Eriksson (2012) sent two abstracts of scientific papers to 200 people with postgraduate degrees. For each participant, one of the abstracts was randomly chosen and had a meaningless sentence inserted describing an unrelated mathematical model, while the other had no mathematical addition. The sentence had no conceptual connection to the subject matter of the abstract; it was just meaningless mathematics in that context. The participants were asked to rate the quality of the research in each abstract on a scale between 1 and 100, and the differences between their scores of their two abstracts, score of the abstract with math minus score of abstract without math, were recorded. Participants were also asked for the subject matter of their postgraduate degree: math, science, technology (MST); medicine (M); humanities, social science (HS); or Other (O). ### Box Plot Analysis for Different Degree SubjectsThe image presents a box plot and accompanying data table that compare score differences across four degree subjects—MST, M, HS, and O.#### Box Plot Description- **Axes:** - The y-axis represents "Score difference". - The x-axis is divided into four categories corresponding to the degree subjects: MST, M, HS, and O.- **Box Elements:** - **Median Line:** Horizontal line within each box, indicating the median score difference. - **Interquartile Range (IQR):** The length of the box shows the range between the 25th percentile (lower quartile) and the 75th percentile (upper quartile). - **Whiskers:** Vertical lines extending from the boxes, indicating variability outside the upper and lower quartiles. - **Outliers:** Individual points beyond the whiskers, representing score differences outside the typical range.#### Data Table SummaryThe table provides detailed statistics for each degree subject:| Degree Subject | Mean Score Difference | SD Score Difference | $ n $ ||----------------|-----------------------|---------------------|--------|| MST | -1.28 | 19.24 | 69 || M | 3.06 | 15.99 | 16 || HS | 6.60 | 21.15 | 84 || O | 13.90 | 23.31 | 31 |- **Mean Score Difference:** The average score difference for each group.- **SD Score Difference:** The standard deviation of the score differences, indicating the spread or variability.- **$ n $:** Number of observations in each group.#### Analysis Task(a) Examine the graph and assess visually how well the data likely fit the assumptions of ANOVA.This exercise involves evaluating the distribution, variability, and outliers in the data to consider the potential effectiveness of an ANOVA analysis.

Name: Eriksson (2012) sent two abstracts of scientific papers to 200 people
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Description: Eriksson (2012) sent two abstracts of scientific papers to 200 people with postgraduate degrees. For each participant, one of the abstracts was randomly chosen and had a meaningless sentence inserted describing an unrelated mathematical model, while

Comment on the assumptions of ANOVA: The Boxplot for the score difference of the groups MST, M, HS and O. Assumptions of ANOVA: Each sample should be selected from a normally distributed population. Each sample must be independent of the other. The variance of data in the different groups must be the same. The dependent variable should be continuous.Explanation: From the graph it is seen that, except the sample M, remaining all 3 samples have outliers. The distribution of first 3 samples seems to be roughly symmetric. The spread of the data of all the 4 samples roughly seems to be same. Since, ANOVA is robust, the data seems to be likely to fit the assumptions of ANOVA.

Answered: Eriksson (2012) sent two abstracts of…

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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Eriksson (2012) sent two abstracts of scientific papers to 200 people with postgraduate degrees. For each participant, one of the abstracts was randomly chosen and had a meaningless sentence inserted describing an unrelated mathematical model, while the other had no mathematical addition. The sentence had no conceptual connection to the subject matter of the abstract; it was just meaningless mathematics in that context. The participants were asked to rate the quality of the research in each abstract on a scale between 1 and 100, and the differences between their scores of their two abstracts, score of the abstract with math minus score of abstract without math, were recorded. Participants were also asked for the subject matter of their postgraduate degree: math, science, technology (MST); medicine (M); humanities, social science (HS); or Other (O).

### Box Plot Analysis for Different Degree Subjects

The image presents a box plot and accompanying data table that compare score differences across four degree subjects—MST, M, HS, and O.

#### Box Plot Description

- **Axes:**
- The y-axis represents "Score difference".
- The x-axis is divided into four categories corresponding to the degree subjects: MST, M, HS, and O.

- **Box Elements:**
- **Median Line:** Horizontal line within each box, indicating the median score difference.
- **Interquartile Range (IQR):** The length of the box shows the range between the 25th percentile (lower quartile) and the 75th percentile (upper quartile).
- **Whiskers:** Vertical lines extending from the boxes, indicating variability outside the upper and lower quartiles.
- **Outliers:** Individual points beyond the whiskers, representing score differences outside the typical range.

#### Data Table Summary

The table provides detailed statistics for each degree subject:

| Degree Subject | Mean Score Difference | SD Score Difference | $ n $ |
|----------------|-----------------------|---------------------|--------|
| MST | -1.28 | 19.24 | 69 |
| M | 3.06 | 15.99 | 16 |
| HS | 6.60 | 21.15 | 84 |
| O | 13.90 | 23.31 | 31 |

- **Mean Score Difference:** The average score difference for each group.
- **SD Score Difference:** The standard deviation of the score differences, indicating the spread or variability.
- **$ n $:** Number of observations in each group.

#### Analysis Task

(a) Examine the graph and assess visually how well the data likely fit the assumptions of ANOVA.

This exercise involves evaluating the distribution, variability, and outliers in the data to consider the potential effectiveness of an ANOVA analysis.

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