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question 1 & 2.
X 1. Using Minitab, we obtained the following values, where the values...
and x = 0. Substitute these values We determined the necessary values for the variables are...
and x = 0. Substitute these values We determined the necessary values for the variables are x, = 2, into the given estimated regression equation. 9 = 10.8 - 4.2x + 6.8x2 + 15.8x3 - 10.8 - 4.2(2) + 6.8(8) + 15.80 ELE 1 This value represents the sales in $1,000s. Therefore, the value obtained from the regression equation must be multiplied by 1,000. Doing so gives a prediction of $ x in sales for a store with two competitors,...
The Minitab output shown below was obtained by using paired data consisting of weights (in lb)...
The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 28 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.)...
Question Help 3.3.9 The Minitab output shown below was obtained by using paired data consisting of...
Question Help 3.3.9 The Minitab output shown below was obtained by using paired data consisting of weights (in b) of 29 cars and their highway fuel consumption amounts (in migal). Along with the paired sample data, Minitab was also given a car weight of 3000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of...
The following partial MINITAB regression output for the Fresh detergent data relates to predictin...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be.10 Predicted Values for New Observations New Obs Flt 8.4139 2 8.4845 SE FIt 1483 125 95% CI (8.1101, 8.7177) (8.2284, 8.7407) 95% PI (6.9872, 9.8406) 7.0672, 9.9019) (a) Report a point estimate of and a 95 percant confidence interval for the mean demand for Fresh in all sales periods when the price difference is.10....
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be .10 Predicted Values for New Observations New Obs Fit SE Fit 8.4139 2 8.4845 1483 125 95% CI (8.1101, 8.7177) (8.2284, 8.7407) 95% Pl (6.9872, 9.8406) (7.0672, 9.9019) (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all sales periods when the price difference...
SOLVE C & D PLEASE The following partial MINITAB regression output for the Fresh detergent data...
SOLVE C & D PLEASE
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be 10 Predicted Values for New Observations New Obs Fit SE Fit 1 8.1879 .1607 2 8.3020 .135 95% CI (7.8588, 8.5170) (8.0262, 8.5779) 95% PI (6.6529, 9.7228) (6.7776, 9.8264) (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all...
Question 7 (1 point) Suppose we have the following values for variables X (independent) and Y...
Question 7 (1 point) Suppose we have the following values for variables X (independent) and Y (dependent) IX-X)(y-7) --630 Z(X - x)2 = 168 SSE = 1225 SSR = 2362.5 What is the value of R-Square (round to four decimal places)? Question 8 (1 point) Suppose we have the following values for variables X (independent) and Y (dependent) EIX-XIY-7)=-630 IIX Xj2 = 168 SSE = 1225 SSR = 2362.5 What is the value of the slope coefficient for the simple...
3. In the multiple regression model shown in the previous question, which one of the following st...
3. In the multiple regression model shown in the previous question, which one of the following statements is incorrect: (b) The sum of squared residuals is the square of the length of the vector ü (c) The residual vector is orthogonal to each of the columns of X (d) The square of the length of y is equal to the square of the length of y plus the square of the length of û by the Pythagoras theorem In all...
Instructions for Excel Assignment 2 a) Using the data in the file (i.e., the values of...
Instructions for Excel Assignment 2 a) Using the data in the file (i.e., the values of Y and values of X), create a scatter plot with the variable Y plotted vertically and the variable Xplotted horizontally. You are to add a trendline, the equation of the line and R2 to this scatter plot. You do not need to add a title to this plot. b) Run the 'Regression analysis tool (found under DATA/Data Analysis). The output using this tool should...
Question 1: Question 2: A statistical program is recommended. A sales manager collected the following data...
Question 1:
Question 2:
A statistical program is recommended. A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is = 81 + 4x. Salesperson Years of Experience Annual Sales ($1,000s) 1 80 3 97 92 107 103 111 119 128 117 136 (a) Compute the residuals. Years of Experience Annual Sales ($1,000s) Residuals 107 103 111 119 128 117 136 Construct a residual...
and x = 0. Substitute these values We determined the necessary values for the variables are x, = 2, into the given estimated regression equation. 9 = 10.8 - 4.2x + 6.8x2 + 15.8x3 - 10.8 - 4.2(2) + 6.8(8) + 15.80 ELE 1 This value represents the sales in $1,000s. Therefore, the value obtained from the regression equation must be multiplied by 1,000. Doing so gives a prediction of $ x in sales for a store with two competitors,...
The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 28 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.)...
Question Help 3.3.9 The Minitab output shown below was obtained by using paired data consisting of weights (in b) of 29 cars and their highway fuel consumption amounts (in migal). Along with the paired sample data, Minitab was also given a car weight of 3000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of...
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be.10 Predicted Values for New Observations New Obs Flt 8.4139 2 8.4845 SE FIt 1483 125 95% CI (8.1101, 8.7177) (8.2284, 8.7407) 95% PI (6.9872, 9.8406) 7.0672, 9.9019) (a) Report a point estimate of and a 95 percant confidence interval for the mean demand for Fresh in all sales periods when the price difference is.10....
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be .10 Predicted Values for New Observations New Obs Fit SE Fit 8.4139 2 8.4845 1483 125 95% CI (8.1101, 8.7177) (8.2284, 8.7407) 95% Pl (6.9872, 9.8406) (7.0672, 9.9019) (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all sales periods when the price difference...
SOLVE C & D PLEASE
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be 10 Predicted Values for New Observations New Obs Fit SE Fit 1 8.1879 .1607 2 8.3020 .135 95% CI (7.8588, 8.5170) (8.0262, 8.5779) 95% PI (6.6529, 9.7228) (6.7776, 9.8264) (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all...
Question 7 (1 point) Suppose we have the following values for variables X (independent) and Y (dependent) IX-X)(y-7) --630 Z(X - x)2 = 168 SSE = 1225 SSR = 2362.5 What is the value of R-Square (round to four decimal places)? Question 8 (1 point) Suppose we have the following values for variables X (independent) and Y (dependent) EIX-XIY-7)=-630 IIX Xj2 = 168 SSE = 1225 SSR = 2362.5 What is the value of the slope coefficient for the simple...
3. In the multiple regression model shown in the previous question, which one of the following statements is incorrect: (b) The sum of squared residuals is the square of the length of the vector ü (c) The residual vector is orthogonal to each of the columns of X (d) The square of the length of y is equal to the square of the length of y plus the square of the length of û by the Pythagoras theorem In all...
Instructions for Excel Assignment 2 a) Using the data in the file (i.e., the values of Y and values of X), create a scatter plot with the variable Y plotted vertically and the variable Xplotted horizontally. You are to add a trendline, the equation of the line and R2 to this scatter plot. You do not need to add a title to this plot. b) Run the 'Regression analysis tool (found under DATA/Data Analysis). The output using this tool should...
Question 1:
Question 2:
A statistical program is recommended. A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is = 81 + 4x. Salesperson Years of Experience Annual Sales ($1,000s) 1 80 3 97 92 107 103 111 119 128 117 136 (a) Compute the residuals. Years of Experience Annual Sales ($1,000s) Residuals 107 103 111 119 128 117 136 Construct a residual...
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