Data on its own is just a list of numbers. The real power comes when you present it visually so that patterns, trends and comparisons leap off the page. This topic covers every standard chart type you will meet at IGCSE, from simple bar charts and pictograms to scatter diagrams and stem-and-leaf plots.
You will learn how to choose the right diagram for a data set, construct each one accurately with correct labels and scales, and interpret what the diagram reveals. At Higher tier, histograms with unequal class widths and cumulative frequency curves extend your toolkit for analysing continuous data in more depth.
A ti ṣe àwòrán kókó yìí kí o lè rí bí àwọn èrò ṣe so pọ̀.
Ṣí àwòrán ọpọlọ nínú áàpù
Ìdánrawò kíákíá lórí ohun tí wọ́n ń dán wò nínú kókó yìí.
Ṣe ìdánrawò káàdì wọ̀nyí nínú áàpù
Gba ohun èlò Green Bridge CBT sórí fóònù tàbí kọ̀mpútà rẹ fún gbogbo ilé ìkàwé IGCSE: ìwé ìdánwò àtijọ́, ìlànà ìdíwọ̀n, àwòrán èrò, káàdì ìkẹ́kọ̀ọ́ àti ẹ̀kọ́ ohùn.
Oriire fun ipari ẹkọ lori Graphical Representation Of Data. Ni bayi ti o ti ṣawari naa awọn imọran bọtini ati awọn imọran, o to akoko lati fi imọ rẹ si idanwo. Ẹka yii nfunni ni ọpọlọpọ awọn adaṣe awọn ibeere ti a ṣe lati fun oye rẹ lokun ati ṣe iranlọwọ fun ọ lati ṣe iwọn oye ohun elo naa.
Iwọ yoo pade adalu awọn iru ibeere, pẹlu awọn ibeere olumulo pupọ, awọn ibeere idahun kukuru, ati awọn ibeere iwe kikọ. Gbogbo ibeere kọọkan ni a ṣe pẹlu iṣaro lati ṣe ayẹwo awọn ẹya oriṣiriṣi ti imọ rẹ ati awọn ogbon ironu pataki.
Lo ise abala yii gege bi anfaani lati mu oye re lori koko-ọrọ naa lagbara ati lati ṣe idanimọ eyikeyi agbegbe ti o le nilo afikun ikẹkọ. Maṣe jẹ ki awọn italaya eyikeyi ti o ba pade da ọ lójú; dipo, wo wọn gẹgẹ bi awọn anfaani fun idagbasoke ati ilọsiwaju.
Ṣiṣẹ́ lórí àwọn ìbéèrè wọ̀nyí nínú ohun èlò náà
Ṣiṣẹ́ lórí àwọn ìbéèrè wọ̀nyí nínú ohun èlò náà
For most of the work you do in this book, you will use a histogram to display the data. One advantage of a histogram is that it can readily display large data sets. A rule of thumb is to use a histogram when the data set consists of 100 values or more. A histogram consists of contiguous (adjoining)
"Introductory Statistics: Histograms, Frequency Polygons, and Time Series Graphs" by OpenStax, OpenStax, Rice University, licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Source: https://openstax.org/books/introductory-statistics/pages/2-2-histograms-frequency-polygons-and-time-series-graphs. Access for free at https://openstax.org/books/introductory-statistics/pages/1-introduction
Once you have a set of data, you will need to organize it so that you can analyze how frequently each datum occurs in the set. However, when calculating the frequency, you may need to round your answers so that they are as precise as possible. A simple way to round off answers is to carry your final
"Introductory Statistics: Frequency, Frequency Tables, and Levels of Measurement" by OpenStax, OpenStax, Rice University, licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Source: https://openstax.org/books/introductory-statistics/pages/1-3-frequency-frequency-tables-and-levels-of-measurement. Access for free at https://openstax.org/books/introductory-statistics/pages/1-introduction
Àwọn àjọ mìíràn ló tẹ̀ wọ́n jáde lábẹ́ ìwé àṣẹ ṣíṣí, a sì dá wọn lọ́lá bí wọ́n ṣe béèrè. Àwọn ìjápọ̀ ń ṣí ojú òpó wọn.
Ṣe o fẹ ṣe adaṣe awọn ibeere idanwo adaṣe nipa Graphical Representation Of Data? Ṣe igbasilẹ ohun elo Green Bridge CBT lati wọle si awọn ibeere idanwo adaṣe ati awọn ayẹwo adaṣe kikun fun koko-ọrọ yii.
Gbogbo ohun ti o nilo lati ṣe dara julọ ninu JAMB, WAEC ati NECO.