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Interpret the following results with (UR) uemployment rate as dependent variable and (INF) inflation as the independent variable.
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- OA linear regression model is Units 3,414-0.839xWeek. For week 45, what is the forecast for the number of units? Round your answer to the nearest whole number. OO unitsThe dependent variable is used to predict the independent variable. Is this True or FalseA certain standardized test measures students' knowledge in English and math. The English and math scores for 10 randomly selected students were recorded and analyzed. The results are shown in the computer output. Predictor Coef SE Coef t-ratio Constant -124.13 78.712 0.046 Math 1.223 0.1966 6.220 0.000 S = 34.55 R-Sq = 82.8% R-Sq (Adj) = 83.5% Which of the following represents the standard deviation of the residuals? O 1.223 34.55 78.712 124.13
- Analysis of Variance Source DF SS MS Regression 1 02364 13 14 Residual Error Total 11.3240 What is the value of SSR (Sums of Squares for Regression)?Paw Patrol Inc. manufactures toys for kids from 1 to 10 years old. The summary of toy sales by month in 2017 and 2018 is as follows: Dt April May 2018 18600 Period Demand 18716 June 19235 2017 September 19400 July August 19500 October 18300 19753 November 17200 September 18320 December 14998 October 17652 2018 January February March 19800 November 16200 19555 December 15563 19800 What is the Mean Absolute Deviation (MAD), when using the weighted 4-period moving average technique? Use weights of 0.6, 0.2, 0.1, and 0.1 for the most recent period, the second most recent period, the third most recent period, and the fourth most recent period, respectively. O 5445 O 1256 O 1555 O 4450 O 1140ANOVA Sigmficance F 0,046 df SS MS F 130433116.219 130433116.219 4.083 Regression Residual Total 113 3609911959.86s 31946123.539 114 3740345076.087 Cosfficrent Standard Error Stát Pvalne 1535.215 Intercept Age 10725.802 6.987 0,000 69.964 34.625 2.021 0.046 Which of the following statements is the best explanation of the R? Select one O'A3.5% of the accident damage can be explained by the age of the driver. B. 3.5% of the variation in accidernt damage can be eaplained by variation in the age of the drver. CC3.5% of the coefficients r stat and p value can be explained by the age of the dtver. D.3.5% of the total errar can be eiplained by the SSE Scanned with CamScanner
- 265 guizres/276906/take Grouping Information Using the lukey Method and .unf.educ Grouping ActivityT N Mean 9 145 444 A shop B. date 9 62.333 Means that do not share a letter are significantly different. Tukey Pairwise Comparisons: Age Grouping Information Using the Tukey Method and 95% Confidence Age N Mean Grouping mid 6 137.167 A old 6 98.500 A B. yng 6 76.000 Means that do not share a letter are significantly different O Marketers should focus on middle age rather than younger groups O Marketers should not pay more for access to middle age rather than older groups O All of the answers are correct. O Marketers should not pay more for access to older groups vs younger groupsAccording to the following given information how to determine: Production Times (months) 1 9 10 11 12 13 14 15 product of 970 1,180 1,239 1,293 1,350 1,398 1,410 1,480 1,492 1,500 1,520 1,592 1,605 1,660 1,685 (Z unit) A) Break Even-point or points? B) How is to construct the relationship between entire variables through the simple drawing for all above figures and highlighting of Break Even point the drawing? C) Justify your final answer for each line of production.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?
- Not sure how to figure out the slope and y-interceptDescribe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?Regression Statistics Multiple R 0.99 R Square 0.98 Adjusted R Square 0.97 Standard Error 2.52 Observations 35 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 131.92 17.76 7.43 0.00 97.11 166.73 Price of Good -7.46 1.18 -6.34 0.00 -9.86 -5.06 Price of Related Good 10.24 0.97 10.60 0.00 8.27 12.21 Income 0.30 0.10 3.00 0.01 0.10 0.50 The demand for your product demands on three factors; the price of your good, the price of a related good, and the average income of your customers. Excel estimated the above linear demand for your product. Refer to the table above. If the price of your good is $10, the price of the related good is $18, and the average income of consumers is $33,000, the predicted demand for your product is Select one: O A. 5,892.22 OB. 10,141.64