An economist believes that price, x, (in dollars) is the biggest factor affecting quantity sold, y. To support his argument, he collected data on price and quantity sold from a sample of 29 stores, selling the same product, and generated the regression output in Excel. The regression equation is reported as y = and the correlation coefficient r = - 9.45x + 20.86 0.333. What proportion of the variation in quantity sold y can be explained by the variation in price?
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- Interpret the coefficients and calculate the price elasticity of soft drink demand.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 shellsA 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 2.88
- Mita, the manufacturer of copiers, has been spending increasing amounts of money on radio and television advertising in recent years. An analyst employed by Mita wanted to estimate a simple linear regression of the company's annual copier sales versus advertising dollars. Th regression results included SSE = 12593 and SSR = 87663. What is the coefficient of determination for this regression? 0.874 0.935 0.144 0.126Refrigerator prices are affected by characteristics such as whether or not the refrigerator is on sale, whether or not it is listed as a Sub- Zero brand, the number of doors (one door or two doors), and the placement of the freezer compartment (top, side, or bottom). The table below shows the regression output from a regression model using the natural log of price as the dependent variable. The model was developed by the Bureau of Labor Statistics. Variable Coefficient Standard Error t Statistic 5.4841 Intercept 0.13081 41.92 Sale price -0.0733 0.0234 -3.13 Sub-Zero brand 1.1196 0.1462 7.66 0.06956 0.005351 13.00 Total capacity (in cubic feet) Two-door, freezer on bottom Two-door, side freezer 0.04657 0.08085 0.58 Two-door, freezer on top 0.03596 -9.55 Base -0.3432 -0.7096 -0.8820 One door with freezer 0.1310 -5.42 -5.92 One door, no freezer 0.1491 (a) Write the regression model, being careful to exclude the base indicator variable. (Negative amounts should be indicated by a minus…To determine whether the revenue made trom selling ice creams in a smal shop (in USD) and the temperature (in Celcius degrees) are related, a researcher decides to employ the simple linear regression analysis. Using a random set of days in which the temperature varies between 20 and 35, she comes up with the following regression line: Ice Cream Revenue- 200 90 Temperature Based on this analysis, what can she conclude? O A)At 20 Celcius degrees. the shop owner can xpect to sell 5000 USD O B)AI 30 Celoius degrees, the shop owner can expect to sel 2000 usD O C)As the temperature goes up by one degree Celoius, the revenue increases by approximately 00% OD)On average ce cream revenue is about 200 USD when the temperature is O degrees Celcus O E)As the temperature goes up by one degree Celcius. the reverue inereases by approximately 200 USD.
- Your company just became international by offering its products in both the United States and Canada. Experts in your analytics department believe that tastes for your product differ in those two countries, and have carefully collected data on prices and quantity demanded in both countries. They then present you with the results of two regressions, one for each country, as follows: Log Price regressed on Log Quantity (United States): Standard Coefficients Error t Stat P-value Lower 95% Upper 95 % 1.67605E- Intercept 52.75573994 10.81051303 4.88040363 31.48708283 74.0239705 06 | Log Quantity -5.382266173 1.170584108 -4.597932039 6.15253E-06 -7.685279168 -3.079253177 Log Price regressed on Log Quantity (Canada): Standard Error Coefficients 22.8707593 10.64507785 -2.095788278 | 1.152727409 -1.818112644 0.069981782 t Stat 2.148482109 Upper 95% 43.8139384 P-value Lower 95% Intercept Log Quantity 0.032425603 1.9275802 -4.363669916 0.17209336 Assume you have adequate statistical significance…The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in portland, to the number of guest registered that week: week guests bar sales 1 16 $330 2 12 $270 3 18 $380 4 14 $315 a) The simple linear regression equation that relates bar sales to number of guests(not to time) is (round your responses to one decimal place): Bar sales = [___]+[___]X guestsINSUR A life insurance company wishes to examine the relationship between the amount of life insurance held by a family and family income. From a random sample of households, the company collected the accompanying data. The data are in units of thousands of dollars. INCOME 97 38 141 29 y = x = Let INSUR 280 75 INCOME 303 81 453 137 The numerator of the slope coefficient formula for the estimated regression equation is: 357 77 a 400,201.10 199 43 b 392,354.02 251 53 384,660.80 807 184 d 377,118.44 147 45 272 70 537 128 527 117 245 55 483 116 673 204 194 46 154 51 163 48 280 69 507 140 464 136 321 71 873 206 476 144 574 111 251 65 497 130 826 171 133 32 259 82 281 73 446 146 332 77 219 48 208 55 180 48 169 42 273 69 502 127 547 126 281 80 428 143 370 77 221 49 214 51
- 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 customerK Period Demand 1 8 where y = Demand and x = Period. What is your estimate of the demand in period 7? Estimated demand in period 7 is 2 11 3 10 4 12 y = 7.93 + 0.83 x, 5 10 The least-squares regression equation that shows the best relationship between demand and period is (round your responses to two decimal places): 6 14 (round your response to two decimal places).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 significant