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Calculate the Margin of Error for the 95% Confidence Interval for the Operations mean salary.

Using the Fine Art dataset, run a linear regression of Rating (Y) on Price (X).Enter the R-squared (R²) value rounded to three decimal places.

In the Linear Model, the Slope is 0.0001 and Intercept is 60. What is the predicted rating for a painting costing $5,000?

Using the University dataset, run a linear regression of Value Score (Y) on Student Satisfaction (X).Enter the slope coefficient for Satisfaction, rounded to four decimal places.

When you create a scatter plot of Price (X) vs Rating (Y) using the Fine Art dataset, what overall pattern do you see?

Using the Logarithmic Model (Y=10+8.5·ln(X)), predict the Rating for a painting costing $10,000. (Note: ln(10000)≈9.21).

In the Logarithmic Model (Y=a+b·ln(X)), the Slope (b) is 8.5. If you use =SLOPE(Y, LN(X)) in Excel, this is the value you get. (This confirms you know how to derive the slope for the non-linear model). Enter 8.5 to confirm.

Using the logarithmic regression you ran in Excel, where Rating (Y) is regressed on ln(Price), predict the Rating for a painting priced at $500,000.Enter your answer rounded to one decimal place.

You perform a regression of Value Score (Y) vs. Graduation Rate (X). The Slope is 0.02 and Intercept is 0.1. Predict the score for a school with an 80% graduation rate.

Case Study 1: University Value Analysis “Education Monthly” publishes an annual ranking of universities. To determine a Value Score (where 1.0 is average, and 2.0 is excellent), they analyze factors like Tuition, Graduation Rate, Student Satisfaction, and Post-Grad Employment. You are analyzing a dataset of 20 Universities. You want to understand which variable is the strongest predictor of Value Score. The dataset includes: Value Score (Y) Annual Tuition ( X 1 ) Graduation Rate ( X 2 ) Student Satisfaction ( X 3 ) (Scale 1-100) Employment Rate ( X 4 ) Final_University_Value_20.csv