Week 3 Assignment 2
Forecasting 1
Making Decisions Based on Demand and Forecasting
Sherri Fishback
Dr. Robert Pennington
ECO550
July 20, 2013
Forecasting 2
1. Use Excel or other calculation software to input the data to calculate an estimated regression. Then, from the calculation provided, interpret the coefficient of determination, indicating how it will influence your decision to open the pizza business in your town or community. Explain any additional variables that may improve the coefficient of determination.
a. To start the regression model is posed a relationship concerning the dependent and independent variables. You input your data for the spreadsheet and in this paper we are using 30 market examples or observations.
In the Price (P) Plot all the square are close to 6.00 and 7.00 the plot to get 6.93 is to add up total Price 207.87 divided by 30 equals 6.929 or 6.93 and the Competitive Price Plot (Px) 184.62 divided by 30 = 6.154 or 6.16 for the total sum of the square.
The coefficient is to determine how well the regression data fits the data. The square of R (0.832980642) is the degree of correlation between the dependable variable Y and Independent variable X.
The regression analysis shows the results also of an Analysis of Variance (ANOOA) to calculate the significant F (2.9764E x 30 -7). The graph shows all measured data points, the line of best fit and the equation for the line and the R2 and P value.
R= 0.8329
X/y= 184.62 or 18.5 over 207.87 or 2.08
Making Decisions Based on Demand and Forecasting
Sherri Fishback
Dr. Robert Pennington
ECO550
July 20, 2013
Forecasting 2
1. Use Excel or other calculation software to input the data to calculate an estimated regression. Then, from the calculation provided, interpret the coefficient of determination, indicating how it will influence your decision to open the pizza business in your town or community. Explain any additional variables that may improve the coefficient of determination.
a. To start the regression model is posed a relationship concerning the dependent and independent variables. You input your data for the spreadsheet and in this paper we are using 30 market examples or observations.
In the Price (P) Plot all the square are close to 6.00 and 7.00 the plot to get 6.93 is to add up total Price 207.87 divided by 30 equals 6.929 or 6.93 and the Competitive Price Plot (Px) 184.62 divided by 30 = 6.154 or 6.16 for the total sum of the square.
The coefficient is to determine how well the regression data fits the data. The square of R (0.832980642) is the degree of correlation between the dependable variable Y and Independent variable X.
The regression analysis shows the results also of an Analysis of Variance (ANOOA) to calculate the significant F (2.9764E x 30 -7). The graph shows all measured data points, the line of best fit and the equation for the line and the R2 and P value.
R= 0.8329
X/y= 184.62 or 18.5 over 207.87 or 2.08