In a regression analysis, if SSE = 600 and SSR = 200, find the coefficient of determination. Select an answer and submit. For keyboard navigation, use the up/down arrow keys to select an answer. a 0.250 0.500 0.600 0.750
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- please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearlyMartha’s Wonderful Cookie Company makes a specialsuper chocolate-chip peanut butter cookie. The companywould like the cookies to average approximately eightchocolate chips apiece. Too few or too many chips distortthe desired cookie taste. Twenty samples of five cookieseach during a week have been taken and the chocolatechips counted. The sample observations are as follows: Construct an -chart in conjunction with an R-chart using 3 limits for this data and comment on the cookie-production process. Samples Chips per Cookie1 7 69 852 7 7 8 8 103 5 57 684 4 59 97We have estimated the impact of gross domestic product (GDP), energy consumption (ENERGY) and population (POP) on CO2 emiisions (CO2) in Cyprus. The results are as follows, Dependent Variable: CO2 Method: Least Squares Date: 04/20/17 Time: 09.46 Sample: 1990 2013 Included observations: 24 Variable Coefficient Std. Error t-Statistic Prob. GDP ENERGY POP 2.002813 0.022114 -0.734352 0.203927 6.458672 0.011872 0.328388 0.293686 0,310097 1.862670 -2.236233 0.694371 0.7597 0.0773 0,0369 0.4954 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.825079 Mean dependentyar 0.798841 0.048515 Akaike info criterion 0.047074 Schwarz criterion 40.75460 Hannan-Quinn.criter. 31.44583 Durbin-Wats on stat 0.000000 3.625982 0.108170 -3.062883 -2.866541 -3.010793 1.410912 S.D. dependent yar a Write down the economie function for the above estimation by using the information obtained from above table| b- Write down the economic model for the above…
- Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?You estimated a regression with the following output. Source | SS df MS Number of obs = 268 -------------+---------------------------------- F(1, 266) = 23.48 Model | 668419.175 1 668419.175 Prob > F = 0.0000 Residual | 7572666.51 266 28468.6711 R-squared = 0.0811 -------------+---------------------------------- Adj R-squared = 0.0777 Total | 8241085.68 267 30865.4895 Root MSE = 168.73 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 1.014128 .2092916 4.85 0.000 .6020489 1.426207 _cons | 9.173163 21.13463 0.43 0.665 -32.43929 50.78561…Based on the following data, estimate the slope of the equation using OLS (3 significant digits in final answer): K=ẞ1+ B2J+ε Observation. 1 2 3 U 2 4 9 K 16 15 14 Answer:
- 12 Sum of squares total (SST) is, a 2558.436 b 2610.649 c 2663.927 d 2718.293Consider the following regression: Test Score, = 68.12 +2.52Hours Studied, - 0.04Hours Studied? %3D Without studying, an individual would average a test score ofcalculate slope coefficient for a regression of Y on X calculate the constant of a regression of Y on X calculate the residual for the first observation in the table
- A rectangle is a four-sided figure that has two sets of parallel sides, so that we have two sides of one length and two sides of another length; a square is just a special case of a rectangle in which all four sides are the same length. Therefore, the procedure for calculating area is the same no matter whether we are dealing with a rectangle or a square. The area of a rectangle is calculated as follows: Area = base x height = b × h In this formula, the base is the width of the rectangle and the height is simply how tall the rectangle Is. For example, if we have a rectangle that is 20 centimeters wide and 10 centimeters tall, its area can be calculated as follows: Area = 20 cm x 10 cm = 200 cm² Note the superscript '2' In our answer; this is because we have multiplied centimeters by centimeters. In economics, we are more likely to be dealing with quantities bought or sold and prices, so don't worry about it too much for our discussion. The area of a triangle A triangle is really just a…You estimated a regression with the following output. Source | SS df MS Number of obs = 472-------------+---------------------------------- F(1, 470) > 99999.00 Model | 2.2728e+09 1 2.2728e+09 Prob > F = 0.0000 Residual | 4246681.85 470 9035.4933 R-squared = 0.9981-------------+---------------------------------- Adj R-squared = 0.9981 Total | 2.2771e+09 471 4834590.83 Root MSE = 95.055------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- X | 29.84419 .0595046 501.54 0.000 29.72726 29.96112 _cons | 88.27799 7.592427 11.63 0.000 73.35868 103.1973------------------------------------------------------------------------------…Consider a regression in which a coefficient suffers from downward omitted variable bias. If you add a regressor that controls for the omitted variable bias: Group of answer choices 1. Can’t determine 2. The new estimate will be smaller. 3. The new estimate will remain unchanged. 4. The new estimate will be larger
