analysis, stem-and-leaf plots, boxplots, and probability. It provides steps for identifying outliers in a dataset and constructing a stem-and-leaf plot and boxplot to represent data. Key aspects of probability are also reviewed, including classical probability, sample spaces, simple and compound events, and basic probability rules.
The document discusses data analysis methods, including collecting, organizing, and interpreting data to draw helpful conclusions. It outlines steps for data analysis: 1) organize raw data in a spreadsheet, 2) check the data for errors, 3) ask questions of the data by calculating frequencies, centers, and comparing subgroups, and 4) share findings. Examples show organizing student survey data on novels read into a spreadsheet and calculating the mode, median, and comparing readings between male and female students. The document emphasizes the importance of data at different organizational levels to improve strategies and interventions.
STRAND 5 DATA HANDLING AND PROBABILITY.pptxkimdan468
This document outlines learning objectives and lessons about data presentation and interpretation, including bar graphs, line graphs, calculating mean, mode, median, and probability. The key points are:
- Students should be able to interpret and draw bar graphs and line graphs of real-life data, and calculate mean, mode, median of discrete data sets.
- Probability can be expressed as a fraction, decimal or percentage and is a measure of how likely something is to occur based on chance experiments.
- Examples are provided to demonstrate calculating mean, median, mode from data sets and determining probabilities of events from experiments.
This document provides an introduction to biostatistics and descriptive statistics concepts. It defines key terms like data, variables, populations, samples, and measurement scales. It also discusses measures of central tendency like mean, median and mode. Measures of dispersion such as range, variance, standard deviation and coefficient of variation are introduced. Finally, the document discusses frequency distributions, histograms, percentiles, quartiles, and box plots as ways to summarize and visualize data distributions. Examples are provided throughout to illustrate statistical concepts.
The document provides an agenda for a math workshop on using manipulatives to help students develop mastery of common core math standards. The workshop includes sessions on using specific math tools, teaching numbers less than one, and resources for 21st century teaching and learning. The objectives are to increase awareness of using manipulatives to develop conceptual understanding and provide strategies and resources to support math instruction.
This document discusses conducting a mini-research on students' performance in mathematics. It provides steps to take, including stating the problem, designing the research, gathering data, using statistical treatment on the data, and formulating conclusions and recommendations. An example is given of a mini-research on the performance of Grade 10 MDL students in mathematics for the third quarter. The results found the average grade was 84% and recommendations were made to provide intervention to the 25% of students who scored below 75%. Learning tasks are also provided to have students conduct their own mini-research on student performance.
for teaching, teachers must know how to create PowerPoint to have more engaging teaching to students. it can be used for giving a possible task, or an amazing activity. but teachers also know that some of the students want extra colorful like art paper or traditional materials. To easily get the attention of the students, teachers can create more engaging games, funny games creative games that relate to your topic.
This document provides an introduction to a unit on organizing, summarizing, and interpreting data. It outlines the materials and expectations for the online lessons. The first lesson will focus on defining data and different ways of collecting and analyzing it. Students will then learn about classifying data by type and number of variables. The lesson will demonstrate displaying data using graphs like histograms, frequency tables, and box plots. It will also cover describing data distributions and comparing data sets using measures of center, spread, and shape.
This document provides an overview of exploratory data analysis (EDA). It discusses the key goals of EDA as understanding the characteristics of a dataset and selecting appropriate analysis tools. The document outlines common EDA tasks like calculating summary statistics, creating visualizations, and detecting patterns and anomalies. Specific techniques covered include frequency tables, measures of central tendency and spread, histograms, box plots, contingency tables, and scatter plots. The document emphasizes exploring one variable at a time before examining relationships between multiple variables to better understand the dataset.
Statistics is the science and art of learning from data. It involves collecting and analyzing data to describe variables and examine relationships between variables. There are different types of variables, including categorical and quantitative variables, and different ways of collecting data, such as surveys, experiments, and observational studies. Graphs and numerical summaries are used to organize and make sense of the data. Statistical inference allows conclusions to be drawn from a sample to a larger population with some degree of uncertainty.
2. week 2 data presentation and organizationrenz50
Here are the answers to the questions:
A.
1. The variables in the graph are age (x-axis) and frequency (y-axis).
2. The variables are quantitative.
3. The variables are discrete.
4. No, a pie chart could not be used to display this data since it involves quantitative variables rather than categorical variables.
B.
1. A line graph would most appropriately represent the number of students enrolled at a local college for each year during the last 5 years. This involves two quantitative variables - years on the x-axis and enrollments on the y-axis.
2. A bar graph would most appropriately represent the frequency of each type of crime committed in
Data mining and machine learning techniques like classification and clustering are increasingly being used to extract useful information from large datasets. Data mining helps provide better customer service and aids scientists in hypothesis formation by analyzing patterns in data from various sources like business transactions, sensor networks, and scientific experiments. Classification algorithms such as decision trees can be applied to datasets containing attributes for individuals and a target variable to predict, like credit worthiness, to build a predictive model. Clustering algorithms like K-means group unlabeled data into clusters without a predefined target variable to discover hidden patterns in the data.
This lesson plan is for a 2 hour math class about tables and line graphs for 5th grade students. The objectives are for students to understand the importance of tables and line graphs in organizing data and be able to create a table and corresponding line graph. During the lesson, the teacher will use direct instruction, classroom tasks, and homework. Students will participate by answering questions and creating their own tables and graphs. The teacher will check for understanding and repeat the main points at the end. Students will apply what they learned by completing a homework assignment involving creating a line graph from attendance data in a table. Learning will be assessed through questioning and reviewing the homework.
Kebijakan moneter adalah upaya untuk mencapai tingkat pertumbuhan ekonomi yang tinggi secara berkelanjutan dengan tetap mempertahankan kestabilan harga.
DEMOKRASI PANCASILA DAN PERKEMBANGAN POLITIK MASA ORDE BARURifda Nadifah
Dokumen ini membahas tentang demokrasi Pancasila dan perkembangan politik pada masa Orde Baru di Indonesia. Pemerintahan Orde Baru diawali setelah terbitnya Surat Perintah 11 Maret 1966 untuk mengatasi keamanan negara pasca Gerakan 30 September 1965. Masa Orde Baru melaksanakan demokrasi Pancasila dengan musyawarah dan mufakat serta melakukan pemilu berkesinambungan, penyederhanaan partai politik, dan pedoman pengh
The document discusses collecting and analyzing data through surveys. It defines key terms like variables, category data, and discrete data. Category data is described in words, while discrete data uses numbers. Data can be organized and presented in tables, tally charts, frequency tables, dot plots, and bar graphs. Bar graphs can show and compare data, with either a horizontal or vertical orientation. The document provides examples of each term and type of data presentation. It concludes with a summary of the key points and practice questions.
This document discusses various methods of collecting and presenting data. It describes how data is collected through direct observation, experiments, and surveys. Common data collection methods include surveys, interviews, observations, and existing databases. The document also discusses how to ensure data is reliable and valid. Additionally, it covers different ways of presenting data through tabulation and diagrams. Tabulation methods include classification by space, time, attributes, and size of observations. Common diagrams for presenting data include histograms, bar diagrams, pie charts, scatter plots, and maps.
This document provides an overview of statistics as a subject. It begins by stating the competencies students are expected to have after learning about statistics, including collecting and processing data, calculating measures of central tendency, and presenting data visually. It then defines statistics as the scientific study of planning, collecting, organizing, analyzing, and drawing conclusions from data. It discusses the difference between qualitative and quantitative data and explains the process of collecting data, arranging it, analyzing it, and drawing conclusions. It also covers topics like measures of central tendency, data presentation methods, and the difference between populations and samples. The overall document serves as a high-level introduction to statistics, its key concepts, and how it is used.
- There are three main methods for collecting data: direct observation, experiments, and surveys. Some common ways to conduct surveys include mailing questionnaires, telephone interviews, and face-to-face interviews.
- Data can be either qualitative (characterized by words) or quantitative (characterized by numbers). Quantitative data can further be classified as discrete or continuous.
- It is important to ensure data is both valid, meaning it accurately measures what it intends to, and reliable, meaning consistent results are produced. Primary data collection directly by the researcher allows for more control but takes more time and resources than using secondary sources.
This document provides an overview of key topics in statistics for management. It covers statistical surveys, classification and presentation of data, measures used to summarize data, probabilities, theoretical distributions, sampling and sampling distributions, estimation, hypothesis testing for large and small samples, and chi-square, F-distribution, analysis of variance, correlation, regression, business forecasting, and time series analysis. The document serves as an introduction to important statistical concepts and methods relevant for management.
The document provides guidance on analyzing and interpreting data in teaching elementary science. It discusses the objectives of interpreting data, which include analyzing given data, making interpretations based on evidence, organizing data in different formats, making inferences, and understanding dependent and independent variables. Examples are given of different types of graphs like pie charts, line graphs, and bar graphs that can be used to visualize and analyze data. Steps for interpreting data involve organizing it, creating a graph, looking for trends, making inferences, and checking inferences against existing knowledge. The document emphasizes that interpreting data relies on human judgment and cognition.
This document discusses outliers, exploratory data analysis, stem-and-leaf plots, boxplots, and probability. It provides steps for identifying outliers in a dataset and constructing a stem-and-leaf plot and boxplot to represent data. Key aspects of probability are also reviewed, including classical probability, sample spaces, simple and compound events, and basic probability rules.
The document discusses data analysis methods, including collecting, organizing, and interpreting data to draw helpful conclusions. It outlines steps for data analysis: 1) organize raw data in a spreadsheet, 2) check the data for errors, 3) ask questions of the data by calculating frequencies, centers, and comparing subgroups, and 4) share findings. Examples show organizing student survey data on novels read into a spreadsheet and calculating the mode, median, and comparing readings between male and female students. The document emphasizes the importance of data at different organizational levels to improve strategies and interventions.
STRAND 5 DATA HANDLING AND PROBABILITY.pptxkimdan468
This document outlines learning objectives and lessons about data presentation and interpretation, including bar graphs, line graphs, calculating mean, mode, median, and probability. The key points are:
- Students should be able to interpret and draw bar graphs and line graphs of real-life data, and calculate mean, mode, median of discrete data sets.
- Probability can be expressed as a fraction, decimal or percentage and is a measure of how likely something is to occur based on chance experiments.
- Examples are provided to demonstrate calculating mean, median, mode from data sets and determining probabilities of events from experiments.
This document provides an introduction to biostatistics and descriptive statistics concepts. It defines key terms like data, variables, populations, samples, and measurement scales. It also discusses measures of central tendency like mean, median and mode. Measures of dispersion such as range, variance, standard deviation and coefficient of variation are introduced. Finally, the document discusses frequency distributions, histograms, percentiles, quartiles, and box plots as ways to summarize and visualize data distributions. Examples are provided throughout to illustrate statistical concepts.
The document provides an agenda for a math workshop on using manipulatives to help students develop mastery of common core math standards. The workshop includes sessions on using specific math tools, teaching numbers less than one, and resources for 21st century teaching and learning. The objectives are to increase awareness of using manipulatives to develop conceptual understanding and provide strategies and resources to support math instruction.
This document discusses conducting a mini-research on students' performance in mathematics. It provides steps to take, including stating the problem, designing the research, gathering data, using statistical treatment on the data, and formulating conclusions and recommendations. An example is given of a mini-research on the performance of Grade 10 MDL students in mathematics for the third quarter. The results found the average grade was 84% and recommendations were made to provide intervention to the 25% of students who scored below 75%. Learning tasks are also provided to have students conduct their own mini-research on student performance.
for teaching, teachers must know how to create PowerPoint to have more engaging teaching to students. it can be used for giving a possible task, or an amazing activity. but teachers also know that some of the students want extra colorful like art paper or traditional materials. To easily get the attention of the students, teachers can create more engaging games, funny games creative games that relate to your topic.
This document provides an introduction to a unit on organizing, summarizing, and interpreting data. It outlines the materials and expectations for the online lessons. The first lesson will focus on defining data and different ways of collecting and analyzing it. Students will then learn about classifying data by type and number of variables. The lesson will demonstrate displaying data using graphs like histograms, frequency tables, and box plots. It will also cover describing data distributions and comparing data sets using measures of center, spread, and shape.
This document provides an overview of exploratory data analysis (EDA). It discusses the key goals of EDA as understanding the characteristics of a dataset and selecting appropriate analysis tools. The document outlines common EDA tasks like calculating summary statistics, creating visualizations, and detecting patterns and anomalies. Specific techniques covered include frequency tables, measures of central tendency and spread, histograms, box plots, contingency tables, and scatter plots. The document emphasizes exploring one variable at a time before examining relationships between multiple variables to better understand the dataset.
Statistics is the science and art of learning from data. It involves collecting and analyzing data to describe variables and examine relationships between variables. There are different types of variables, including categorical and quantitative variables, and different ways of collecting data, such as surveys, experiments, and observational studies. Graphs and numerical summaries are used to organize and make sense of the data. Statistical inference allows conclusions to be drawn from a sample to a larger population with some degree of uncertainty.
2. week 2 data presentation and organizationrenz50
Here are the answers to the questions:
A.
1. The variables in the graph are age (x-axis) and frequency (y-axis).
2. The variables are quantitative.
3. The variables are discrete.
4. No, a pie chart could not be used to display this data since it involves quantitative variables rather than categorical variables.
B.
1. A line graph would most appropriately represent the number of students enrolled at a local college for each year during the last 5 years. This involves two quantitative variables - years on the x-axis and enrollments on the y-axis.
2. A bar graph would most appropriately represent the frequency of each type of crime committed in
Data mining and machine learning techniques like classification and clustering are increasingly being used to extract useful information from large datasets. Data mining helps provide better customer service and aids scientists in hypothesis formation by analyzing patterns in data from various sources like business transactions, sensor networks, and scientific experiments. Classification algorithms such as decision trees can be applied to datasets containing attributes for individuals and a target variable to predict, like credit worthiness, to build a predictive model. Clustering algorithms like K-means group unlabeled data into clusters without a predefined target variable to discover hidden patterns in the data.
This lesson plan is for a 2 hour math class about tables and line graphs for 5th grade students. The objectives are for students to understand the importance of tables and line graphs in organizing data and be able to create a table and corresponding line graph. During the lesson, the teacher will use direct instruction, classroom tasks, and homework. Students will participate by answering questions and creating their own tables and graphs. The teacher will check for understanding and repeat the main points at the end. Students will apply what they learned by completing a homework assignment involving creating a line graph from attendance data in a table. Learning will be assessed through questioning and reviewing the homework.
Kebijakan moneter adalah upaya untuk mencapai tingkat pertumbuhan ekonomi yang tinggi secara berkelanjutan dengan tetap mempertahankan kestabilan harga.
DEMOKRASI PANCASILA DAN PERKEMBANGAN POLITIK MASA ORDE BARURifda Nadifah
Dokumen ini membahas tentang demokrasi Pancasila dan perkembangan politik pada masa Orde Baru di Indonesia. Pemerintahan Orde Baru diawali setelah terbitnya Surat Perintah 11 Maret 1966 untuk mengatasi keamanan negara pasca Gerakan 30 September 1965. Masa Orde Baru melaksanakan demokrasi Pancasila dengan musyawarah dan mufakat serta melakukan pemilu berkesinambungan, penyederhanaan partai politik, dan pedoman pengh
Demokrasi Pancasila Orde Baru (1966-1998)Rifda Nadifah
Dokumen tersebut merangkum tentang demokrasi Pancasila pada masa Orde Baru di Indonesia dari tahun 1966 hingga 1998. Ia menjelaskan latar belakang lahirnya Orde Baru setelah Orde Lama, kebijakan ekonomi seperti Repelita dan swasembada beras, serta penataan politik luar negeri seperti kembali menjadi anggota PBB dan normalisasi hubungan dengan negara lain.
The document discusses the factors that led to the Protestant Reformation in the 16th century. Some of the key factors mentioned include:
1. Corruption and abuse of power within the Catholic Church hierarchy.
2. The rise of nationalism across European states weakened the influence of the Church.
3. Theologians like Martin Luther and John Calvin criticized the Church and its doctrines, helping spark the Reformation.
SEJARAH NABI MUHAMMAD PERIODE MADINAH (SKI)Rifda Nadifah
Tahukah anda, alasan Nabi Muhammad SAW hijrah ke Madinah ?
Karena tekanan dan gangguan bahkan ancaman masyarakat Quraisy terhadap dirinya dan umat Islam semakin menjadi. Beliau memerintahkan para sahabatnya terlebih dahulu untuk pergi ke Madinah. Ketika kaum musyrikin Mekkah mendengar rencana tersebut, mereka sangat marah dan berusaha merencanakan pembunuhan terhadap Nabi. Berita ancaman itu segera didengar Nabi, lalu ia bersama Abu Bakar dan Ali menunggu perintah Allah. Ketika suasana semakin kritis, turunlah perintah Allah yang memerintahkan Nabi-Nya hijrah ke Madinah.
Atas berbagai pertimbangan di atas, Nabi Muhammad saw. menempuh jalan hijrah sebagai alternatif perjuangan untuk menegakkan ajaran Islam.
SENI RUPA MURNI NUSANTARA DAN MANCANNEGARARifda Nadifah
Dokumen tersebut membahas tentang seni rupa murni Nusantara dan mancanegara yang mencakup seni lukis, seni patung, seni grafis, aliran-alirannya seperti naturalisme, realisme, impresionisme, ekspresionisme, kubisme, dan juga jenis, bahan, alat, serta teknik pembuatan patung.
4. A. The Definition Of Statistic
• Statistics is a study or scientific method of
collecting, organizing, processing, presenting, an
alizyng data and drawing a conclusion according
to the result of the data analysis.
5. 1) Presenting Data
From the following definition, we know that
statistics is closely releated to data.
• Data is the information obtained from the
result of observation or research .
• The singular form of data is datum
• Data can describe a situation, so that based
on the data we can draw a conclution
6. According to the types, data can
be divided into two. They are :
1. Qualitative data
are data releated to the category or
characteristic of an object.
For examples :
a. Data about the cleanest country in the
world (Singapore, Japan, Australia, or the
other)
b. Data about the best female singer
(BoA, Taylor Swift, Beyonce, Rihanna, Whitney
Housten or the other)
Qualitative data are usually obtained by interview
7. 2. Quantitative data, are obtained from
the result of numerical recording (in
the form of numbers)
For examples are :
a. Data about the weight in the class
(48kg, 45.5kg, 45kg, 49kg, 48kg,
50kg and so on)
b. Data about the number of sister (2,
3, 2, 4, 1, 0 and so on)
8. As we know, the data are used to draw a
conclusion. But before draw a conclusion, the
data that have been collected beforehand
arranged, processed, presented, then analyzed
carefully, accurately and according to the right
theory.
Based on the data that have been analyzed, we
can draw a conclusion
There are several examples of conclutions :
a. The average of students in the class IX E is 14
years old
b. The favorite teacher in Nedusi is Mr. Yoko
c. The favorite subject in class IX E is Mathematic
9. 2). Collection of Data
• Collection of data is the beginning stage (the
first step) in the statistical activity.
• Collecting data can be performed by interview
of questionnaire, then continued by one of the
following activities :
a. Counting
b. Measuring
c. Recording data by tallies
10. 3). Ordering Single Data
• Simple data is only consist of relatively small
data.
• It isn’t necessary to group them, ordering data
is enaugh
• In general, the collected statiscal data are still
scattered and the measurement isn’t in order.
• However, to simply presentation and processing
of data, we need to order them from the
lowest value to the highest value. After the
data are ordered, we can determine the highest
and the lowest value of data.
11. Measures Of Central Tendency
• The collected and even the ordered data
don’t automatically give clear information.
• To get clearer information, we need
measures to represent the collection of
data.
• The measures are known as the measures
of central tendency
• By knowing measures of central
tendency, we can find out the value of the
centralized data.
12. The Measures Of Central
Tendency Consist Of 3
Measures, Namely :
1. MEAN (arithmetic mean)
2. MODE (the value that occurs most
often)
3. MEDIAN (the middle value)
13. 1. Mean
• In statistic, the average value is usually called
arithmetic mean or simply mean.
• So, mean is the average value of all data values
• Mean is one of the measures of central tendency that
is most often used.
• The mean of data set is the sum of all data values
divided by the number of data.
X
☞ X
n
Example : Data = 7 8 9 8 5 9 9 6 4
The ordered data =4 5 6 7 8 8 9 9 9
= 7.3
14. 2. Mode
• Mode is generally used to express the tendency which
most frequently happens.
• In statistics, the mode means the value that occurs
most frequently or the value of highest frequency.
Example :
Data = 3 5 8 6 5 9 8 8 6 No. Marks Tally Frequency
The ordered data = 3 5 5 6 6 8 8 8 8 9 1. 3 l 1
2. 5 ll 2
3. 6 ll 2
Based on the data above, the 4. 8 llll 4
value of highest frequency is 5. 9 l 1
8
So, the mode is 8
15. 3. Median
• Median is a measure of central tendency used to analyze data.
• If the number of data ordered is odd, then the median is the
data value right in the middle that divides the data into two
equal parts
• And if the number of data ordered is even, then the median is
the mean of two data values in the middle
Median = The middle value after being ordered
Example :
a. If n is odd, then the median is Data : 9 9 7 5 8 7 3 6 4
The ordered data : 3 4 5 6 7 7 8 9 9
Because n is odd (9), so the median is
in the order to :
b. If n is even, then the median is 5
Median = 3 4 5 6 7 7 8 9 9
4 Median 4
So, the median is 7
16. C. The Presentation Of
Data
• The data collected and ordered according to the need should be
presented in a certain form in such away that they can be read
and interpreted easily
The presentation of statiscal data can be performed in two
methods, they are :
1. Presented in the form of list or table
a. Frequency table of single data
b. Frequency table of grouped data
2. Presented in the form of a diagram or graph
a. Pictogram (A method of presenting information using picture)
b. Bar chart (A method of presenting data in bars)
c. Line graph (A method of presenting data in a line)
d. Pie charts (A method of presenting data in a circular region)
21. Quartile
• Quartile means the grouping of four, split the
data that has been sorted into four equal
parts.
• To present quartile, we used Q symbol.
Q₁ = initial quartile / bottom quartile (¼)
Q₂ = middle quartile / median (¾)
Q₃ = upper quartile (²⁄₄)
22. We Have Some
Observation In Class
VII C And Class VII G
Curious ?
Do you want to know ?
Serious ?
Okay Let’s check it out !
23.
24. Observation In Class VII G
(Quantitative Data)
Questions :
1. How many sister
do you have?
2. How many
brother do you
have?
3. When you were in
class six, what
rank did you get ?
25. 1. The Number of Sisters
Owned by Students of VII G
Sisters
12
10
Haven't Sister
Students
8
Have 1 Sister
6
Have 2 Sisters
4
Have 3 Sisters
2 Have 4 Sisters
0 Have 5 Sisters
Haven't Have 1 Have 2 Have 3 Have 4 Have 5
Sister Sister Sisters Sisters Sisters Sisters
26. So, from the data we can make a
conclution :
a. The students that haven’t sister are 10 students
b. The students that have 1 sister are 8 students
c. The students that have 2 sisters are 6 students
d. The students that have 3 sisters are 2 students
e. The student that have 4 sisters is nothing
f. The students that have 5 sisters are 2 students
The number of students that have the most sister are 2 students
And the students that haven’t sister are 4 students
27. 2. The Number of Brothers Owned
by Students of VII G
Broters
Students That Haven't
6 Brother
10 Students That Have 1
Brother
3 Students That Have 2
Brothers
Students That Have 3
9
Brothers
28. So, from the data we can make
the conclution :
a. The students that haven’t brother 10 students
b. The students that have 1 brother are 9 students
c. The students that have 2 brothers are 3 students
d. The students that have 3 brothers are 6 students
e. The student that have 4 brothers and 5 brothers is nothing
The number of students that have the most brothers (3 brothers) are
6 students
And the number of students that haven’t brother are 10 students
29. 3. The Rank Of Ranked
Students
Students VII G
1
2
When They 3
4
Were In Class 5
Six
8
9
10
11
12
13
14
18
19
20
30. 4. The Interest or The Favorite
Language in Grade VII C
Favorite Language
Bahasa Indramayu
France Bahasa Indonesia
English
Japan
Japan
English France
Bahasa Indramayu
Bahasa Indonesia
0 2 4 6 8 10 12 14
31. Observation In Class VII C
(Qualitative Data)
Question :
1. What kind of
language do you like ?
2. Which country will be
visited most ?
3. Which city will you go
to your studytour ?
32. So, From The Data We Can Make
Conclution :
a. The students who interested Bahasa Indonesia are 3
students
b. The students who interested English are 14 students
c. The students who interested Japan Language are 9
students
d. The students who interested France are 2 students
e. The student who interested Indramayu Language is
nothing
So, English is the most interested
language. And Indramayu language isn’t
interested with all students VII C
33. 5. The Country Will Be Visited Most By
Students VII C
The Countries
4% 4% 3%
7%
South Korea
41%
24%
Japan
17% France
Saudi Arabia
Holand
Rumania
The number of students VII C is 28 Australia
students
34. So, from the data we can make
the conclution :
o The number of students VII C will be visited South Korea are 12
students
o The number of students VII C will be visited Japan are 5 students
o The number of students VII C will be visited France are 7 students
o The number of students VII C will be visited Saudi Arabia are 2
students
o The number of students VII C will be visited Holand is 1 student
o The number of students VII C will be visited Rumania is 1 student
o The number of students VII C will be visited Australia is 1 student
So, the city will be visited most is South Korea
And the city will be visited least is Holand, Rumania, and Australia
35. 6. The City That Very Want To
Visit To Your Studytour
12
10
Bali
8 Lombok
Yogyakarta
Frequency
6
Paris
4
Jakarta
2 Bandung
Raja Ampat
0
0 2 4 6 8
-2
36. So, from the data we can make
the conclution
• The students who choose Bali to visit to their studytour are 11 students
• The students who choose Lombok to visit to their studytour are 5
students
• The students who choose Yogyakarta to visit to their studytour are 2
students
• The students who choose Paris to visit to their studytour are 5 students
• The student who choose Jakarta to visit to their studytour is nothing
• The student who choose Bandung to visit to their studytour is nothing
• The student who choose Raja Ampat to visit to their studytour is 1
student
So, the city that most choose to visit to students of VII C’s studytour
is South Korea
And the cities that not choose to visit to students of VII C’s studytour
are Jakarta and Bandung
37. We Have Learn About
Statistics
So, please answer these
exercises
38. EXERCISE
1. Given the following data
2681435394
6842552555
4877694675
Based on the data above :
a. Make the frequency table
b. Find the mean, the mode and the median
39. 2. The mean mark of
mathematics test of 15
students is 8.6 if
Sofhie’s mark is
included, then the mean
mark becomes 8.7 find
Dinda’s mark in the test.
3. The mean weight of a
group of children is 38 kg.
Children having a mean
weight of 40 kg are
added, then the mean
weight becomes 38.4 kg.
Find the number of
children at first
40. 4. Consider the table below
KINDS OF PEOPLE’S OCCUPATIONS
IN VILLAGE A
NO. Kinds Of Occupations Frequency
1 Public Servants 56
2 Private Employees 42
3 Farmers 84
4 Traders 126
5 Workers 28
The frequency table above shows the data about the
kinds of occupations of people at a village. Make a pie
chart based on the data.
41. 5. The number of classes and the class
interval in the table of frequency
distribution respectively are ...
Value Frequency
30-34 2
35-39 7
40-44 11
45-49 4
50-54 3