SPSS Assignment



SPSS Assignment

Data Presentation and Distributions

Statistics

VAR00001

N

Valid

113

Missing

0

Mean

5.7965

Median

6.0000

Mode

6.00

Std. Deviation

1.27587

Variance

1.628

Skewness

.364

Std. Error of Skewness

.227

Kurtosis

-.504

Std. Error of Kurtosis

.451

Range

5.00

Minimum

4.00

Maximum

9.00

From the above table it can be figure out that the mean for the given data is 5.749. the standard deviation for the given data is 1.27 which is not a high value.

The Skewness is a positive value it lead us to conclude that the right tail is longer; the mass of the distribution is concentrated on the left of the figure. It has relatively few high values. The distribution is said to be right-skewed.

VAR00001

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

4

20

17.7

17.7

17.7

5

28

24.8

24.8

42.5

6

35

31.0

31.0

73.5

7

17

15.0

15.0

88.5

8

11

9.7

9.7

98.2

9

2

1.8

1.8

100.0

Total

113

100.0

100.0

From the above histogram shows that there is a positive distribution because the right tail is longer; the mass of the distribution is concentrated on the left of the figure. It has relatively few high values. The distribution is said to be right-skewed.

Comparison of Two Data Sets - Unpaired Data [to do an unpaired or independent t-test]

Case Processing Summary

group

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

data

Micro fem

15

100.0%

0

.0%

15

100.0%

Micro male

15

100.0%

0

.0%

15

100.0%

Macro fem

15

100.0%

0

.0%

15

100.0%

Macro male

15

100.0%

0

.0%

15

100.0%

Descriptives

group

Statistic

Std. Error

data

Micro fem

Mean

3.1567

.04333

95% Confidence Interval for Mean

Lower Bound

3.0637

Upper Bound

3.2496

5% Trimmed Mean

3.1646

Median

3.1700

Variance

.028

Std. Deviation

.16783

Minimum

2.76

Maximum

3.41

Range

.65

Interquartile Range

.14

Skewness

-.606

.580

Kurtosis

1.175

1.121

Micro male

Mean

3.4780

.03512

95% Confidence Interval for Mean

Lower Bound

3.4027

Upper Bound

3.5533

5% Trimmed Mean

3.4772

Median

3.4500

Variance

.019

Std. Deviation

.13603

Minimum

3.22

Maximum

3.75

Range

.53

Interquartile Range

.13

Skewness

.127

.580

Kurtosis

.304

1.121

Macro fem

Mean

5.7653

.03842

95% Confidence Interval for Mean

Lower Bound

5.6829

Upper Bound

5.8477

5% Trimmed Mean

5.7781

Median

5.7800

Variance

.022

Std. Deviation

.14880

Minimum

5.34

Maximum

5.96

Range

.62

Interquartile Range

.18

Skewness

-1.618

.580

Kurtosis

4.176

1.121

Macro male

Mean

5.3673

.02504

95% Confidence Interval for Mean

Lower Bound

5.3136

Upper Bound

5.4210

5% Trimmed Mean

5.3731

Median

5.3800

Variance

.009

Std. Deviation

.09699

Minimum

5.14

Maximum

5.49

Range

.35

Interquartile Range

.16

Skewness

-.660

.580

Kurtosis

.680

1.121

The above table provides descriptive statistics for each group. From the table it can be figured out that micro male has greater mean that micro female, in the Macropterous case it can be seen that Macropterous female has higher value of mean.

From the above histogram it can be figured out that there is negative distribution because The left tail is longer; the mass of the distribution is concentrated on the right of the figure. It has relatively few low values. The distribution is said to be left-skewed.

The above histogram shows a positive distribution because the right tail is longer; the mass of the distribution is concentrated on the left of the figure. It has relatively few high values. The distribution is said to be right-skewed.

Again it can be seen that once again we have a negative distribution because The left tail is longer; the mass of the distribution is concentrated on the right of the figure. It has relatively few low values. The distribution is said to be left-skewed.

Again it can be seen that once again we have a negative distribution because The left tail is longer; the mass of the distribution is concentrated on the right of the figure. It has relatively few low values. The distribution is said to be left-skewed.

Independent Samples T-test (Group 1 - 1 Group 2 - 2)

We use this test to compare two small sets of quantitative data when samples are collected independently of one another. When one randomly takes replicate measurements from a population he/she is collecting an independent sample. Use of a paired t test, to which some statistics programs unfortunately default, requires nonrandom sampling (see below).

Criteria

Only if there is a direct relationship between each specific data point in the first set and one and only one specific data point in the second set, ...