Bar graphs are used to display the frequency of categorical data, making it easy to compare different groups. This visual tool helps in quickly assessing which categories are more prevalent.

The median is the middle value of a dataset when arranged in order. It is less affected by extreme scores compared to the mean, making it a robust indicator of central tendency.

Line graphs illustrate trends over time by plotting data points sequentially. They are particularly useful for showing changes and patterns in data across intervals.

The mean is calculated by summing all the values in a dataset and dividing by the number of values. It represents the average and gives a sense of overall central tendency.

Pie charts are designed to display how different parts make up a whole by using proportional slices. They visually show the percentage breakdown of categories within the dataset.

In a histogram, the height of each bar indicates the frequency of data points that fall within a specific interval. This helps in understanding the distribution of the dataset.

A positive correlation indicates that as one variable increases, the other variable tends to increase as well. This supports the conclusion that more study time is linked with higher test scores.

A mean that is significantly higher than the median typically suggests that the distribution is skewed to the right. This happens when a few extreme high values pull the mean upward.

An outlier is a data point that deviates markedly from the rest of the dataset. It can indicate variability, error, or interesting exceptions in the pattern.

When data points cluster tightly around a line in a scatter plot, it suggests a strong correlation between the variables. This means that changes in one variable tend to be reliably associated with changes in the other.

The mean is sensitive to extreme values because it takes every data point into account. Outliers can cause the mean to shift significantly compared to more resistant measures like the median.

The data shows that students who study 3 or more hours tend to achieve higher scores, indicating a positive association. However, it does not prove causation or guarantee outcomes for all students.

Box-and-whisker plots effectively summarize the distribution, median, quartiles, and outliers of datasets. They facilitate easy comparisons between different groups.

Since 60% of students indicate a preference for online classes, it shows a majority favoring this mode. However, the data does not provide information about the reasons behind this preference.

A positive correlation means that as one variable increases, the other also increases. In this case, higher temperatures are associated with increased ice cream sales.

The weak positive correlation, along with the presence of outliers, suggests only a slight association between the number of books read and grades. It indicates that while there may be a relationship, other variables are likely influencing academic performance.

Removing the top 5% indicates that these values were significantly different from the rest, acting as outliers. Their removal results in a mean that more accurately reflects the central tendency of the main dataset.

A correlation coefficient of -0.45 indicates a moderate negative correlation. This suggests that as social media use increases, average grades tend to decrease, though it does not establish causation.

The median is a robust measure that is less affected by outliers compared to the mean. This makes it a preferable choice when data contains extreme values.

A histogram displays the frequency distribution of continuous data, making it easier to observe skewness and outliers. It effectively highlights the asymmetry in the distribution.