Zayden can use linear regression to predict his savings trend for the next year. By fitting a line to the data points representing weekly changes, he can estimate the annual change and project his future savings. This method assumes a consistent pattern, helping him make a reasonable prediction for his overall savings or spending.
Zayden can employ linear regression as a method to predict his total savings or spending over the next year based on the observed changes in his savings account. Linear regression involves fitting a straight line to the data points to model the relationship between the independent variable (in this case, weeks) and the dependent variable (changes in savings).
First, Zayden would organize his data into a scatter plot, with the x-axis representing weeks and the y-axis representing the corresponding changes in savings. The data points (24.85, 19.70, -16.63, 45.15, -9.02) would be plotted accordingly. By visually inspecting the scatter plot, he can assess if there's a linear trend, indicating a consistent pattern in his savings behavior.
Next, Zayden can use statistical tools or software to perform linear regression analysis on the data. The regression model will provide him with an equation for the line that best fits the data. This equation can then be used to predict future changes in savings based on the week number.
With the equation in hand, Zayden can extrapolate the trend to estimate his total savings or spending over the course of the next year. He would substitute the week numbers corresponding to the upcoming weeks into the regression equation to obtain the predicted changes in savings for each week. By summing up these predicted changes, he can estimate the overall change in his savings by the end of the year.