Question

- Regression: First, choose any metric variable as the dependent
variable and then choose any three other metric variables as
independent variables. HOWEVER, this process must be repeated until
you find a model that produces a significant F-calc (p-value (sig)
<.05). Thus, you may have to sort through several combinations
of dependent and independent variables before finding a combination
that produces a significant F-calc. This is actually quite easy to
do in SPSS using the drop down menus as shown in the powerpoint
slides. Once you get a significant F-calc, then interpret the rest
of the model (r
^{2}, adjusted r^{2}, t-calcs, confidence intervals) and type out the model in the form of y = b_{0}+ b_{1}x_{1}+ b_{2}x_{2}+ b_{3}x_{3}putting in the actual estimates and names of the variables used. Use alpha = .05 for both the F-calc and t-calcs. As a helpful starting hint, you may want to use the variables listed early in the data set to explain related variables listed later in the data set. You only need to report the significant model and not all of those you had to sort through to find it. -
**Variables Entered/Removed**^{a}Model

Variables Entered

Variables Removed

Method

1

DesiredListens, DesiredConvenience, DesiredFriendly

^{b}.

Enter

a. Dependent Variable: ActualConvenience

b. All requested variables entered.

**Model Summary**Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.225

^{a}.051

.040

1.602

a. Predictors: (Constant), DesiredListens, DesiredConvenience, DesiredFriendly

**ANOVA**^{a}Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

36.887

3

12.296

4.789

.003

^{b}Residual

690.696

269

2.568

Total

727.582

272

a. Dependent Variable: ActualConvenience

b. Predictors: (Constant), DesiredListens, DesiredConvenience, DesiredFriendly

**Coefficients**^{a}Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

95.0% Confidence Interval for B

B

Std. Error

Beta

Lower Bound

Upper Bound

1

(Constant)

2.112

.726

2.909

.004

.682

3.542

DesiredConvenience

.219

.104

.158

2.108

.036

.014

.424

DesiredFriendly

.061

.125

.041

.492

.623

-.184

.307

DesiredListens

.106

.150

.059

.703

.483

-.190

.402

a. Dependent Variable: ActualConvenience

REPORT: Your report will consist of one multiple regression output using three metric variables with interpretation (Part A),

Answer #1

There are three independent variables, which are DesiredConvenience, DesiredFriendly and DesiredListens.

Overall F score for the test is 4.789 with a p value of 0.003, which is significant at 0.05 level of significance. So, ANOVA result is significant

t scores corresponding to each independent variable DesiredConvenience, DesiredFriendly and DesiredListens are 2.108, 0.492 and 0.703 with respective p values 0.036, 0.623 and 0.483.

Only DesiredConvenience is a significant predictor of dependent variable based on the p value because its p value is less than 0.05 significance level.

Final model is

**ActualConvenience = 2.112 +
0.219*(DesiredConvenience) + 0.061*(DesiredFriendly) +
0.106*(DesiredListens)**

R square or coefficient of determination is 0.051 or 5.1%, which means that only 5.1% of variation in the dependent variable can be explained by the model.

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...

(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...

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...

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...

Based on the charts below, Determine whether a
statistically reliable oil consumption model can be estimated
Variables
Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
Number People, Home
Index, Degree Days, Customerb
.
Enter
a. Dependent Variable:
Oil Usage
b. All requested
variables entered.
Model
Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.889a
.790
.766
85.445
a. Predictors:
(Constant), Number People, Home Index, Degree Days, Customer
ANOVAa
Model
Sum of Squares
df
Mean Square...

ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
109,780
3
36,593
617,763
,030a
Residual
10,722
181
,059
Total
120,501
184
a. Predictors:
(Constant), F4, F2, F3
b. Dependent Variable:
F1
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
,356
,105
3,373
,001
F2
-,269
,026
-,699
-2,997
,030
F3
,030
,028
,570
2,103
,021
F4
,859
,024
,989
1,112
,141
a. Dependent Variable:
F1
a- Write down the hypothesis, p-...

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...

Analyzed the data from the two tables beow.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
7.029
.059
119.307
.000
Q1. Age
.027
.001
.090
17.839
.000
a. Dependent Variable:
Trust in Government Index (higher scores=more trust)
Model
Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.090a
.008
.008
4.18980
a. Predictors:
(Constant), Q1. Age

Do the following results from SPSS demonstrate a relationship
between relationship status (married and single) and happiness. In
other words, are people more happy when they are married?
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
1.059
1
1.059
.998
.318b
Residual
422.531
398
1.062
Total
423.590
399
a. Dependent Variable:
Relationship happiness
b. Predictors:
(Constant), Marital Status
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
3.957
.081
49.032
.000...

Overview of the Study: The data are based on a Comprehensive
School Reform (CSR) Initiative that focused on the improvement of
reading and writing for students in the primary grade. The school
received a grant from the state which was used to strengthen
classroom teachers’ instructional skills.
The regression outputs present information for students in the
school. Description of the variables: Please use the following
description/coding to help you in your analyses. Gender: female; 1
male=0 EnrollmentStatus: 0 - Not...

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