An account of a multivariate analysis of a provided data file, using multiple regressions
An account of a multivariate analysis of a provided data file, using multiple regressions
Assignment - 1
Requirement A:
We have use general linear model on SPSS to test the impact of independent variable on dependent variable. GLM is used when the independent variable is scale but the independent variables are combination of scale and categorical variable. Therefore, there were some categorical independent variables like gender, group, handed etc and the result is discussed below:
Tests of Between-Subjects Effects
Dependent Variable:time1
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
462986.598a
7
66140.943
6.893
.000
Intercept
282159.824
1
282159.824
29.407
.000
keen
17519.719
3
5839.906
.609
.610
group
.000
0
gender
320452.985
1
320452.985
33.398
.000
hand2
34805.584
1
34805.584
3.628
.058
dumgroup
.000
0
age
65934.457
1
65934.457
6.872
.009
Error
2341144.875
244
9594.856
Total
5308755.000
252
Corrected Total
2804131.472
251
a. R Squared = .165 (Adjusted R Squared = .141)
The above table “Tests of Between-Subjects Effects” shows the impact of independent variable on time, which is our study variable. Here in this table the impact is shown as whole mean this table does not show the impact of an individual category. For example, the gender has impact on the time taken by the observant but it does not tell which gender takes more time and which takes less time and there might be no impact of either gender. Now, we just simply discuss the impact of independent variables understudy as whole. The keenness has no impact on the time taken by the observant as sig value for this variable is above 0.05 (0.61) but that does not mean that any keen level that we are studying have no impact on the time taken, therefore we discuss individual impact of each category in further analysis. Similarly, being a right handed or left handed has nothing to do with the time take as this variable also has p value greater than 0.05. On the other hand, intercept gender and age has significant impact on the study variable, so we can say that variation in gender and age can influences that time taken by the coffee drinker.
Parameter Estimates
Dependent Variable:time1
Parameter
B
Std. Error
t
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Intercept
279.517
44.205
6.323
.000
192.446
366.589
[keen=1]
-15.662
18.102
-.865
.388
-51.317
19.994
[keen=2]
-17.770
17.705
-1.004
.317
-52.644
17.103
[keen=3]
.684
17.723
.039
.969
-34.226
35.594
[keen=4]
0a
[group=1]
-38.955
12.770
-3.051
.003
-64.107
-13.802
[group=2]
0a
[gender=1]
-76.752
13.281
-5.779
.000
-102.911
-50.592
[gender=2]
0a
[hand2=1]
-26.674
14.005
-1.905
.058
-54.261
.912
[hand2=2]
0a
[dumgroup=0]
0a
[dumgroup=1]
0a
age
-4.397
1.677
-2.621
.009
-7.702
-1.093
a. This parameter is set to zero because it is redundant.
The above table parameter estimates has benchmarked one category form each level as to measure the impact of other category (ies) in respect variable against the benchmarked variable. This table shows that any level of keenness does effect the time taken by the coffee drinker. Similarly hand used by the coffee drinker while taking the coffee does not influence the time taken. On the other hand group (drank coffee as part of the experiment) shows greater influence on the time taken than the other group (drank decaffeinated coffee as part of the experiment) that is set as benchmark. The beta of the group 1 is -38.955, which mean this group takes less time than other one (p<0.05). Similarly, gender (female) takes less time than he males so as age of the coffee drinker.
Requirement B:
Tests of Between-Subjects Effects
Dependent Variable:time10
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
1187.956a
7
169.708
9.050
.000
Intercept
6.057
1
6.057
.323
.570
keen
106.825
3
35.608
1.899
.130
group
.000
0
gender
623.560
1
623.560
33.254
.000
hand2
1.717
1
1.717
.092
.762
dumgroup
.000
0
age
446.364
1
446.364
23.804
.000
Error
4575.314
244
18.751
Total
27122.000
252
Corrected Total
5763.270
251
a. R Squared = .206 (Adjusted R Squared = .183)
Here in this table, we just simply discuss the impact of independent variables understudy as ...