Multiple linear regression: number of automobiles sold per day

Help! 1. In order to determine whether or not the number of automobiles sold per day (Y) is related to price (Xl in $1,000), and the number of advertising spots (X2), data were gathered for 7 days. Part of the regression results is shown below.
Coefficient Standard Error
Constant 0.8051
X1 0.4977 0.4617
X2 0.4733 0.0387
Analysis of Variance
Source of Degrees Sum of Mean
Variation of Freedom Squares Square F
Regression 40.700 Error
1.016

MULTIPLE LINEAR REGRESSION help needed

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This is the given.
1. In order to determine whether or not the number of automobiles sold per day (Y) is related to price (Xl in $1,000), and the number of advertising spots (X2), data were gathered for 7 days. Part of the regression results is shown below.
...................................Coefficient ....................................Standard Error
Constant ....................... 0.8051
X1 ............................... 0.4977 .........................................0.4617................................
X2 ............................... 0.4733 ........................................ 0.0387................................
Analysis of Variance-------------------------------------------------------------------------------------
Source of ______________Degrees _____________Sum of ____________Mean
Variation _______________of Freedom __________Squares ___________Square ____________F
Regression __________________________________40.700________________________________
Error________________________________________1.016________________________________
Needs: Fill-in ANOVA; least squares regression function relating Y to X1 and X2; significant relationship between independent and dependent? (alpha = .05); Price significant variable?; Ad spots? (both .05); multiple coefficient of determination.

rick patchen said:

Help! 1. In order to determine whether or not the number of automobiles sold per day (Y) is related to price (Xl in $1,000), and the number of advertising spots (X2), data were gathered for 7 days. Part of the regression results is shown below.
Coefficient Standard Error
Constant 0.8051
X1

0.4977 0.4617
X2 0.4733 0.0387
Analysis of Variance
Source of Degrees Sum of Mean
Variation of Freedom Squares Square F
Regression 40.700 Error
1.016

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