Suppose the variable x is the number of minutes walked each week and the variable y is the blood cholesterol level given by the following table:

x y
31 290
60 278
90 259
120 238
150 219
Using the regression equation, predict cholesterol levels when walking is 175 minutes per week.

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To predict the cholesterol levels when walking is 175 minutes per week using regression analysis, you need to fit a regression line to the given data and use the equation of the line to make the prediction. Here are the steps to do that:

Step 1: Plot the given data points (x, y) on a scatter plot. The x-axis represents the number of minutes walked each week (x), and the y-axis represents the blood cholesterol levels (y).

Step 2: Draw a line that best fits the scatter plot of the data points. This line is called the regression line. It represents the relationship between the two variables.

Step 3: Determine the equation of the regression line. To do this, you can use statistical software or tools like Microsoft Excel, Google Sheets, or Python. These tools will calculate the regression line equation based on the given data points.

Step 4: Once you have the equation of the regression line, substitute the value of x=175 into the equation. This will give you the predicted cholesterol level (y) when walking is 175 minutes per week.

For example, suppose the equation of the regression line is:
y = mx + b

For the given data points, this equation could be:
y = -1.1x + 308

Substituting x=175 into the equation, we get:
y = -1.1(175) + 308
y = -192.5 + 308
y ≈ 115.5

Therefore, the predicted cholesterol level when walking 175 minutes per week is approximately 115.5.

Note: The regression line assumes a linear relationship between the variables, and the prediction is based on this assumption.