2. Using the following regression summery output for the estimation of demand for a product. Regression Statistics R Square 0.969 df SS F Significance F Regression 3 3656.960437 318.144172 0.00000000 Residual 31 118.7781973 Total 34 3775.738635 Coefficients t Stat P-value Intercept 87.30 24.15250613 0.00000000 Price (Px) -0.80 -10.1936142 0.00000000 Price Other (Py) -0.60 3.263427775 0.01580374 Income (I) 1.00 6.097885873 0.00000985 a. Comment on the significance of the regression model as a whole: b. Comment on the significance of the specific coefficients: c. Interpret the R2 value:
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
2. Using the following regression summery output for the estimation of demand for a product.
|
Regression Statistics |
|
|
|
|
|
R Square |
0.969 |
|
|
|
|
|
df |
SS |
F |
Significance F |
|
Regression |
3 |
3656.960437 |
318.144172 |
0.00000000 |
|
Residual |
31 |
118.7781973 |
|
|
|
Total |
34 |
3775.738635 |
|
|
|
|
Coefficients |
t Stat |
P-value |
|
|
Intercept |
87.30 |
24.15250613 |
0.00000000 |
|
|
Price (Px) |
-0.80 |
-10.1936142 |
0.00000000 |
|
|
Price Other (Py) |
-0.60 |
3.263427775 |
0.01580374 |
|
|
Income (I) |
1.00 |
6.097885873 |
0.00000985 |
|
a. Comment on the significance of the regression model as a whole:
b. Comment on the significance of the specific coefficients:
c. Interpret the R2 value:
c. State the estimated demand
d. Determine the change in Qx if Px increases by $1:
e. If Px = $67.50, Py = $50.5, I = $69, compute the value for Qx
f. Using the information above, compute the own price elasticity of demand and determine if the firm is maximizing its revenue at the current price.
g. Using the information above, compute the cross-price elasticity (round your answer to two decimals) and interpret your findings.
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