Question

The explained sum of squares measures variation in the dependent variable Y about the:.

mean of the Y values. |
||

estimated Y values. |
||

mean of the X values |
||

Y-intercept |

Which statement is incorrect?

Binary predictors shift the intercept of the fitted regression |
||

a qualitative variable has c categories, we would use only c - 1 binaries as predictors. |
||

If there is a binary predictor (X = 0, 1) in the model, the residuals may not sum to zero. |
||

Another name for the dummy variable is the dummy variable. |

If the SSE is 104, and the SST is 349, the value of r is: (rounded to 2 significant digets)

Answer #1

Solution :

i) The sum of squares of the deviations of the estimated values from the mean value of a response variable is the explained sum of squares.

Hence the correct option is **estimated Y
values**

ii) If a qualitative variable has C categories then we would have only C-1 binaries as predictors as the Cth category gets fixed due to the other categories.

Hence the correct option is a **qualitative variable has c
categories, we would use only c-1 binaries as
predictors**

iii)

Given : SSE = 104 , SST = 349

To find : r

Solution :

r2 = 1 - (SSE/SST)

= 1 - (104/349)

= 1 - 0.2979

r2 = 0.7021

Therefore, **r = 0.84**

In regression analysis, the total variation in the dependent
variable, measured by the total sum of squares
(SST), can be decomposed into two parts: the amount of
variation that can be explained by the regression model, and the
remaining unexplained variation.
True
False
In employing the randomised block design of ANOVA, the primary
interest lies in reducing the within-treatments variation in order
to make easier to detect differences between the treatment
means.
True
False
If we reject the null hypothesis,...

Select all the statements that are true of a least-squares
regression line.
1. R2 measures how much of the variation in Y is explained by X
in the estimated linear regression.
2.The regression line maximizes the residuals between the
observed values and the predicted values.
3.The slope of the regression line is resistant to outliers.
4.The sum of the squares of the residuals is the smallest sum
possible.
5.In the equation of the least-squares regression line, Y^ is a
predicted...

The following information regarding a dependent variable (Y) and
an independent variable (X) is provided.
Y
X
4
2
3
1
4
4
6
3
8
5
SSE = 6
SST = 16
Refer to Exhibit 12-4. The least squares estimate of the slope
is
Question 7 options:
1
2
3
4

Is it true that total sample variation of the dependent variable
Y, also called the total sum of squares = (s^2)y * (n − 1) where n
= sample size and s^2y is the sample variance of Y? Why or why
not?

A regression and correlation analysis resulted in the following
information regarding a dependent variable (y) and an
independent variable (x).
Σx = 90
Σ(y - )(x - ) = 466
Σy = 170
Σ(x - )2 = 234
n = 10
Σ(y - )2 = 1434
SSE = 505.98
The least squares estimate of the intercept or
b0 equals
Question 18 options:
a)
-1.991.
b)
.923.
c)
-.923.
d)
1.991.

Can the likelihood to choose HP again (q6) be explained
by respondents’ perceptions of HP’s quick delivery
(q8_3)?
Run a simple linear regression in SPSS and paste the output (4
tables below):
Variables
Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
q8_3b
.
Enter
a. Dependent Variable:
q6
b. All requested
variables entered.
Model
Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.303a
.092
.089
.54315
a. Predictors:
(Constant), q8_3
ANOVAa
Model
Sum of...

You are given the following information about x and
y.
x
Independent Variable
y
Dependent Variable
1
5
2
4
3
3
4
2
5
1
Refer to Exhibit 14-5. The least squares estimate of
b0 (intercept)equals ______.
A) -1
B) 1
C) 5
D) 6

8. Calculating SSR, SSE, SST, and R-squared
Suppose you are interested in studying the effects of education
on wages. You gather four data points and use ordinary least
squares (OLS) to estimate the following simple linear model:
wage=β0+β1educ+u
where
wage = hourly wage in dollars
educ = years of formal education
After running your regression, you decide to examine how the
fitted values of wages from your regression compare to the actual
wages in your data set. These data are...

SUMMARY OUTPUT
Dependent
X variable:
all other variables
Regression Statistics
Independent
Y variable:
oil usage
Multiple R
0.885464
R
Square
0.784046
variation
Adjusted R Square
0.76605
Standard Error
85.4675
Observations
40
ANOVA
df
SS
MS
F
Significance F
Regression
3
954738.9
318246.3089
43.56737
4.55E-12
Residual
36
262969
7304.693706
Total
39
1217708
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-218.31
63.95851
-3.413304572
0.001602
-348.024
-88.596
-348.024
-88.596
Degree Days
0.275079
0.036333
7.571119093
5.94E-09...

Exhibit 6
The following information regarding a dependent variable (Y) and
an independent variable (X) is provided.
Y X
4 2
6 3
8 4
8 5
11 6
Also given are SSE=1.6 and SST=27.2.
a. Refer to Exhibit 6. What is the least squares estimated
regression equation?
A. Yhat = 1 + 1.6X
B. Yhat = 2 + 3.2X
C. Yhat = 1.6 + 27.2X
D. Yhat = 27.2 + 4.6X
b. Refer to Exhibit 6. What is the...

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