What is a trend line on a scatter plot?
What is an outlier?
Great read:
Outliers by Malcolm Gladwell
we cant help you if u don't give the options and what you think the answers are
A trend line on a scatter plot is a straight line that represents the general pattern or trend in the relationship between two variables. It is used to visually summarize the data points in a scatter plot and find the overall direction or tendency of the data.
To create a trend line, you can follow these steps:
1. Start by creating a scatter plot with the two variables on the x and y axes.
2. Plot all the data points on the graph.
3. Analyze the distribution of the points. If there appears to be a linear relationship between the variables, it may be appropriate to add a trend line.
4. Fit a line to the data points that best represents the general trend. There are different methods to obtain the best-fit line, such as the least squares method.
5. Draw the trend line on the scatter plot, ensuring that it passes as close as possible to most of the data points.
6. Interpret the trend line by looking at its slope and direction. A positive slope indicates a positive linear relationship, where the dependent variable increases as the independent variable increases. Likewise, a negative slope indicates a negative linear relationship.
An outlier, on the other hand, is a data point that significantly deviates from the overall pattern or trend of the data. It is an observation that lies far away from the rest of the data points and may indicate a rare or unusual occurrence. Outliers can have a notable impact on the analysis and interpretation of the data.
To identify an outlier, you can use certain methods, including:
1. Visual inspection: Plot the data points on a scatter plot or a box plot and look for any points that fall noticeably away from the majority of the data points.
2. Calculation: Use statistical techniques, such as the z-score or the interquartile range (IQR), to calculate how far a data point deviates from the mean or the median, respectively. If a data point's z-score or position outside the IQR is beyond a certain threshold (often defined as 1.5 or 3), it is considered an outlier.
However, it is important to note that outliers can be the result of genuine observations or data errors. Therefore, it is necessary to carefully evaluate and verify the outlier before deciding how to handle it in your analysis or modeling.