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group each variable as categorical nominal/ ordinal numerical discrete/continuous

group each variable as categorical nominal/ ordinal numerical discrete/continuous

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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**Table: Student Data Overview** | Year | Gender | Award | HigherSAT | Height | Weight | Siblings | BirthOrder | VerbalSAT | MathSAT | SAT | GPA | Piercings | |-------------|--------|---------|-----------|--------|--------|----------|------------|-----------|---------|------|-----|-----------| | Sophomore | M | Nobel | Verbal | 71 | 175 | 1 | 1 | 630 | 620 | 1250 | 3.05| 0 | | Sophomore | M | Olympic | Math | 70 | 183 | 3 | 2 | 470 | 500 | 970 | 2.20| 4 | | Sophomore | F | Nobel | Verbal | 65 | 140 | 2 | 3 | 550 | 540 | 1090 | 3.30| 4 | | Sophomore | F | Nobel | Verbal | 67 | 140 | 1 | 1 | 690 | 660 | 1350 | 3.45| 6 | **Explanation:** This table presents data on four students, all sophomores, with the following details: - **Gender:** Two males ("M") and two females ("F"). - **Awards:** Two have received the Nobel award, while one has received an Olympic award. - **HigherSAT:** Indicates whether the student's verbal or math SAT score is higher. - **Height and Weight:** Provided in inches and pounds, respectively. - **Siblings and BirthOrder:** Number of siblings and the order of birth among siblings. - **VerbalSAT and MathSAT:** Scores in the verbal and math sections of the SAT. - **SAT:** Total SAT score (VerbalSAT + MathSAT). - **GPA:** Grade Point Average on a 4.0 scale. - **Piercings:** Number of piercings each student has. This data could be part of a larger study analyzing academic performance, extracurricular recognition, and personal characteristics.
Transcribed Image Text:**Table: Student Data Overview** | Year | Gender | Award | HigherSAT | Height | Weight | Siblings | BirthOrder | VerbalSAT | MathSAT | SAT | GPA | Piercings | |-------------|--------|---------|-----------|--------|--------|----------|------------|-----------|---------|------|-----|-----------| | Sophomore | M | Nobel | Verbal | 71 | 175 | 1 | 1 | 630 | 620 | 1250 | 3.05| 0 | | Sophomore | M | Olympic | Math | 70 | 183 | 3 | 2 | 470 | 500 | 970 | 2.20| 4 | | Sophomore | F | Nobel | Verbal | 65 | 140 | 2 | 3 | 550 | 540 | 1090 | 3.30| 4 | | Sophomore | F | Nobel | Verbal | 67 | 140 | 1 | 1 | 690 | 660 | 1350 | 3.45| 6 | **Explanation:** This table presents data on four students, all sophomores, with the following details: - **Gender:** Two males ("M") and two females ("F"). - **Awards:** Two have received the Nobel award, while one has received an Olympic award. - **HigherSAT:** Indicates whether the student's verbal or math SAT score is higher. - **Height and Weight:** Provided in inches and pounds, respectively. - **Siblings and BirthOrder:** Number of siblings and the order of birth among siblings. - **VerbalSAT and MathSAT:** Scores in the verbal and math sections of the SAT. - **SAT:** Total SAT score (VerbalSAT + MathSAT). - **GPA:** Grade Point Average on a 4.0 scale. - **Piercings:** Number of piercings each student has. This data could be part of a larger study analyzing academic performance, extracurricular recognition, and personal characteristics.
**Grouping Variables in Data Analysis** In data analysis, it's important to categorize each variable appropriately. Variables can be categorized as follows: 1. **Categorical Variables**: - **Nominal**: These are categories without any intrinsic ordering. Examples include colors, types of fruit, or names of cities. - **Ordinal**: These categories have a meaningful order but the intervals between them are not necessarily equal. Examples include rankings, education levels, or satisfaction ratings. 2. **Numerical Variables**: - **Discrete**: These are countable values, often integers. Examples include the number of students in a classroom, the number of cars in a parking lot, or the number of pets owned. - **Continuous**: These variables can take on any value within a range. Examples include height, weight, and temperature. Understanding these distinctions helps in selecting the correct statistical tests and visualizations for data analysis.
Transcribed Image Text:**Grouping Variables in Data Analysis** In data analysis, it's important to categorize each variable appropriately. Variables can be categorized as follows: 1. **Categorical Variables**: - **Nominal**: These are categories without any intrinsic ordering. Examples include colors, types of fruit, or names of cities. - **Ordinal**: These categories have a meaningful order but the intervals between them are not necessarily equal. Examples include rankings, education levels, or satisfaction ratings. 2. **Numerical Variables**: - **Discrete**: These are countable values, often integers. Examples include the number of students in a classroom, the number of cars in a parking lot, or the number of pets owned. - **Continuous**: These variables can take on any value within a range. Examples include height, weight, and temperature. Understanding these distinctions helps in selecting the correct statistical tests and visualizations for data analysis.
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