Suppose that we obtained an estimated equation for the regression of weekly sales of palm pilots and the price charged during the week. Interpret the constants b0 for the product brand manager.
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Suppose that we obtained an estimated equation for the regression of weekly sales of palm pilots and the price charged during the week. Interpret the constants b0 for the product brand manager.
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- Given the following summary statistics, determine the regression equation used to predict y from Ta Round all answers to 2 decimal places. slope - y-intercept Sy SI T 15 Y 1.02 1.6 -0.71 20.65 77-9 Use the exact value of slope when calculating the y-intercept.QUESTION 1 Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. This table is used for Questions 1-6. Dependent variable: In(Price) Regressor (1) (2) (3) (4) (5) 0.00042 (0.000038) Size In(Size) 0.57 (2.03) 0.69 0.68 0.69 (0.055) (0.054) (0.087) In(Size)² 0.0078 (0.14) Bedrooms 0.0036 (0.037) Рol 0.082 0.071 0.071 0.071 0.071 (0.032) (0.034) (0.034) (0.036) (0.035) 0.037 0.027 0.026 0.027 0.027 (0.030) View (0.029) (0.028) (0.026) (0.029) Pool x View 0.0022 (0.10) 0.12 (0.035) Condition 0.13 0.12 0.12 (0.035) 0.12 (0.045) (0.035) (0.036) 6.63 (0.53) Intercept 10.97 6.60 7.02 6.60 (0.069) (0.39) (7.50) (0.40) Summary Statistics SER 0.102 0.098 0.099 0.099 0.099 R? 0.72 0.74 0.73 0.73 0.73 Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0…The following data relate the sales figures of restaurant, to the number of customers registered that week: Week Customers Sales (SR) First 16 330 Second 12 270 Third 18 380 Fourth 14 300 a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?
- Sally Sells Sea Shells by the Sea Shore and collects all sales dataNow she is curious to find out what the elasticity of demand is for her shells Assume they are all the same type and quantity She scatter plots the data and finds there is a linear relationship that looks ripe for a regression estimation of the price response function for her shells The slope of her regression line is 61. Currently, her average daily price is 11.74 and she sells 95 quantity at that priceCalculate the point elasticity of demand for her sea shellsHow do you interpret the R-squared obtained from running this regression?Numerical Answer Only Type Question Enter the numerical value only for the correct answer in the blank box. If a decimal point appears, round it to two decimal places. Assume that the number of visits by a particular customer to a mall located in downtown Toronto is related to the distance from the customer's home. The following regression analysis shows the relationship between the number of times a customer visits(Y)per month and the distance(X, measured in km) from the customer's home to the mall. \[ Y=15-0.5 X \] A customer who lives30 kmaway from the mall will visi______ who lives10 km away. less times than a customer
- A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage industry and collects monthly data for 25 firms. He estimates the modet: Sales- Bo + B1 Advertising +t. The following table shows a portion of the regression results. Coefficients Standard Error t-stat p-value Intercept 40.10 14.08 2.848 0.0052 Advertising 2.88 1.52 -1.895 0.0608 Which of the following are the competing hypotheses used to test whether the slope coefficient differs from 3? Multiple Choice Ho i bị 3; HAtbi3 Họ ib - 2.88; HAibi 2.88Stores commonly offer a cheaper unit price for large quantity purchases. Quantity 1 2 5 10 20 Unit Price $100.00 $80.00 $70.00 $50.00 $40.00 a. Use regression to find a logarithmic equation to model the data. Round the numbers in your equation to 2 decimal places. y = a + bln(z) with You b b. Use your equation to find an appropriate unit price for a customer who purchases 15 items. c. Use your equation to find an appropriate unit price for a customer who purchases 25 items. $You estimated a regression with the following output. Source | SS df MS Number of obs = 163 -------------+---------------------------------- F(1, 161) = 63944.07 Model | 218107451 1 218107451 Prob > F = 0.0000 Residual | 549156.503 161 3410.90995 R-squared = 0.9975 -------------+---------------------------------- Adj R-squared = 0.9975 Total | 218656608 162 1349732.15 Root MSE = 58.403 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 22.59171 .0893406 252.87 0.000 22.41528 22.76814 _cons | 89.83793 8.208112 10.95 0.000 73.62848 106.0474…
- A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage industry and so collects monthly data for 25 firms. He estimates the model: Sales 6g + 61 Advertising + e. The following table shows a portion of the regression results. Coefficients Standard Error t-stat p-value 40.10 14.88 2.848 0.0052 Intercept Advertising 2.88 1.52 -1.895 0.0608 When testing whether Advertising is significant at the 10% significance level, the conclusion is to Multiple Choice reject Hg, we can conclude advertising is significant not reject He; we cannot conclude advertising is significant reject He; we cannot conclude advertising is significant not reject He; we can conclude advertising is significantYou estimated the following regression. What value would you predict for Y, if X = 81? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 204 -------------+---------------------------------- F(1, 202) = 406.05 Model | 6131684 1 6131684 Prob > F = 0.0000 Residual | 3050340.21 202 15100.6941 R-squared = 0.6678 -------------+---------------------------------- Adj R-squared = 0.6661 Total | 9182024.21 203 45231.6463 Root MSE = 122.88 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 40.27997 1.998931 20.15 0.000 36.33853 44.22142 _cons | 192.9333 120.837 1.60 0.112…GENERAL LINEAR MODEL I 1. Consider the simple regression model y; = B2x; + ɛ Find the least squares estimator b, and show E §, &, = 0
