A senior financial analyst with Ace Gadgets (AG) is attempting to get a better g

Question

A senior financial analyst with Ace Gadgets (AG) is attempting to get a better grasp on sales forecasting for AG’s new franchises. She has obtained various details for 27 existing franchises including (see associated spreadsheet):
Sales (Annual) Square feet of the store Inventory Advertising $ (annual) Number of competing stores in the district
From her recollection of her undergraduate course in statistics, she thought of regression analysis as a possibility in modeling new franchise sales. She has enlisted your help in this modeling task and has provided you with this list of questions.
1. What is the correlation between the above variables and sales? 2. Which variable appears to have the strongest relationship with sales? Why do you suggest this variable? 3. Create a scatterplot between the variable that you selected in requirement 2 and sales. Properly label your chart. 4. Add a trend line to the requirement 3 chart along with the regression equation and R2. 5. Interpret (in layman’s language) what the equation means and what the R2 means. Remember that the senior analyst (senior old) hasn’t had a course in statistics in several years and needs an interpretation that is understandable. Be sure to include all elements of the equation. 6. Using the analysis toolpak add-in, run regression analysis using the variable that you selected in requirement 2.   7. Using the output from requirement 6, is this variable statistically significant in predicting sales? What specifically on the output allows you to reach this conclusion1? 8. Which variables from the above list are useful in predicting sales? Why? 9. Using an appropriate Excel function, if a new franchise decided to carry $300,000 in inventory, what can be the expected annual sales for this franchise? Are you 100% confident in your answer? Why or why not?

Can I please get the anwers in excel format? Thank you.

sales sqft inventory advertising competing 231,000 3,000 294,000 8,200 11 156,000 2,200 232,000 6,900 12 10,000 500 149,000 3,000 15 519,000 5,500 600,000 12,000 1 437,000 4,400 567,000 10,600 5 487,000 4,800 571,000 11,800 4 299,000 3,100 512,000 8,100 10 195,000 2,500 347,000 7,700 12 20,000 1,200 212,000 3,300 15 68,000 600 102,000 4,900 8 570,000 5,400 788,000 17,400 1 428,000 4,200 577,000 10,500 7 464,000 4,700 535,000 11,300 3 15,000 600 163,000 2,500 14 65,000 1,200 168,000 4,700 11 98,000 1,600 151,000 4,600 10 398,000 4,300 342,000 5,500 4 161,000 2,600 196,000 7,200 13 397,000 3,800 453,000 10,400 7 497,000 5,300 518,000 11,500 1 528,000 5,600 615,000 12,300 0 99,000 800 278,000 2,800 14 500 1,100 142,000 3,100 12 347,000 3,600 461,000 9,600 6 341,000 3,500 382,000 9,800 5 507,000 5,100 590,000 12,000 0 400,000 8,600 517,000 7,000 8

Explanation / Answer

1)

Find the correlation between the variables and sales.

Use MS Excel software to calculate the correlation between the variables.

From the above output, the correlation between the sales and sqft, inventory, advertising is positive correlation and sales vs competing indicates the negative correlation.

2)

Find the strongest relationship with sales.

Sketch a Scatter plot for the given data,

From the above scatter plot, it is observed that the strongest positive correlation occurs between the Sales Vs sqft.

c)

Same as part (b).

d)

Find the regression equation and

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