Answer:take this knowledge
Step-by-step explanation:To complete the table with the residuals, we can use the given linear model equation y = 12x + 41. For example, for the first row where x = 10, we can substitute it into the equation: y = 12 * 10 + 41 y = 120 + 41 y = 161 The observed value of y is 162, so the residual would be: Residual = observed value - predicted value Residual = 162 - 161 Residual = 1 By following the same process for each row, you can complete the table with the residuals. b. To determine if the line given is a reasonably good fit, we need to analyze the residuals. If the residuals are mostly small and close to zero, it indicates a good fit. If the residuals are large and have a wide range, it suggests a poor fit. c. The slope of the line represents the rate of change or the increase in donations for each additional hour worked. In this situation, the slope of 12 means that for every additional hour Clare works, the organization collects $12 more in donations. d. To describe the correlation between the variables, we need to consider the direction and strength. - The direction of the correlation is positive because as the number of hours worked increases, the amount of donations also increases. - The strength of the correlation can be determined by looking at how close the data points are to the line. If the points are tightly clustered around the line, it suggests a strong positive correlation. If the points are more spread out, it indicates a weaker correlation. e. Clare's boss is making a hasty conclusion based on the correlation between Clare's hours worked and the donations collected. While there is a positive correlation, it doesn't necessarily mean that Clare's presence alone is the cause of the increase in donations. There could be other factors at play, such as the organization's promotional efforts or external events. It's important to consider all possible variables before attributing the increase solely to Clare's presence.
