A new product is planned to be released to the market. Data were collected through prelaunch forecasting, the data were analyzed to understand the market behavior and regression analysis was applied and the following equation was obtained y = 0.00005x + 0.5x + 450 The coefficient of innovation is: (1.5) 0.08 CO D. 0.04 0.005 Od 0.10 0.03
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- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.Under what conditions might a firm use multiple forecasting methods?
- (a) Assuming that a simple linear regression model is appropriate, obtain the least squares fit relating selling price to taxes paid. What is the estimate of o²? (b) Find the mean selling price given that the taxes paid are x = 7.50. (c) Calculate the fitted value of y corresponding to x = 5.8980. Find the corresponding residual.Star, Incorporated, used Excel to run a least-squares regression analysis, which resulted in the following output: Regression Statistics Multiple R R Square Observations 0.9798 0.9560 30 Coefficients Standard Error 60,902 0.9237 Intercept Production (x) How much of the variation in cost is explained by production? 174,302 11.06 T Stat 2.86 11.97 P-Value 0.021 0.000A manufacturing firm has developed a skills test, the scores from which can be used to predict workers' production rating factors. 1 Click the icon to view the data on the test scores of various workers and their subsequent production ratings. a. Using POM for Windows' least squares-linear regression module, develop a relationship to forecast production ratings from test scores. (Round your responses and include a minus sign if necessary.) Y= - 23 + .945 X where Y = Production rating and X= Test score. b. If a worker's test score was 54, what would be your forecast of the worker's production rating? (Enter your response as an integer.) More info Production Rating Test Production Test Worker Worker Score Rating Score A 55 43 K 58 57 B 38 43 75 75 93 87 M 67 48 86 77 31 26 E 88 82 62 49 F 66 68 24 25 G 55 47 Q 78 84 Clear all H 50 46 R. 34 32 41 41 51 58 J 69 74 39 30
- year quarterly sales (000 units) Q1 Q2 Q3 Q4 2016 1300 1500 1200 2000 2017 1600 1800 1100 2200 2018 1700 1900 1300 2300 2019 1800 2100 1400 2500 Using a simple regression analysis, determine the trend equation of the sales and use it to estimate the number of units of clothing sold throughout the fiscal year 2020. Assume that Q1 of 2016 is 1, Q2 of 2016 is 2, etc. Show all relevant cakculation detailYour company is preparing an estimate of its production costs for the coming period. The controller estimates that direct materials costs are $45 per unit and that direct labor costs are $23 per hour. Estimating overhead, which is applied on the basis of direct labor costs, is difficult. The controller's office estimated overhead costs at $4,000 for fixed costs and $17 per unit for variable costs. Your colleague, Lance, who graduated from a rival school, has already done the analysis and reports the "correct" cost equation as follows. Overhead = $10,511 + $15.94 per unit Lance also reports that the correlation coefficient for the regression is 0.81 and says, "With 81% of the variation in overhead explained by the equation, it certainly should be adopted as the best basis for estimating costs." When asked for the data used to generate the regression, Lance produces the following: Month LEMASTOSOHN3 1 2 4 5 6 7 8 9 10 11 12 13 Overhead Unit Production $57,064 60,683 77,008 56,624 81,700…A study to determine the correlation between bankdeposits and consumer price indices in Birmingham, Alabama,revealed the following (which was based on n = 5 years of da ta):• LX = 15• Lx2 = 55• Lxy = 70• Ly = 20• L/ = 130a) What is the equation of the least-squares regression line?b) Find the coefficient of correlation. What does it imply to you?c) What is the standard error of the estimate?
- Refer to the following null hypothesis formulated by a restaurant manager who wanted to investigate the factors that encourage the customers to return to their restaurant in future. Identify which data analysis method is the most appropriate for the null hypothesis below. * "There is no relationship between Return in Future and Service Quality, Food Quality and Food Presentation". t-test one-way ANOVA correlation O multiple regressionTopic 2 - Time Series Analysis and F Problem 6-25 eBook Quarter =234 Consider the following time series datą. 고 numurus RANVERAR Meno:aldummodo Year 1 - 2 3 5 Year 2 6 5 7 Year 3 7 6Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances Demand for Guitars 4 Y = 3 4 5 D 5 6 7 7 7 4 10 6 This exercise contains only parts b, c, and d. b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): = 0+0x