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c. Identify all outliers using the boxplot. Write down the values and indicate which type they apply to. d. Which vehicle type has the lowest variability in acceleration time?

c. Identify all outliers using the boxplot. Write down the values and indicate which type they apply to. d. Which vehicle type has the lowest variability in acceleration time?

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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I have provided the box plot below, please only answer the highlighted questions
### Box Plot Analysis of Vehicle Types This box plot visually represents the distribution of a specific variable (not specified) across six different vehicle types: Minivan, Hatchback, Wagon, Sporty, Sedan, and SUV. Each box plot consists of a box and two whiskers, illustrating the range and distribution of the data for each category. #### Components of the Box Plot: - **Box:** Represents the interquartile range (IQR), which contains the middle 50% of the data. The edges of the box are the first quartile (Q1) and the third quartile (Q3). - **Whiskers:** Extend from the edges of the box to the smallest and largest values within 1.5*IQR from the quartiles. - **Median Line:** A line inside the box denotes the median (Q2) of the data. - **Outliers:** Points beyond the whiskers which are considered outliers. #### Detailed Breakdown by Vehicle Type: 1. **Minivan:** - **Median:** Slightly above 7.0 - **IQR:** Narrow, signifying a small spread in the data. - **Range:** Approximately between 7.0 and 7.5, indicating minimal variability. 2. **Hatchback:** - **Median:** Around 8.0 - **IQR:** Data is spread from approximately 7.0 to 8.5. - **Range:** Extends from about 6.0 to 9.5, showing a moderate spread. 3. **Wagon:** - **Median:** Close to 8.0 - **IQR:** Narrower, spanning roughly from 7.0 to 8.0. - **Range:** Falls between about 6.5 and 8.5. 4. **Sporty:** - **Median:** Approximately 8.0 - **IQR:** Spreads from roughly 6.8 to 8.2. - **Range:** Stretches from about 6.3 to 8.7, indicating some variability. 5. **Sedan:** - **Median:** Significantly higher at around 9.5 - **IQR:** Broad, from near 8.0 to 10.0, suggesting considerable spread. - **Range:** From approximately 7
Transcribed Image Text:### Box Plot Analysis of Vehicle Types This box plot visually represents the distribution of a specific variable (not specified) across six different vehicle types: Minivan, Hatchback, Wagon, Sporty, Sedan, and SUV. Each box plot consists of a box and two whiskers, illustrating the range and distribution of the data for each category. #### Components of the Box Plot: - **Box:** Represents the interquartile range (IQR), which contains the middle 50% of the data. The edges of the box are the first quartile (Q1) and the third quartile (Q3). - **Whiskers:** Extend from the edges of the box to the smallest and largest values within 1.5*IQR from the quartiles. - **Median Line:** A line inside the box denotes the median (Q2) of the data. - **Outliers:** Points beyond the whiskers which are considered outliers. #### Detailed Breakdown by Vehicle Type: 1. **Minivan:** - **Median:** Slightly above 7.0 - **IQR:** Narrow, signifying a small spread in the data. - **Range:** Approximately between 7.0 and 7.5, indicating minimal variability. 2. **Hatchback:** - **Median:** Around 8.0 - **IQR:** Data is spread from approximately 7.0 to 8.5. - **Range:** Extends from about 6.0 to 9.5, showing a moderate spread. 3. **Wagon:** - **Median:** Close to 8.0 - **IQR:** Narrower, spanning roughly from 7.0 to 8.0. - **Range:** Falls between about 6.5 and 8.5. 4. **Sporty:** - **Median:** Approximately 8.0 - **IQR:** Spreads from roughly 6.8 to 8.2. - **Range:** Stretches from about 6.3 to 8.7, indicating some variability. 5. **Sedan:** - **Median:** Significantly higher at around 9.5 - **IQR:** Broad, from near 8.0 to 10.0, suggesting considerable spread. - **Range:** From approximately 7
### Vehicle Acceleration by Type: Educational Exercise #### Introduction This exercise uses the dataset "Cars2020," which contains detailed information about new car models in the year 2020. The goal is to analyze vehicle acceleration by car type using statistical methods. #### Instructions 1. **Data Preparation**: - Upload the "Cars2020" dataset. - Use the Statkey software to generate side-by-side boxplots comparing acceleration times to 60 mph (variable: Acc60) across different vehicle types (variable: Type). 2. **Tasks**: - Include a screenshot of the generated boxplots in your report. - These variables (Acc60 and Type) are not in the dropdown menu in Statkey, so you'll need to manually input them. 3. **Analysis Questions**: a. **Association Analysis**: - Does there appear to be an association between car type and acceleration time? - Provide a description of how the generated boxplots support your answer. b. **Acceleration Extremes**: - Identify the type of car with the highest acceleration time. - Identify the type of car with the lowest acceleration time. c. **Outlier Identification**: - Using the boxplot, identify all outliers. - Document the values of these outliers and indicate the car type they correspond to. d. **Variability Analysis**: - Determine which vehicle type has the lowest variability in acceleration time. #### Explanation of Boxplots 1. **Boxplot Components**: - **Median**: Line inside the box showing the middle value of the data. - **Quartiles**: The box itself, representing the interquartile range (IQR) which contains the middle 50% of the data. - **Whiskers**: Lines extending from the box to the smallest and largest values within 1.5 * IQR from the quartiles. - **Outliers**: Points beyond the whiskers, representing data points significantly different from the others. 2. **Interpreting Side-by-Side Boxplots**: - Use side-by-side boxplots to easily compare distributions across different car types. - Look at the spread, center (median), and outliers to make inferences about group similarities and differences. #### Example - **Screenshot of Boxplots** (Include your screenshot here) - **Analysis**: - Describe the associations
Transcribed Image Text:### Vehicle Acceleration by Type: Educational Exercise #### Introduction This exercise uses the dataset "Cars2020," which contains detailed information about new car models in the year 2020. The goal is to analyze vehicle acceleration by car type using statistical methods. #### Instructions 1. **Data Preparation**: - Upload the "Cars2020" dataset. - Use the Statkey software to generate side-by-side boxplots comparing acceleration times to 60 mph (variable: Acc60) across different vehicle types (variable: Type). 2. **Tasks**: - Include a screenshot of the generated boxplots in your report. - These variables (Acc60 and Type) are not in the dropdown menu in Statkey, so you'll need to manually input them. 3. **Analysis Questions**: a. **Association Analysis**: - Does there appear to be an association between car type and acceleration time? - Provide a description of how the generated boxplots support your answer. b. **Acceleration Extremes**: - Identify the type of car with the highest acceleration time. - Identify the type of car with the lowest acceleration time. c. **Outlier Identification**: - Using the boxplot, identify all outliers. - Document the values of these outliers and indicate the car type they correspond to. d. **Variability Analysis**: - Determine which vehicle type has the lowest variability in acceleration time. #### Explanation of Boxplots 1. **Boxplot Components**: - **Median**: Line inside the box showing the middle value of the data. - **Quartiles**: The box itself, representing the interquartile range (IQR) which contains the middle 50% of the data. - **Whiskers**: Lines extending from the box to the smallest and largest values within 1.5 * IQR from the quartiles. - **Outliers**: Points beyond the whiskers, representing data points significantly different from the others. 2. **Interpreting Side-by-Side Boxplots**: - Use side-by-side boxplots to easily compare distributions across different car types. - Look at the spread, center (median), and outliers to make inferences about group similarities and differences. #### Example - **Screenshot of Boxplots** (Include your screenshot here) - **Analysis**: - Describe the associations
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