In: Statistics and Probability
1) Consider the following sample data for the relationship between advertising budget and sales for Product A:
Observation | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
Advertising ($) | 40,000 | 50,000 | 50,000 | 60,000 | 70,000 | 70,000 | 80,000 | 80,000 | 90,000 | 100,000 |
Sales ($) | 240,000 | 308,000 | 315,000 | 358,000 | 425,000 | 440,000 | 499,000 | 494,000 | 536,000 | 604,000 |
What is the predicted sales quantity for an advertising budget of $68,000?
Please round your answer to the nearest integer.
Note that the correct answer will be evaluated based on the full-precision result you would obtain using Excel.
2) Consider the following sample data for the relationship between advertising budget and sales for Product A:
Observation | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
Advertising ($) | 50,000 | 60,000 | 60,000 | 70,000 | 70,000 | 80,000 | 90,000 | 90,000 | 100,000 | 110,000 |
Sales ($) | 299,001 | 371,000 | 364,000 | 430,000 | 440,000 | 485,000 | 535,000 | 546,000 | 595,000 | 675,000 |
What is the correlation value for the relationship between advertising and sales?
Please round your answer to the nearest hundredth.
Note that the correct answer will be evaluated based on the full-precision result you would obtain using Excel.
1) What is the predicted sales quantity for an advertising budget of $68,000?
We know regression equation is,
Y = a + b*X.
Here, X : Independent variable = Advertising and Y : Dependent variable = Sales
By using Excel data analysis tool, we calculate regression.
Output:
SUMMARY OUTPUT | ||||||
Regression Statistics | ||||||
Multiple R | 0.9971322 | |||||
R Square | 0.9942727 | |||||
Adjusted R Square | 0.9935567 | |||||
Standard Error | 9239.0163 | |||||
Observations | 10 | |||||
ANOVA | ||||||
df | SS | MS | F | Significance F | ||
Regression | 1 | 1.185E+11 | 1.185E+11 | 1388.8101 | 2.94894E-10 | |
Residual | 8 | 682875380 | 85359422 | |||
Total | 9 | 1.192E+11 | ||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | 7711.25 | 11491.76 | 0.67 | 0.52 | -18788.81 | 34211.30 |
Advertising ($) | 6.00 | 0.16 | 37.27 | 0.00 | 5.63 | 6.37 |
Therefore, Regression equation is,
Sales = 7711.25 + 6.00 * Advertising.
Then,
Sales = 7711.25 + 6.00 *.68000
= 415897
The predicted sales quantity for an advertising budget of $68,000 is $415,897.
2) What is the correlation value for the relationship between advertising and sales?
By using Excel data analysis tool, we calculate correlation.
Output:
Advertising ($) | Sales ($) | |
Advertising ($) | 1 | |
Sales ($) | 0.997 | 1 |
The correlation value for the relationship between advertising and sales is 0.997