**Title: Understanding Correlations in Scatter Plots****Question:**In the image below, which figure(s) show no distinct correlation?**Diagrams:**1. **Top Scatter Plot:** - Description: This scatter plot shows data points that form a clear upward linear pattern from the bottom-left to the top-right, indicating a positive correlation. The data points are closely clustered around an imaginary line that slopes upward, suggesting that as one variable increases, the other variable also tends to increase.2. **Bottom Scatter Plot:** - Description: This scatter plot displays data points that appear randomly scattered without forming a clear pattern or line, indicating little to no correlation. While there are some clusters, they don't follow a consistent trend, suggesting no distinct relationship between the variables.**Conclusion:**Based on the scatter plots above, the second (bottom) figure shows no distinct correlation, as it lacks a clear upward or downward trend among the data points. ### Transcription and Analysis of Scatter PlotsThis section features two scatter plots aimed at illustrating different types of data relationships.#### Scatter Plot Analysis1. **Top Scatter Plot:** - **Description:** The data points in this scatter plot form a pattern that appears to have a downward trend. This suggests a negative correlation between the two variables being compared. As one variable increases, the other tends to decrease. 2. **Bottom Scatter Plot:** - **Description:** The data points in this plot are more dispersed and appear random without any discernible pattern or trend. This indicates no clear correlation between the two variables.These plots are typically used in statistical analysis to visually demonstrate correlations, if any, between datasets. A clear pattern suggests a possible correlation, while randomness indicates an absence of correlation. This visualization technique is invaluable for quickly identifying relationships in data for further statistical analysis.

Correlation is the measure of strength of linear relationship between two variables , e.g. X and Y ,…

Answered: In the image below, which figure(s)…

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In the image below, which figure(s) show no distinct correlation?

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

### Transcription and Analysis of Scatter Plots

This section features two scatter plots aimed at illustrating different types of data relationships.

#### Scatter Plot Analysis

1. **Top Scatter Plot:**
- **Description:** The data points in this scatter plot form a pattern that appears to have a downward trend. This suggests a negative correlation between the two variables being compared. As one variable increases, the other tends to decrease.

2. **Bottom Scatter Plot:**
- **Description:** The data points in this plot are more dispersed and appear random without any discernible pattern or trend. This indicates no clear correlation between the two variables.

These plots are typically used in statistical analysis to visually demonstrate correlations, if any, between datasets. A clear pattern suggests a possible correlation, while randomness indicates an absence of correlation. This visualization technique is invaluable for quickly identifying relationships in data for further statistical analysis.

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