Question

In: Statistics and Probability

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 95% CI 95% PI
1 8.4972 .1734 (8.1421, 8.8523) (6.9576, 6.9576)
2 8.4425 .134 (8.1690, 8.7160) (6.9197, 6.9197)


(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 is .10. (Round your answers to 3 decimal places.)

Point estimate =
Confidence interval = [, ]

(b) Report a point prediction of and a 95 percent prediction interval for the actual demand for Fresh in an individual sales period when the price difference is .10. (Round your answers to 3 decimal places.)

Point estimate =
Confidence interval = [, ]


(c) Remembering that s = .731328 and that the distance value equals (syˆ/s)2(sy^/s)2, use syˆsy^· from the computer output to hand calculate the distance value when x = .10. (Round your answer to 4 decimal places.)


dv   =   


(d) For this case: n = 30, b0 = 8.533642, b1 = -.364499, and s = .731328. Using this information, and your result from part (c), find 99 percent confidence and prediction intervals for mean and individual demands when x = .10. (Round your answers to 4 decimal places.)

99% C.I.:[, ]
99% P.I.:[, ]


Solutions

Expert Solution

please like if it helps


Related Solutions

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 95% CI 95% PI 1 8.3323 .1570 (8.0107, 8.6539) (6.7456, 9.9189) 2 8.3601 .142 (8.0689, 8.6512) (6.7793, 9.9408) (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...
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 95% CI 95% PI 1 8.6116 .1505 (8.3032, 8.9200) (7.1346, 10.0887) 2 8.4946 .129 (8.2308, 8.7583) (7.0262, 9.9629) (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...
Question 2. The following partial JMP regression output for the Fresh detergent data relates to predicting...
Question 2. The following partial JMP regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be .10. Predicted Demand Lower 95% Mean Demand Upper 95% Mean Demand 31 8.537095392 8.140229299 8.933961486 StdErr Indiv Demand Lower 95% Indiv Demand Upper 95% Mean Demand 0.804754873 6.888629432 10.185561350 (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all sales periods when...
The data in the following MINITAB output refer to an automobile’s stopping distance (in feet) at...
The data in the following MINITAB output refer to an automobile’s stopping distance (in feet) at different speeds. Data Display Row     Speed StopDist 1          25        63 2          25        56 3          30        84 4          35        107 5          45        153 6          45        164 7          55        204 8          55        220 9          65        285 10        65        303 Descriptive Statistics Variable          N         ...
Consider the demand for Fresh Detergent in a future sales period when Enterprise Industries' price for...
Consider the demand for Fresh Detergent in a future sales period when Enterprise Industries' price for Fresh will be x1 = 3.70, the average price of competitors’ similar detergents will be x2 = 3.90, and Enterprise Industries' advertising expenditure for Fresh will be x3 = 6.50, y = the demand in hundreds of thousands of bottles. A 95 percent prediction interval for this demand is given on the following Excel add-in (MegaStat) output: 95% Confidence Interval 95% Prediction Interval Predicted...
Consider the demand for Fresh Detergent in a future sales period when Enterprise Industries' price for...
Consider the demand for Fresh Detergent in a future sales period when Enterprise Industries' price for Fresh will be x1 = 3.70, the average price of competitors’ similar detergents will be x2 = 3.90, and Enterprise Industries' advertising expenditure for Fresh will be x3 = 6.50, y = the demand in hundreds of thousands of bottles. A 95 percent prediction interval for this demand is given on the following Excel add-in (MegaStat) output: 95% Confidence Interval 95% Prediction Interval   Predicted...
(Ch. 18 – Interaction Effects)  Below is Minitab output for a regression model using the Teen Gambling...
(Ch. 18 – Interaction Effects)  Below is Minitab output for a regression model using the Teen Gambling data from Chapter 18.   Use it to answer the questions below. Regression Analysis: Gambling Amount ($) versus Sex, Status, Sex:Status, Income ($100) Model Summary       S    R-sq  R-sq(adj)   21.7798  56.39%     52.24%       Coefficients Term          Coef  SE Coef  T-Value  P-Value   Constant      30.1     16.7     1.80    0.079 Sex          -68.2     20.1    -3.40    0.002   Status      -0.481    0.269    -1.79    0.081 Sex:Status   1.088    0.460     2.37    0.023 Income ($)   4.970    0.984     5.05    0.000 Regression Equation Gambling Amount($) = 30.1 - 68.2 Sex - 0.481 Status + 1.088 Sex:Status + 4.970 Income Interpret the slope of the Sex:Status (interaction) predictor for a female teenager....
For the following data, find the regression equation for predicting Y from X X Y 1...
For the following data, find the regression equation for predicting Y from X X Y 1 2 4 7 3 5 2 1 5 8 3 7 1a. Group of answer choices a. Ŷ = -2X + 8 b. Ŷ =2X + 8 c. Ŷ =1.8X - 0.4 d. Ŷ =1.8X + 0.4 1b. For the following scores, find the regression equation for predicting Y from X X Y 3 8 6 4 3 5 3 5 5 3
For the following data: Find the regression equation for predicting Y from X (Provide your work)....
For the following data: Find the regression equation for predicting Y from X (Provide your work). Use the regression equation to find a predicted Y for each X. Find the difference between the actual Y value and the predicted Y value for each     individual, square the differences, and add the squared values to obtain SSresidual. Calculate the Pearson correlation for these data. Use r2 and SSy to compute SSresidual. You should obtain the same value as in part c. Now...
This question presents regression output from a model predicting life expectancy from gross national product. The...
This question presents regression output from a model predicting life expectancy from gross national product. The model output is also provided below: Variable Estimate Standard Error T Value Pr (> |t| ) Intercept 69.4 0.54 126.7 0.000 Gross National Product 0.000323 0.00004 8.06 0.000 (a) Write out the regression equation (b) Interpret the coefficient and the slope (c) What are the hypotheses for evaluating if gross national product has any impact on life expectancy? (d) State the conclusion of the...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT