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SPSS Assignment only - Lab Report Example

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The positive relationship is depicted by the coefficient value of 0.636 between the two variables.
Does the average daily attendance rate have stronger…
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SPSS Assignment only
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SPSS #4 Assignment- STA-250.16 _____________________________________ Correlations Math Achievement in Twelfth Grade Reading Achievement in Twelfth Grade
School Average Daily Attendance Rate
Math Achievement in Twelfth Grade
Pearson Correlation
1
.636**
.051
Sig. (2-tailed)
.000
.296
N
500
500
417
Reading Achievement in Twelfth Grade
Pearson Correlation
.636**
1
.014
Sig. (2-tailed)
.000
.769
N
500
500
417
School Average Daily Attendance Rate
Pearson Correlation
.051
.014
1
Sig. (2-tailed)
.296
.769
N
417
417
417
**. Correlation is significant at the 0.01 level (2-tailed).
Question 8
What is the correlation between reading achievement in 12th grade and math achievement in the 12th grade? How strong is this relationship? In what direction is this relationship? (2 points)
There exists a strong positive relationship between reading achievement in 12th grade and math achievement in the 12th grade. The positive relationship is depicted by the coefficient value of 0.636 between the two variables.
Question 9
Does the average daily attendance rate have stronger relationship with math achievement in 12th grade or reading achievement in 12th grade? Are either of these two relationships significant? (2 points)
There is no strong relationship either between math achievement in 12th grade or reading achievement in 12th grade the coefficient is 0.051 and 0.014 respectively. None of the relationships is significant because they have a p value that is greater than 0.01.
Question 10
Based on the scatterplot for math achievement in 12th grade and socio-economic status, what direction is the relationship? How strong is the relationship? Be sure to explain your answers. (2 points)
There is a weak positive correlation between math achievement in 8th grade and socio-Economic Status. This is because the data points are highly scattered and the trend of the data points seems not to be linear.
Question 11
Based on the scatter plot for math achievement in 8th grade and math achievement in 12th grade, what direction is the relationship? How strong is the relationship? Be sure to explain you answer. (2 points)
There is a strong positive relationship between math achievement in 8th grade and math achievement in 12th grade. The trend of the scatter plot clearly shows it’s linear whereby the variables are directly proportional
Question 12
Based on the scatterplot for math achievement in 8th grade and math achievement in 12th grade, how many outliers are there? (1 point)
There are two outliers that can be observed
Part II
Run a Bivariate regression with guns as the only independent variable and interpret the results. (3 points)
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.159a
.025
.005
4.01279
a. Predictors: (Constant), rate
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
20.145
1
20.145
1.251
.269b
Residual
772.919
48
16.102
Total
793.064
49
a. Dependent Variable: Handgun Homicides per 100,000
b. Predictors: (Constant), rate
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
20.145
1
20.145
1.251
.269b
Residual
772.919
48
16.102
Total
793.064
49
a. Dependent Variable: Handgun Homicides per 100,000
b. Predictors: (Constant), rate
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
5.958
.768
7.754
.000
rate
.011
.010
.159
1.119
.269
a. Dependent Variable: Handgun Homicides per 100,000
Is the overall model significant? How do you know?
The overall model is not significant because the p value (sig) of 0.269 is greater than 0.01.
How much of the variation is explained?
The analysis of variance is 1.251 and the R squared of 0.005 explains the variation of the variable.
How does whether the State has a waiting period for handgun purchase influence the handgun homicide rate for that State? Remember to describe this relationship in terms of existence, strength, and direction.
It positively influences the handgun homicide rate and the relationship is weak
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.542a
.294
.248
3.48862
a. Predictors: (Constant), Unemployment Rate, Jan 1996, State has executed a prisoner since 1976, rate
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
233.222
3
77.741
6.388
.001b
Residual
559.842
46
12.170
Total
793.064
49
a. Dependent Variable: Handgun Homicides per 100,000
b. Predictors: (Constant), Unemployment Rate, Jan 1996, State has executed a prisoner since 1976, rate
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
2.277
2.685
.848
.401
rate
-.002
.009
-.029
-.217
.829
State has executed a prisoner since 1976
-2.462
1.010
-.309
-2.437
.019
Unemployment Rate, Jan 1996
1.511
.429
.455
3.525
.001
a. Dependent Variable: Handgun Homicides per 100,000
Coefficient Correlationsa
Model
Unemployment Rate, Jan 1996
State has executed a prisoner since 1976
rate
1
Correlations
Unemployment Rate, Jan 1996
1.000
-.053
-.279
State has executed a prisoner since 1976
-.053
1.000
.211
rate
-.279
.211
1.000
Covariances
Unemployment Rate, Jan 1996
.184
-.023
-.001
State has executed a prisoner since 1976
-.023
1.021
.002
rate
-.001
.002
8.108E-005
a. Dependent Variable: Handgun Homicides per 100,000
Run a Multivariate regression controlling for the other variables (unemp96 and executed) and interpret the results. (3 points)
State execution has a negative influence while unemployment has a positive influence on the Hand gun Homicides per 100,000
Is the overall model significant? How do you know?
The overall model is significant because the p value is less than 0.01
How much of the variation is explained?
The R squared is 0.294 and the F value is 6.388
How does having a waiting period, unemployment rate, and number of executions influence the handgun homicide rate of a State? Remember to describe these relationships in terms of existence, strength, and direction.
Number of execution has a negative influence while the unemployment has a positive influence on the handgun homicide rate of a state. Both have a weak relationship with the handgun homicide rate of a state
Did controlling for the other variables change the results? If so, how? (3 points)
It never changed the results significantly Read More
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