Question

1. Compute the regression equation (regression coefficient and constant) using the same data from the previous question. Compute the explained variance (R Square) and the standardized regression coefficient (beta) for this model. For R Square, Sums of Squares Explained = 235.944; Sums of Squares Total = 520.

2. Given: sample R Square 0.232; SS explained = 2848.62; SS residual = 9425.25; N = 62. Test the hypotheses Ho: R square = 0; Ha: R square NE 0 at the .05 level of significance by computing the f statistic and comparing it to the appropriate f critical value.

3.Interpret the following multiple regression equation: meaning of regression constant, coefficients, and explained variance statistics. Income (in $1,000) = 2.446 + 3.628(Education) + 0.233(Occupation Status)

R Square = 0.337

PLEASE ANSWER ASAP WITH CLEAR WORK SHOWN

Answer #1

Compute the regression equation (regression coefficient and
constant) using data
Given X: Mean = 14; Variance =
8.667; Std Deviation = 2.944. Y: Mean = 13;
Variance = 86.667; Std Deviation = 9.309.
cross products = 18, 45, 4 , -9, 4, -3, 52.
covariance = 15.857143
Correlation = 0.578608
X - u Y - u
-3
-6
-3 -15
-2
-2
-1
9
2
2
3 ...

(7) A regression analysis was used in a study about perceived
strength (str) and body condition (cond) among seniors, both
measures are in the range of 0-100. Answer questions based on the
given output
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.880a
.704
.701
2.404
a. Predictors:
(Constant), str
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
688.725
1
688.725
101.665
.002a
Residual
2553.465
414
6.168
Total...

The reason that we obtain the best-fitting line as our
regression equation is that we mathematically calculate the line
with the smallest amount of total squared error.
T
F
Multiple regression is used to predict the value of a single DV
from a weighted, linear combination of IVs.
T
F
The coefficient of determination in multiple regression is the
proportion of DV variance that can be explained by at least one
IV.
T
F
Multicollinearity is desirable in multiple regression....

Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.816
.666
.629
1.23721
a. Predictors:
(Constant),x
ANOVA
Model
Sum of Squares
df
Mean Square
F
Sig
Regression
Residual
Total
27.500
13.776
41.276
1
9
10
27.500
1.531
17.966
.002b
a. Dependent Variable: Y
b. Predictors: (Constant), X
Coefficients
Model
Understand Coefficients
B
Std Error
Standardized
Coefficients
Beta
t
Sig
1 (Constant)
x
3.001
1.125
.500
.118
.816
2.667...

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...

The following estimated regression equation relating sales to
inventory investment and advertising expenditures was given.
ŷ = 25 + 11x1 +
9x2
The data used to develop the model came from a survey of 10
stores; for those data, SSyy (Total Sum of Squares) = 16,000 and
SSR (Regression Sum of Squares) = 11,360.
(a)
For the estimated regression equation given, compute
R2.(Round your answer to two decimal
places.)
R2 = ____
(b)
Compute the adjusted r-square,
Ra2.
(Round your...

The following estimated regression equation relating sales to
inventory investment and advertising expenditures was given.
ŷ = 24 + 12x1 +
7x2
The data used to develop the model came from a survey of 10
stores; for those data, SSyy (Total Sum of Squares) = 17,000 and
SSR (Regression Sum of Squares) = 12,070.
(a)For the estimated regression equation given, compute
R2.(Round your answer to two decimal
places.)
R2 =
(b) Compute the adjusted r-square,
Ra2.(Round
your answer to two...

Given the following regression output,
Predictor
Coefficient
SE Coefficient
t
p-value
Constant
84.998
1.863
45.62
0.000
x1
2.391
1.200
1.99
0.051
x2
-0.409
0.172
-2.38
0.021
Analysis of Variance
Source
DF
SS
MS
F
p-value
Regression
2
77.907
38.954
4.138
0.021
Residual Error
62
583.693
9.414
Total
64
661.600
answer the following questions:
Write the regression equation. (Round your answers to 3
decimal places. Negative amounts should be indicated by a minus
sign.)
y= ____________ + _______________x1 + ________________
x2...

1. The following output is from a multiple regression analysis
that was run on the variables FEARDTH (fear of
death) IMPORTRE (importance of religion),
AVOIDDTH (avoidance of death),
LAS (meaning in life), and
MATRLSM (materialistic attitudes). In the
regression analysis, FEARDTH is the criterion
variable (Y) and IMPORTRE,
AVOIDDTH, LAS, and
MATRLSM are the predictors (Xs). The SPSS output
is provided below, followed by a number of questions (12 points
total).
Descriptive
Statistics
Mean
Std. Deviation
N
feardth
27.0798
8.08365...

Results for: KCereals.mtw
Regression Analysis: Rating versus Shelf position
Method
Categorical predictor coding (1, 0)
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 2 1511 755.6 5.50 0.013
Shelf position 2 1511 755.6 5.50 0.013
Error 20 2748 137.4
Total 22 4259
Model Summary
S R-sq R-sq(adj) R-sq(pred)
11.7222 35.48% 29.03% 21.34%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 32.85 4.43 7.41 0.000
Shelf position
bottom 7.40 7.35 1.01 0.326 1.30
top 18.15 5.58...

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