**Buried Change**: Drilling down beneath a lake in Alaska yields chemical evidence of past changes in climate. Biological silicon, left by the skeletons of single-celled creatures called diatoms, is a measure of the abundance of life in the lake. A variable based on the ratio of certain isotopes gives an indirect measure of moisture, mostly from snow. As we drill down, we look further into the past. Here is a scatterplot of data from 2300 to 12,000 years ago. Describe the effect the point in the upper-right corner has on the equation of the least-squares regression line.**Scatterplot Description**: - **Axes**: - The vertical axis is labeled "Silicon" with values ranging from 0 to 350. - The horizontal axis is labeled "Isotope" with values ranging from approximately -21.8 to -19.4.- **Data Points**: - The scatterplot shows several purple data points scattered across the graph. - One point is prominently located in the upper-right corner.- **Regression Line**: - A red line represents the least-squares regression line. - The line has a slightly downward slope, indicating a negative relationship between isotope ratios and silicon values. The presence of the point in the upper-right corner likely affects the slope and position of the regression line, potentially skewing the data analysis by pulling the line upwards and decreasing the strength of the negative correlation.

Given that,There are two variables: silicon and isotopes.The scatter plot shows the relationship between these variables over a span of 2300 to 12000 years ago.The scatter plot shows the data from a lake in Alaska that helps us understand past climate changes.The point in the upper-right corner of the plot is an outlier, which means it is different from the other points.This outlier has a big impact on the equation of the line that represents the trend in the data.The points in the upper-right corner represent an extreme value, which has considerable influence on the slope and intercept of the regression line.The outlier affects the slope of the regression line, potentially causing it to be steeper or flatter depending on the position of the outlier relative to the other data points.Since in this case, the outlier is very far from the remaining observations, it pulls the regression line closer to itself altering the overall slope.The outlier also influences the intercept of the regression line, shifting its position along the vertical axis.Basically, the outlier changes the overall pattern that the line represents.The point in the upper right corner is very far from the remaining observations. In this case, it reduces the steepness of the slope and increases the value of the y-intercept.Thus, the point makes the slope less steep and increases the y-intercept.