fitting a least square line to n = 14 data points, the quantities below were computed. SSxx = 25 x-bar = 4 SSyy = 22 y-bar = 3 SSxy = 23 Find the following: (Show work) a- Find the least square line (Y-hat = Y-intercept + Slope * x) b- Calculate SSE c- Calculate s^2

fitting a least square line to n = 14 data points, the quantities below were computed. SSxx = 25 x-bar = 4 SSyy = 22 y-bar = 3 SSxy = 23 Find the following: (Show work) a- Find the least square line (Y-hat = Y-intercept + Slope * x) b- Calculate SSE c- Calculate s^2

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The table shows the amounts of crude oil (in thousands of barrels per day) produced by a certain country and the amounts of crude oil (in thousands of barrels per day) imported by the same country for seven years. The equation of the regression line is y=−1.326x+16,981.04. Complete parts (a) and (b) below. Produced,_x 5,789 5,679, 5,626, 5,407, 5,234, 5,138, 5,094 Imported,_y 9,309 9,130, 9,681, 10,090, 10,151, 10,152, 10,017 (a) Find the coefficient of determination and interpret the result. r2= (Round to three decimal places as needed.) (b) Find the standard error of estimate se and interpret the result. Se= (Round to three decimal places as needed.)
The following regression equation is based on the analysis of four variables: SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads. RADIO_ADS is the number of radio advertisements aired weekly by the agency. WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads. The variable WEB_VISITS is the number of weekly visitors to their educational website. These data have been recorded every week for the past three years. WEB_VISITS (expected) = 208 + 1.5*SM_DOLLARS + 0.5*RADIO_ADS + 0.8*WS_DOLLARS The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $108 without changing the amount spent on radio ads or web search ads? (Round your answer to the nearest whole number.)
Google "covid 19 california" and choose statistics from menu on left. Select data for "all time" period. In the discussion editor, click on 3 dots, insert table using editor (3 columns and 10 rows) Label the table with headings Day (t), Cases (y), Rate of change () Fill column 1 with alternate Sunday dates starting from May 3 ending Aug 16 Fill in column 2 with number of daily cases from Google statistics page. For each row (except first), find rate of change in cases per fortnight (2wk). At = 14 For example day rate of change cases May 3 5707 (8658-5707)/14= ? cases per fortnight May 10 8658 May 17
Compute the least-squares regression line for predicting the right foot temperature from the left foot temperature. Round the slope and y-intercept values to four decimal places.
Given the data as shown in the table below X Y 56 0.86 44 0.93 68 0.85 76 0.87 94 0.73 a) Using �^=-0.0034�^+1.0798 as the equation of the "Best-Fit" line. Calculate the residuals, �-�^, of this data:�1=56, �1-�^1 = −0.0292Incorrect . Round your answer to 4 decimal places.�2=44, �2-�^2 = 0.0000Incorrect . Round your answer to 4 decimal places.�3=68, �3-�^3 = 0.0016Incorrect . Round your answer to 4 decimal places.�4=76, �4-�^4 = 0.0488Incorrect . Round your answer to 4 decimal places.�5=94, �5-�^5 = −0.0300Incorrect . Round your answer to 4 decimal places.b) Using the formula��=�12+�22+⋯+��2�-2calculate the standard deviation, ��, of all residuals.��=0.0371Correct . Round your answer to 4 decimal places. Question HelpQuestion 4: Message Message instructor Submit QuestionQuestion 4
A least squares line for a sample with 11 observations has an SSE = 192; calculate and s. Please show your work So i can understand how you arrived at the answer
A frequent flyer was interested in the relationship between dollars spent on flying and the distance flown. She sampled 20 frequent flyers of a certain airline. She collected the number of miles flown in the previous year and the total amount of money the flyer spent. A regression line of distance flown on money spent was fit to the data: y^=24000+10x. One person in the sample spent $3000 and flew 42,567 miles. What would be the residual (or error) in the predicted distance this person would fly?
Describe in detail about regression lines?
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Given the “data” determined by y = x^3 + (x-1)^2 with x = 0.1, 1.2, 2.3, and 2.9, calculate SSTO, SSR, and R^2. Then recalculate these using x = 0.3, 1, 2.45, and 2.8. Does where you collect your data (i.e., which values of x) appear to impact your interpretation of how good the linear model fits?.
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Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables margin, - the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and • home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to 0 when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: regress margin home if team===== "Brooklyn Nets" . Source Model Residual Total margin home _cons SS 1459.95122 15252.0488 16712 df 1459.95122 1 80 190.65061 None of the above 81 206.320988 Coef. Std. Err. 8.439024 3.049595 -5.219512 2.156389 MS t Number of obs F(1, 80) Prob > F R-squared O The Nets lost more games than they won in 2014 P>|t| 2.77 0.007 -2.42 0.018 Adj R-squared = Root…