Kerk Yu Hang - 22/23S2-2447DIF

Wong Ke Xin - 22/23S2-2514DIF

Lim Mai Sie - 23/24S2 -2678DBM

Fiona Choo Yan Rou- 23/24S2-2676DBM

1. Discrete Data:

• Countable: Discrete data is made up of individual values that you can count, like how many students are in a class or how many cookies you ate today.

• No In-Between Values: Discrete data doesn’t have in-between values. For example, you can have 3 apples or 4 apples, but not 3.5 apples.

• Examples: the number of student , Shoe Size

2. Continuous Data:

   • Measurable: Continuous data is something you measure with tools like a ruler, scale, or clock, such as height, weight, or time.

   • Infinite Possible Values: Continuous data can take any value within a range, like being 1.75 meters tall or even 1.751 meters tall.

   • Examples: Height, weight, temperature, time

     
     

   Summary: Discrete data is countable, made up of distinct values with no in-between possibilities, while continuous data is measurable, can take any value within a range, and often includes fractions and decimals.

Continuous data can take on any value within a range and is measured (for example:height).

Discrete data is graphed with bar charts, and continuous data is with histograms or line graphs.

Group 6

Yong ziyi

Sze Han

Lexin

Huihui

-Data is countable: Values are distinct and separate, often represented by whole numbers.

-Finite or countable infinite values: There's a limit to the number of possible values within a given range.

-Examples:

* Number of items in a basket

* Number of cars in a parking lot

* Number of customers in a store

Continuous World Applications

-Data is measurable: Values can take on any value within a specified range, including fractions and decimals.

-Infinitely many values: There's no limit to the number of possible values within a range.

-Examples:

* Weight

* Time

* Distance

* Height

EILEEN WONG XIN LIN

CHONG YU JIE

ELLYSE NG KAI YING

Another is the precision in real life discrete data is less precise whilst continuous is more finer granularity and precise measurement

Tavis Yeap

Lee Mei Qi

Huishan

LIM XIN YAN

KOK ERN QI

LIM WIN NEE

HAW YEE KENG

1.Discrete data: - Value characteristics: only specific, separated values ​​can be taken. such as integer or categorical variables

- Examples: Number of students, exam results

2. Continuous data: - Value characteristics: There can be unlimited intermediate values ​​between these values, such as measurement data.

- Example: height, time

Conclusion:

Discrete data provides clear, countable insights, while continuous data offers detailed, measurable information. Together, they enable businesses to gain a comprehensive understanding of their customers, make well-informed decisions, and continually refine their marketing strategies and product offerings.

Law Yi Kuan23/24S3-2734DBM

Tan Xuan Yu23/24S3-2736DBM

Chew Sin Lu23/24S3-2730DBM

Chong Shi Hui 23/24S3-2731DBM

1.Discrete data refers to countable, individualized, and nondivisible figures in statistics. These data points exist only in set increments. Data analysts and statisticians visualize discrete data using bar graphs, line charts, histograms, and pie charts.

2.Continuous data is a type of numerical data that refers to the unspecified number of possible measurements between two realistic points

Discrete data

• Countable

• Limited number of possible values

• Often visualized using bar charts or pie chart

• Probability mass function

Continuous Data

• Measurable

• Values can vary smoothly across a range

• Typically visualized using line graph or histogram

• Probability density function

Aw Chin Xuan

Joyce Tay Yi Yang

Priya Ramis

Rayson Ng

Continuous data is considered the complete opposite of discrete data. It’s the type of numerical data that refers to the unspecified number of possible measurements between two presumed points.

The numbers of continuous data are not always clean and integers, as they are usually collected from very precise measurements. Measuring a particular subject is allowing for creating a defined range to collect more data.

Variables in continuous data sets often carry decimal points, with the number stretching out as far as possible. Typically, it changes over time. It can have completely different values at different time intervals, which might not always be whole numbers.

Continuous data can be measured by using specific tools and displayed in line graphs, skews, histograms.