Name: descriptive statistics and graphs
Uploaded: 2023-04-30T11:12:52.000Z
Duration: 1 min 13 s
Channel: JoVE

K12 Standards

Virginia  Standards of Learning

HIgh School

Earth Science

ES.1

ES.1.c

Review and Preview

Rappelons que les données sont généralement classées en données quantitatives et en données qualitatives.
Les données quantitatives représentent les mesures ou le dénombrement de valeurs numériques, telles que les tailles variables des élèves d’une classe.
À l’inverse, les données qualitatives, également appelées données catégorielles, représentent des variables non numériques telles que les différentes couleurs de cheveux.
Pour une analyse statistique efficace, ces grands ensembles de données non organisés sont résumés et représentés numériquement sous forme tabulaire ou visuellement sous forme graphique.
Par exemple, les changements de température mesurés au cours d’une journée peuvent être résumés sous la forme d’un tableau.
Ces données peuvent également être représentées graphiquement. Ici, le temps est donné le long de l’axe horizontal et la température est affichée le long de l’axe vertical.
Les points du graphique sont joints pour former le modèle, ce qui permet de comprendre visuellement comment la température diurne change avec le temps.
Le graphique identifie également les valeurs aberrantes par rapport aux autres valeurs de données qui indiquent des températures extrêmes observées pendant la journée.

révision et aperçu

Les données sont des éléments individuels d'information obtenus à partir d'une population ou d'un échantillon. Les données peuvent être classées en qualitatives (catégorielles), quantitatives continues ou quantitatives discrètes. Comme il n’est pas pratique de mesurer l'ensemble d’une population lors d'une étude, les chercheurs utilisent des échantillons pour représenter la population. Un échantillon aléatoire est un groupe représentatif de la population choisi selon une méthode qui donne à chaque individu de cette population une chance égale d'être inclus dans l’échantillon. Parmi les méthodes d’échantillonnage aléatoire figurent l’échantillonnage aléatoire simple, l’échantillonnage stratifié, l’échantillonnage en grappes et l’échantillonnage systématique. L’échantillonnage par convenance est une méthode non aléatoire de sélection d'un échantillon qui produit souvent des données biaisées. Une fois les données collectées, elles peuvent être décrites et présentées sous de nombreux formats différents. Par exemple, supposons qu’une personne soit intéressée par l'achat d'une maison dans une zone particulière. Ne disposant pas de beaucoup d'informations sur les prix immobiliers, l'acheteur pourrait demander à un agent immobilier de lui fournir un échantillon représentatif des prix. Lire tous les prix de l’échantillon pourrait être un peu fastidieux. Une approche plus efficace consisterait à examiner le prix médian ainsi que la dispersion des prix. La médiane et la dispersion sont seulement deux exemples de méthodes permettant de décrire des données. L’agent pourrait également fournir un graphique des données, ce qui constituerait une façon plus pratique de comprendre les prix immobiliers. Le domaine des statistiques qui détaille les méthodes numériques et graphiques pour décrire et présenter les données d’un échantillon s’appelle les « statistiques descriptives ». Un graphique statistique est un outil permettant d'étudier la forme ou la distribution d’un échantillon ou d’une population. Un graphique peut constituer une manière plus efficace de présenter les données qu’une simple liste de chiffres, car il est facile d’observer les regroupements de données et d’identifier les positions où il y a peu de valeurs. Les journaux et internet utilisent les graphiques pour présenter les tendances et permettre aux lecteurs de comparer rapidement des faits et des chiffres. Parmi les types de graphiques utilisés pour résumer et organiser les données figurent le dot plot, le diagramme à barres, l’histogramme, le diagramme à tiges et à feuilles, le polygone de fréquence (un type de graphique en ligne brisée), le diagramme circulaire et le diagramme en boîte.

Révision et aperçu

Les données sont des éléments d’information individuels obtenus à partir d’une population ou d’un échantillon. Les données peuvent être classées comme qualitatives (catégorielles), quantitatives...

Education

Enseignement

JoVE Core

Statistics

Statistiques

Unit 1

Unité 1

Summarizing and Visualizing Data

Synthèse et visualisation de données

KEY TERMS AND CONCEPTS

TERMES ET CONCEPTS CLÉS

descriptive statistics and graphs

Descriptive statistics help summarize data with numbers and graphs. Data are individual pieces of information collected from a population or a sample. They can be qualitative, which means categorical, or quantitative, which includes continuous and discrete data.

In many studies, researchers do not measure every member of a population. Instead, they use a sample to represent the population. A random sample is a representative group chosen so that each individual has an equal chance of being selected. Random sampling methods include simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Convenience sampling is nonrandom and often leads to biased data.

After data are collected, they can be described in different ways. For example, when someone wants to buy a house in a certain area, a real estate agent might share a sample set of house prices. Looking at every price can be overwhelming, so the buyer may focus on the median price and the variation in the prices. The median and variation are two ways to describe the data.

Graphs also help organize and present sample data. A statistical graph shows the shape or distribution of data and can make patterns easier to see than a long list of numbers. Clusters and gaps in the data are often easier to notice on a graph. Newspapers and the Internet use graphs to show trends and help readers compare facts and figures quickly. Common graphs include the dot plot, bar graph, histogram, stem-and-leaf plot, frequency polygon, pie chart, and box plot.

Descriptive Statistics and Graphs

review and preview

Descriptive statistics help summarize data with numbers and graphs. Data are individual pieces of information collected from a population or a sample. They can be qualitative, which means categorical, or quantitative, which includes continuous and discrete data. In many studies, researchers do not measure every member of a population. Instead, they use a sample to represent the population. A random sample is a representative group chosen so that each individual has an equal chance of being...

what is a frequency distribution

Reading Frequency Tables and Histograms

Frequency distributions organize quantitative data by showing how often each value appears. A frequency is the number of times a data value occurs. When the frequency values are added together, the total equals the number of students in the sample. These distributions are useful for grouping numerical data into clear patterns. They can be shown in a table, histogram, line graph, dot plot, or pie chart. A histogram displays tabulated frequencies as adjacent rectangles over discrete intervals,...

construction of frequency distribution

Building a Frequency Table from Data

A frequency distribution table organizes data by showing how often each value appears. It starts with two columns: one for the data labels and one for frequency. A third column for tally marks can be added if needed. The next step is to look at the data set and decide whether an ungrouped frequency distribution table or a grouped frequency distribution table is more suitable. If the data includes a large set of different values, a grouped frequency distribution table is the better choice. This...

relative frequency distribution

Relative Frequency in Data Tables

Relative frequency distribution shows how often each value or class appears in a data set as a fraction of the whole. To find a relative frequency, divide each class frequency by the total number of data points in the sample. The result can be written as a fraction, decimal, or percent. This type of distribution is very similar to a regular frequency distribution. The same data are grouped in classes, and the same rules are used to build those classes. The main difference is in the way the...

percentage frequency distribution

Survey Data as Percentage Frequencies

Survey data can be shown as percentage frequencies to display how observations are spread across each data point or group. This format shows the share of the total for each response or category. It is a common way to present relative frequency in survey results and other data. Percentage frequency distributions are often displayed as bar graphs, pie charts, or tables. These displays make it easier to compare groups and see how much each category contributes to the whole. They are useful...

cumulative frequency distribution

Building a Cumulative Frequency Table

A cumulative frequency distribution shows how data build up across class intervals. Instead of counting only the values in one class, it counts the values in that class and in every class to its left. It is a useful way to track how data accumulate as the table progresses. A cumulative frequency is found by adding the frequency of each class lower than the class interval or category being studied. In other words, each new total includes the current class and the frequencies from the lower...

ogive graph

Cumulative Frequency Graph for Medians

An ogive graph is a cumulative frequency graph used in statistics. It is also called a cumulative frequency polygon. Instead of showing each class on its own, it adds the cumulative percentages from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries on the horizontal x-axis. It looks similar to a histogram, but it uses single points instead of rectangles. Each point appears where the top right corner of a histogram bar would be. This graph is...

histogram

Reading Data Patterns with Histograms

A histogram shows how data is distributed in a data set. It uses an x-y graph to display the pattern of the data. The x-axis is labeled with what the data represents, such as distance from home to school. The y-axis shows frequency, relative frequency, percent frequency, or probability. A histogram is made of adjoining bars. The height of each bar matches the frequency value for that class. Even with different labels, the graph keeps the same overall shape. Like a stemplot, a histogram can...

relative frequency histogram

Reading Relative Frequency in Histograms

A relative frequency histogram shows how data are spread across classes using percentages instead of counts. The relative frequency tells the proportion of data points in each value or class. The frequency tells the number of data points in each value or class. This graph has the same overall shape as a histogram. It uses a horizontal scale on the x-axis, and it marks the y-axis with relative frequencies, which are percentages of the whole. Each bar shows how many data values fall into a...

scatter plot

Reading Relationships in a Scatter Plot

A scatter plot shows the relationship between two variables, x and y. It is one of the easiest ways to display how the variables are connected. The graph can show the direction of the relationship. It can also show whether the points suggest a clear pattern or little pattern at all. A clear direction appears when high values of one variable go with high values of the other variable. It can also appear when low values of one variable go with low values of the other variable. Another pattern...

time-series graph

Reading Trends with Time-Series Graphs

A time-series graph shows repeated measurements taken at successive intervals of time. It is also called a time series chart. This type of line graph helps organize data that changes over time. To build a time-series graph, use both parts of a paired data set. Plot the time increments on the horizontal axis. Plot the measured values on the vertical axis. Each point then shows one time and one quantity, and the points are usually connected with straight lines in the order they occur. <br...

bar graph

Comparing Categories with Bar Charts

A bar graph, also called a bar chart, compares categories with separate bars. The bars may be horizontal or vertical. Each bar stands apart so the viewer can compare groups clearly.

multiple bar graph

Comparing Data with Multiple Bar Graphs

A multiple bar graph compares two or more sets of data with grouped bars. It works like a bar graph, but it uses several bars to show related values side by side. Each bar or column stands for one data value. This graph is useful when the data share the same unit of measurement. You can include many parameters, as long as they use that same unit. The categories are placed on the horizontal x-axis, and the frequency is shown on the vertical y-axis. Multiple bar graphs are used mainly to relate...

pareto chart

Pareto Chart for Ranking Data

A Pareto chart is a bar graph used to rank data by size or frequency. It can also combine bars with a line graph. In the chart, the bar lengths show the individual values or how often each category appears, and the line shows the cumulative total. The bars are arranged from longest on the left to shortest on the right. This order makes the data easier to read and interpret. A Pareto chart is also called a Pareto diagram or Pareto analysis. The chart is named after the Italian economist...

pie chart

Pie Chart Data and Percentages

A pie chart shows categorical data in a circular graph. It is divided into slices, and each slice shows a numerical proportion. This makes it easy to compare the relative size of parts in one chart. In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. That means larger values take up larger slices. Smaller values take up smaller slices. Pie charts are useful for real-world data such as marks...

key terms and concepts

Learn how descriptive statistics summarize data with numbers and graphs, including samples, random sampling, median, variation, and common graph types.

Descriptive Statistics and Graphs | JoVE K12

statistics

JoVE Core Statistiques introduit aux étudiants les concepts suivants et leurs applications: la collection, l'analyse, la présentation, et l' interprétation des données. En parallèle, les scientifiques à l'action démontrent l’application de ces concepts dans des expériences de recherche classiques et originales réalisées dans des laboratoires du monde entier.

ES.1.e

Life Science

LS.1

LS.1.c

LS.1.e

Physical Science

PS.1

PS.1.c

Physics

PH.1

PH.1.c

PH.1.e

Texas Essential Knowledge and Skills

High School

Specialised Topic in Science

112.51.c.1.

112.51.c.1.F.

Biology

BIO.1

BIO.1.E

PHYS.1

PHYS.1.F

PHYS.2

PHYS.2.B

Chemistry and Physics

IPC.1

IPC.1.F

IPC.2

IPC.2.B

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The student will demonstrate an understanding of scientific and engineering practices by asking questions and defining problems
• ask questions that arise from careful observation of phenomena, examination of a model or theory, or unexpected results, and/or to seek additional information
• determine which questions can be investigated within the scope of the school laboratory or field experience
• generate hypotheses based on research and scientific principles
• make hypotheses that specify what happens to a dependent variable when an independent variable is manipulated
• define design problems that involve the development of a process or system with multiple components and criteria

ES.1.a

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The student will demonstrate an understanding of scientific and engineering practices by planning and carrying out investigations
• individually and collaboratively plan and conduct observational and experimental investigations
• plan and conduct investigations to test design solutions in a safe and ethical manner including considerations of environmental, social and personal effects
• select and use appropriate tools and technology to collect, record, analyze, and evaluate data

ES.1.b

Introduction to Test of Independence

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Statistical Analysis: Overview

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Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful ...

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Data: Types and Distribution

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• construct, analyze, and interpret graphical displays of data and consider limitations of data analysis
• apply mathematical concepts and processes to scientific questions
• use data in building and revising models, supporting explanations of phenomena, or testing solutions to problems
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Data Collection by Experiments

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Reliability and Validity

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Reasoning

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The student will demonstrate an understanding of scientific and engineering practices by constructing and critiquing conclusions and explanations
• make quantitative and/or qualitative claims based on data
• construct and revise explanations based on valid and reliable evidence obtained from a variety of sources, including students' own investigations, models, theories, simulations, and peer review
• apply scientific ideas, principles, and/or evidence to provide an explanation of phenomena or design solutions
• construct arguments or counterarguments based on data and evidence
• differentiate between a scientific hypothesis, theory, and law

ES.1.d

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The student will demonstrate an understanding of scientific and engineering practices by developing and using models
• evaluate the merits and limitations of models
• develop, revise, and/or use models based on evidence to illustrate or predict relationships
• construct and interpret scales, diagrams, classification charts, graphs, tables, imagery, models, including geologic cross sections and topographic profiles
• read and interpret topographic and basic geologic maps and globes, including location by latitude and longitude

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• gather, read, and evaluate scientific and/or technical information from multiple sources, assessing the evidence and credibility of each source
• communicate scientific and/or technical information about phenomena and/or a design process in multiple formats

ES.1.f

Descriptive Statistics and Graphs

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