Simple Regression Sales Revenue Year Product Price (P) (millions of pesos) 1 1.20 0.95 1.50 0.93 3 2.00 0.92 4 3.50 0.90 4.10 0.87 5.60 0.85 Let Sales Revenue be (v) and Product Price be (x) Solve for the coefficient of correlation O -96.0818% O -96.0717% O-98.0717% -98.0818%
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Solve for the coefficient of correlation
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- 1. R-squaredSuppose regression of y on an intercept and x with 50 observations yields total sum of squares 100 andexplained sum of squares 36.(a) What is ?^2?(b) What is the correlation coefficient between y and x?(c) What is the standard error of the residual?Sales in a company are $188 million in 2009 and increase $208 million in 2010. Compute the percentage increase in sales using the usual formula Compare this value to the approximation The approximation performs (1) percentage change increases. 100 x (1) O better (2) O deteriorates O worst O improves 100 x 100 x (Sales 2010 - Sales 2009) Sales 2009 100 x [In (Sales2010) -In (Sales2009)] (Sales 2010 - Sales2009) Sales 2009 100 x [In (Sales 2010) -In (Sales2009)] = [ (Express your response as a percentage and round to three places) Now, assume that sales in a company are $188 million in 2009 and increase $264 million in 2010. = (Sales2010 - Sales 2009) Sales 2009 % % % 100 x [In (Sales 2010) -In (Sales 2009)] = [ (Express your response as a percentage and round to three places) % when the change is small. The quality of the approximation (2). as theA 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
- The regression equation Netincome = 2,049 +.0478 Revenue was estimated from a sample of 100 leading world companies (variables are in millions of dollars). (0-1) If Revenue = 1, then Netincome = (a-2) Choose the correct statement. (b) Choose the right option. (c) O Increasing the revenue raises the net income. Decreasing the revenue raises the net income. O Increasing the revenue lowers the net income. million. (Round your answer to 2 decimal places.) O The intercept is not meaningful because a firm cannot have net income when revenue is zero. The intercept is meaningful because a firm can have net income when revenue is zero. If Revenue = 24,574, then NetIncome = million. (Round your answer to the nearest whole number.)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 1140OA 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 units
- ANOVA 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 CamScannerPlastic cups are made by forming of raw plastic. The price of plastic is 4.500 OMR per kg and the price of plastic cups is 13 OMR per 1000 cups. Plant requires differ- ent machine which cost about 50,000 OMR, but the machines has to be imported, after paying 25% as customs duty. On a graph paper, draw a freehand graph and represent all important parameters. Due to increase in plastic price, the break-even quantity has increased by 10%. Find out the new plastic price?You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…
- 5. The following estimated equation was obtained by OLS regression using quarterly data for 1978 to 1996 inclusive. Yt = 2.20+ 0.104Xt₁ - 3.48 Xt₂ + 0.34Xt3 (3.4) (0.005) (2.2) (0.15) Standard errors are in parentheses, the explained sum of squares was 109.6, and the residual sum of squares 18.48. a. Test at the 5% level for the statistical significance of the parameter estimates. b. Calculate the coefficient of determination.(c) Diberi Jadual 3 Given Table 3 Jadual 3 Table 3 Dependent Variable: GNIG Method: Least Squares Sample: 1971 2020 Included observations: 50 Variable Coefficient Std. Error t-Statistic Prob. INV -0.029738 0.143418 -0.207349 0.8367 ME -0.403906 0.830281 -0.486469 0.6290 INF 0.171334 0.194713 0.879930 0.3837 GEXP 0.133799 0.126295 1.059421 0.2952 DUMMY -2.894680 1.990087 -1.454549 0.1529 -4.133286 6.959526 -0.593903 0.5556 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Mean dependent var S.D. dependent var 0.191174 6.031855 0.099262 3.736466 5.481783 3.546176 Akaike info criterion 553.3159 Schwarz criterion 5.711226 -131.0446 Hannan-Quinn criter. 5.569156 1.529494 2.079971 Durbin-Watson stat Prob(F-statistic) 0.085970 di mana Dummy ialah pembolehubah dummi (D=0 untuk pra-krisis 2008; D=1 untuk pasca-krisis 2008) where Dummy, is dummy variable (D=0 for pre-crisis 2008; D=1 for post-crisis 2008) Kemelesetan besar, kemelesetan ekonomi yang…As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…