1.
SST = Σ(y - ȳ)2 = 3948
2.
SSE = SST - SSR = 3948 - 208 = 3740
3.
MSE = SSE / df of regression = 3740 / 1 = 3740
4.
Slope (a) = Σ(x - x̅).(y - ȳ) / Σ(x - x̅)2 = -312/468 = -0.6667
5.
Intercept (b) = ȳ - a * x̅ = (Σy)/n - a * (Σx)/n = (680/4) - (-0.6667)*(180/4) = 200
Section B: Long Questions - - B1: The following information regarding a dependent variable Y and...
Case 1 The following information regarding a dependent variable Y and an independent variable X is provided. ZX-16 Σ(X-x)(Y-Y) SST-42 n=4 SSE 34 Refer to case 1. The slope of the regression function is 0.1 Refer to case 1. The slope of the regression function is Refer to case 1. The slope of the regression function is Refer to case 1. The slope of the regression function is -1 Refer to Case 1. The Y intercept is 0.1 Refer to...
4 Consider the following set of ordered pairs. a) Calculate the slope and y-intercept for these data. b) Calculate the total sum of squares (SST). c) Partition the sum of squares into the SSR and SSE. a) Calculate the slope and y-intercept for these data. y- Round to four decimal places as needed.) b) Calculate the total sum of squares (SST) SST c) Partition the sum of squares into the SSR and SSE. (Round to one decimal place as needed.)...
a) Calculate the slope and y-intercept for these data. b) Calculate the total sum of squares (SST). c) Partition the sum of squares into the SSR and SSE. a) Calculate the slope and y-intercept for these data. y = + x (Round to four decimal places as needed.)
Consider the following set of ordered pairs. 5 4 1 n 5 5 3 2 4 3 у 3 a) Calculate the slope and y-intercept for these data. b) Calculate the total sum of squares (SST). c) Partition the sum of squares into the SSR and SSE. a) Calculate the slope and y-intercept for these data. X у (Round to four decimal places as needed.) b) Calculate the total sum of squares (SST). SST = (Round to one decimal place...
A) Calculate the slope and y-intercept for these data. B) Calculate the total sum of squares (SST). C) Partition the sum of squares into the SSR and SSE. Cursider she fallrwing set af ardered pairs a) Calculate the slope and y intercept for these dala. b) Caiculatc the total sum of squares ISST c) Pa ton the sum of squaresmo the SSH, and SSE sum ct saunto tme SSH
Consider the following set of ordered pairs. X 4 5 4 6 3 3 у a) Calculate the slope and y-intercept for these data. b) Calculate the total sum of squares (SST). c) Partition the sum of squares into the SSR and SSE. a) Calculate the slope and y-intercept for these data. y=-x (Round to four decimal places as needed.)
An instructor asked a random sample of eight students to record their study times at the beginning of a course. She then made a table for total hours studied (x) over 2 weeks and test score (y) at the end of the 2 weeks. The table is given below. Complete parts (a) through (d). X 11 1512 208 15 16 21 0 y 91 76 83 75 89 77 80 80 Ex= 118, Ey= 651, Exy = 9,464, Ex2 =...
The following information regarding a dependent variable (Y in $1000) and an independent variable (X) is provided. Y Dependent Variable 15 17 23 17 I. The least-squares estimate of the slope equals: II. The least-squares estimate of the intercept equals: III. If the independent variable increases by 2 units, the dependent variable is expected to a. decrease by $300 b. decrease by $3000 c. decrease by $3 d. decrease by $2 e. none of the above The letter corresponding...
The following information regarding a dependent variable (Y in $1000) and an independent variable (X) is provided. Y Dependent Variable 15 17 23 17 I. The least-squares estimate of the slope equals: II. The least-squares estimate of the intercept equals: III. If the independent variable increases by 2 units, the dependent variable is expected to a. decrease by $300 b. decrease by $3000 c. decrease by $3 d. decrease by $2 e. none of the above The letter corresponding...
Part of an Excel output relating 15 observations of X (independent variable) and Y (dependent variable) is shown below. Provide the values for a-e shown in the table below. (See section 15.5) Summary Output ANOVA df SS MS F Significance F Regression 1 2.7500 -d- -e- 0.632 Residual -a- -b- 11.45 Total 14 -c- A Company has recorded data on daily demand for its product (y in thousands of units) and the unit price (x in hundreds of dollars). A...