# Analyze some Data

**Using Excel** Think about an interest (hobby) you have or some situation at work … or simply look through a magazine (auto, sports?), newspaper, almanac … and find two variables (X and Y) that you think are correlated. For example (you can’t use this one!), get the horsepower rating (X) and the MPG gas consumption (Y) for 15 or more automobiles, to see (using Excel) if there is a correlation between the two variables. The focus of your analysis must be an original idea of your own. Taking an example from a regression tutorial on the Internet gets you ZERO credit!

Your independent variable cannot be time periods (like months, years, etc). So, do NOT select data like monthly sales, where sales is the ‘y’ and time period is the ‘x’.

Your sample size should be at least 15, but preferably 20-plus.

Enter the X and Y data (in side-by-side columns) in Excel (and check the accuracy of your data entry). Put a title on each column. Highlight the two titles and data columns. Select INSERT. Select SCATTER DIAGRAM. Select the diagram with the points not connected by lines (first choice). Point to any one of the plotted points in the diagram when it appears and right-click the mouse. Select ADD TRENDLINE. Select both SHOW REGRESSION EQUATION and SHOW R-SQUARED VALUE.

What to turn in:

1. State the source of your data. (Web site URL, magazine title/date/page#, etc.)

2. State in a sentence or two the question you are trying to answer.

3. Excel printout showing: - data listing (clearly stating the unit of measurement for each variable) - fully labeled scatter diagram (at least a ¼ page in size, and with the Independent variable on the X axis) showing plotted points, regression equation, R-squared value,

and regression line

4. On the printout write or type your interpretations of the: - slope - y intercept (attempt this, but there might not be a sensible interpretation) - R-squared

An example of interpreting the slope (b1 = - 0.036) is: “For each additional 1 horsepower of the engine, we estimate the miles-per-gallon rating of the vehicle to decrease by 0.036 mpg.” Put another way, “For each additional 100 horsepower of the engine, we estimate the miles-per-gallon rating of the vehicle to decrease by 3.6 mpg.”

An example of interpreting the y intercept (b0 = 92.6) is: “For a vehicle having an engine with 0 horsepower, we estimate the miles-per-gallon rating of the vehicle to be 92.6 mpg.” (Sensible? Yes and no.)

An example of interpreting the R-squared value (R2 = 0.64) is: “64% of the variation in gas consumption rating of the vehicles sampled is explained by the variation in their horsepower ratings.

Taidot: tietojenkäsittely

Tietoa työnantajasta:
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Projektin tunnus: #13830445

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