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Section Two Question, Part One a single GET DATA /TYPE=XLSX /FILE='C:Usersaofu550272DesktopData View-1.xlsx' /SHEET=name "Sheet1" /CELLRANGE=FULL /READNAMES=ON /DATATYPEMIN PERCENTAGE=95.0 /HIDDEN IGNORE=YES.

EXECUTE.

NAME OF DATASET DataSet1 WINDOW=FRONT.

/DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF EXIT PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT CI(95) R ANOVA Depressed /METHOD=ENTER Aggression Frustration Inhibition Attention.

Regression

Notes

Output Comments received on 04-MAY-2017 14:22:00

Input

Dataset in Use

DataSet1

none> Filter

Weight nil>

none> Split File

N rows in the working data file

250

Missing Value Management

Missing Definition

Missing values defined by the user are viewed as such.

Cases Statistics are based on circumstances where there are no missing values for any variable.

Syntax

REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS CI(95) R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT Depressive

/METHOD=ENTER Aggression Frustration Inhibition Attention.

Regression

Notes

Output Created

04-MAY-2017 14:22:00

Comments

Input

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

250

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics are based on cases with no missing values for any variable used.

Syntax

REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS CI(95) R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT Depressive

/METHOD=ENTER Aggression Frustration Inhibition Attention.

Resources

Processor Time

00:00:00.05

Elapsed Time

00:00:00.14

Memory Required

4192 bytes

Additional Memory Required for Residual Plots

0 bytes

[DataSet1]

Descriptive Statistics

Mean

Std. Deviation

N

Depressive

50.35

6.824

250

Aggression

51.51

9.791

250

Frustration

50.08

8.815

250

Inhibition

49.41

6.975

250

Attention

49.21

9.400

250

Correlations

Depressive

Aggression

Frustration

Inhibition

Attention

Pearson Correlation

Depressive

1.000

.212

.631

-.216

-.523

Aggression

.212

1.000

.651

-.835

-.812

Frustration

.631

.651

1.000

-.705

-.856

Inhibition

-.216

-.835

-.705

1.000

.888

Attention

-.523

-.812

-.856

.888

1.000

Sig. (1-tailed)

Depressive

.

.000

.000

.000

.000

Aggression

.000

.

.000

.000

.000

Frustration

.000

.000

.

.000

.000

Inhibition

.000

.000

.000

.

.000

Attention

.000

.000

.000

.000

.

N

Depressive

250

250

250

250

250

Aggression

250

250

250

250

250

Frustration

250

250

250

250

250

Inhibition

250

250

250

250

250

Attention

250

250

250

250

250

Variables Entered/Removeda

Model

Variables Entered

Variables Removed

Method

1

Attention, Aggression, Frustration, Inhibitionb

.

Enter

a. Dependent Variable: Depressive

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.799a

.638

.632

4.137

a. Predictors: (Constant), Attention, Aggression, Frustration, Inhibition

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

7401.763

4

1850.441

108.124

.000b

Residual

4192.961

245

17.114

Total

11594.724

249

a. Dependent Variable: Depressive

b. Predictors: (Constant), Attention, Aggression, Frustration, Inhibition

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

39.134

7.784

5.027

.000

Aggression

-.182

.051

-.261

-3.583

.000

Frustration

.353

.059

.457

5.957

.000

Inhibition

.891

.092

.911

9.714

.000

Attention

-.836

.088

-1.152

-9.489

.000

Coefficientsa

Model

95.0% Confidence Interval for B

Lower Bound

Upper Bound

1

(Constant)

23.802

54.466

Aggression

-.282

-.082

Frustration

.237

.470

Inhibition

.711

1.072

Attention

-1.010

-.663

a. Dependent Variable: Depressive

Descriptive - Descriptive Statistics - May 4, 2017

Descriptive Statistics Descriptive Statistics, table, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 5 rows

N

Minimum

Maximum

Mean

Std. Deviation

Amygdala

250

21.270000000000000

56.590000000000000

37.409400000000000

7.366907829000001

Sex

76

1

2

1.50

.503

Valid N (listwise)

Research Study 1

In the study to investigate if adolescent depressive mood can be predicted from the genders and their level of aggressive behavior, frustration, inhibition and attention. The relationship between the depressive mood and these variable are determined. The research question will be; given the gender of adolescent students, does the depressive mood depends on the level of aggressive, frustration, inhibition and attention? In other words between the boys and girls participants, what is the relationship between depressive mood and these variables.

Research study 2

In investigating the possible differences between adolescent boys and girls in the sizes of their amygdala brain structure, information from their amygdala brain structure were taken. The research question of this study will be is there differences in the sizes of amygdala brain structure of girls and boys in adolescent?

Research question 3

The study three investigates possible differences between the sizes of the right and left anterior cingulate cortex in adolescent.

This study tries to find out if the right and left anterior cingulate cortex in adolescent.

The research question in this study would therefore be; is there differences in sizes of the right and left anterior cingulate in adolescent?

Research study one would be better answered by regression analysis from SPSS model.

In Research study two, the question would be better answered by a descriptive statistics. This also applies in the research study three.

According to regression analysis, among girls in adolescent, the depressive mood was not much affected by aggression as compared to the boys. However depression mood affected both gender positively which means, when the aggression rate increases, the depressive mood also increases. Frustrations was the highest variable that positively affected depressive mood. It was higher in girls than boys. 63.1% of depressive mood were caused by frustrations in girls as compared to boys at 55%. This only mean that girls are more affected by frustration than boys. When the frustration increases, depressive mood would increase.

Inhibition, is described as the extent to which the adolescent boys and girls has the capacity to plan suppress all the inappropriate responses in the situation when required.

In the SPSS analysis, inhibition showed a negative relationship with the depressive mood. This means that when inhibition increases, depressive mood decreases. This can said to be a reality as the inhibition is the ability to suppress negative thoughts and therefore when such ability increases, then the depressive mood should decreases.

Attention is another variable that affected depressive mood negatively. Attention is defined as the ability or capacity of the adolescents to shift their focus when desired and focus their attention. Among these gender, attention in girls highly affected their depressive mood negatively. It means that girls have higher attention focus than boys. However in both, the increase in attention focus decreases depressive mood.

Research study two

The size of amygdala structure in girls were seen to be higher than that of boys. The mean of the size of their amygdala was seen to be 37.4. In girls, the size of the structure was at 56 while 21. 2 percent in boys.

Research question three

Descriptive Statistics

N

Minimum

Maximum

Mean

Std. Deviation

Left_ACC

250

.300000000000000

111.940000000000000

45.119000000000000

31.159629130000000

Right_ACC

250

1.080000000000000

85.190000000000000

38.021400000000000

24.737456860000000

Sex

76

1

2

1.50

.503

Valid N (listwise)

76

On the sizes of the left and right anterior cingulate cortex in adolescent, the left side had a maximum size of 111.94% and minimum of 0.3% while right side showed a maximum of 85% and a minimum of 1%. In terms of mean, the mean size of the left was seen to be 45 while that of the right side was 38%. These sizes in the sides of amygdala both the left and right side was higher in boys than in girls.

Part two

Section 4

a). the R2 is 0.36 which translates to 36%. Therefore multiple correction which is R is the square root of R2 in this case, it will be 6. This means R will be 0.6

In this two intervals 0.067 for point 0.07 and 0.505 for point 0.56.

b). Provide the full interpretation of the prediction of the strength of linear regression model in terms of multiple regression

The linear regression model shows that there was a moderate positive relationship between the variables. This is because the R of 0.6 means the relationship is positive and it is also above zero but less than 0.7 which is the strongest point of a relationship between the variables.

c). the 95% confidence interval for the sample R2 value

0.13957 ≤ R2 ≤ 0.58043

Adjusted R2 will be

Adjusted R-square:

0.3018181

When compared to the first one, it can be said the size of the sample do affect confidence level and also the adjusted R squared. In the first sample, the confidence interval was at 0.07 and adjusted R squared was at 0.56. This shows how big the second one is and this is due to the size of the sample.

Section 4.1 problem 2

In this problem, anxiety affects maths value negatively which means when anxiety increases the maths value goes down. On the other hand, self-confidence is the major factor affecting maths value at 56%. This means that the increase in self-confidence increases the maths value. Attitude is the factor that least affects the maths value at 9 percent. Attitude of the father of students also affects the outcome in their performance in mathematics at 15 percent. In general, if the school wants to score higher in mathematics, they need to work so much on the self-confidence of student while at the same time reduce the anxiety among the students in the class.

b. ) The following standardized multiple regression equation has been obtained: • = -+ ...T o MTELD age Siblings 068011045 The dependent variable is Theory of Mind¸ and the three independent variables are TELD (Test of Early Language Development) scores, the Age of Child, and the Number of Siblings.

Give a clear and concise interpretation of the regression coefficients for both Age of Child and Number of Siblings in this model.

This model regression model shows that the number of siblings that one has makes them develop in mind very first when they are growing same as test of language at an early age.

April 26, 2023

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