r studio answer to these questions
What is the intercept parameter (2dp) for the regression equation of height (y) versus age (x) using the pine_growth.csv data?
What is the total amount of variation explained by the regression model (SSr) of height (y) versus age (x) using the pine_growth.csv data?
What is the residual error sum of squares (SSe) of a regression model of height (y) versus age (x) using the pine_growth.csv data.
Use your regression equation fitted to the pine_growth.csv data to predict the height (1 dp) of pine trees at 21.8 years.
pine_growth.csv
age height
7.15576656 18.72849431
8.129775638 35.65477778
16.72602077 40.84925353
22.1494314 61.16668286
24.98842629 63.88029087
10.84800989 38.8948113
16.21167696 50.89108137
21.62648568 58.84822993
27.37032481 58.21976844
8.781800332 38.78419079
15.73044358 47.08995523
18.44298031 47.03292591
24.55722077 57.78373702
8.803465152 19.78892211
14.69364295 38.47185166
19.76821622 52.85251645
25.73732976 54.03867394
9.557438738 19.52645519
13.38045294 50.71181575
21.18576248 50.32075486
25.14524863 66.45636737
11.05895289 35.09854872
15.90885155 46.20593357
21.16517367 59.1335112
24.74997389 53.78095766
10.76039501 31.87024304
17.24982036 38.43437782
20.67469663 42.75693936
24.9187064 56.50248257
9.456849174 27.94407215
17.31785455 44.56102526
19.4035485 43.26457601
26.94362485 69.07639749
10.18743471 30.07622452
14.82076927 32.81933238
19.41960951 45.6665625
26.22703124 54.85557574
11.19384358 37.97390848
13.43821684 44.08033847
20.28160916 60.39976036
26.66076684 66.07282947
10.75153533 27.48342127
15.10263364 30.26795241
19.30307787 47.03971079
26.1097938 57.30252533
7.334193884 16.02310563
7.780802556 29.81303216
13.55907716 35.60521517
18.98254692 45.67647822
27.2336165 49.5956103
8.64498995 22.56785223
16.72757792 40.15677105
18.76667604 39.37013143
26.03969617 62.91864072
11.89543801 16.12208671
14.86010339 33.6554498
18.36353229 50.36578767
25.28054491 65.29231743
r studio answer to these questions What is the intercept parameter (2dp) for the regression equation of height (y) versus age (x) using the pine_growth.csv data? What is the total amount of variation...
1. In regression analysis, the Sum of Squares Total (SST) is a. The total variation of the dependent variable b. The total variation of the independent variable c. The variation of the dependent variable that is explained by the regression line d. The variation of the dependent variable that is unexplained by the regression line Question 2 In regression analysis, the Sum of Squares Regression (SSR) is A. The total variation of the dependent variable B. The total variation of the independent variable...
For INTERCEPT() and SLOPE() functions, do we place x values or y values first in the parentheses? For CORREL() function, do we place x values or y values first in the parentheses? What is the difference in meaning between y and y_hat? What does the regression model minimize? SSE or SSR or SST? Variation in Y = Time explained by X = Miles is SST, SSR or SSE? Variation in Y = Time not explained by X = Miles is SST,...
Use the table and the given regression equation to answer parts (a)-(e). y = - 1.50 a. Compute the three sums of squares, SST, SSR, and SSE, using the defining formulas. SSTEN (Type an integer or a decimal.) SSR=N (Type an integer or a decimal.) SSEN (Type an integer or a decimal.) b. Verify the regression identity, SST = SSR + SSE. Is this statement correct? O No O Yes c. Determine the value of the coefficient of determination. (Round...
b. Calculate the slope and y-intercept for the regression equation, SST, and Partition the SST into the SSR and the SSE. (Round to three decimal places as needed.) c. Provide an interpretation for the value of the slope. Suppose an environmental agency would like to investigate the relationship between the engine size of sedans and the miles per gallon (MPG) they get. The accompanying table shows the engine size in cubic liters and rated miles per gallon for a selection...
A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 81 + 4x. Salesperson Years of Experience Annual Sales ($1,000s) 1 107 103 101 119 8 9 10 10 11 13 123 127 136 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = (b) Compute the coefficient of determination 2. (Round your answer to three decimal places.) 12...
Below are given (a) A scatterplot of Y versus X and (b) A plot of residuals versus fitted values after a simple linear regression model was fit to the data. What is the equation of the fitted line? Discuss what is indicated about the relationship between Y and X as it relates to simple linear regression. Fitted Line Plot Y = - 14.64 + 7.431 X R-Sq R-Sq (adj) 2.43700 91.9% 91.8% 1 > 20- 3 4 5 6 7...
A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ŷ = 80 + 4x. Salesperson Years of Experience Annual Sales ($1,000s) 1 1 80 2 3 97 3 4 97 4 4 102 5 6 103 6 8 101 7 10 119 8 10 118 9 11 127 10 13 136 (a) Compute SST, SSR, and SSE. SST= SSR= SSE= (b) Compute...
Please answer all the questions relating to part 1 Stat 3309 - Statistical Analysis for Business Applications I Consider the following data representing the starting salary in $1,000) at some company and years of prior working experience in the same field. The sample of 10 employees was taken and the following data is reported. years of experience starting salary in $1,000) 2 50 15 18 20 Part 1: Use the formulas provided on the 3rd formula sheet to compute the...
**R-STUDIO KNOWLEDGE REQUIRED*** PLEASE ANSWER THE FOLLOWING WITH ****R-STUDIO**** CODING- thank you so much!! I am specifically look for the solution to part ***(h)**** and *****(i)***** below using R-Studio code: The data set in question is: YEAR Height Stories 1990 770 54 1980 677 47 1990 428 28 1989 410 38 1966 371 29 1976 504 38 1974 1136 80 1991 695 52 1982 551 45 1986 550 40 1931 568 49 1979 504 33 1988 560 50 1973 512...
ek-tin Based on the following regression output, what proportion the total variation in Y is explained by X? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA di SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 Residual 8 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 0.917214 o b.9.385572...