Regression Analysis: TA, ABC Total, Gender and Statistical Anxiety

Lab report
(Regression)

Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Regression analysis between TA and Statistical anxiety.......................................................3
Regression analysis between ABC Total and Statistical anxiety...........................................4
Regression analysis between Gender and Statistical anxiety.................................................6
CONCLUSION................................................................................................................................9
REFERENCES..............................................................................................................................10

INTRODUCTION
In stats, the linear regression model is being used to discover patterns and trends in a particular
collected data. It may assume that there have been a relation among how much a user work and
how much they receive salary; regression test can support to measure that relation. Regression
analysis can also provide specific graph valid equation, which is further beneficial to forecast the
results (Morris, 2015).
In this report, regression analysis between various variables has been performed in order to
determine the relation. The respective dataset allows a regression analysis to test which (if any)
of TA/ABC/Gender has a predictive relationship to Statistical anxiety.
MAIN BODY
H0 : The TA/ABC/Gender has a predictive have a positive relationship to Statistical anxiety.
H1: The TA/ABC/Gender has a predictive have a negative relationship to Statistical anxiety
Regression analysis between TA and Statistical anxiety
Regression Statistics
Multiple R
0.54377
6
R Square
0.29569
3
Adjusted R
Square
0.29090
1
Standard
Error
3.51583
8
Observations 149
ANOVA
df SS MS F
Significan
ce F
Regressi
on 1
762.87
52
762.87
52
61.715
71 7.69E-13
Residual 147
1817.0
84
12.361
12
Total 148
2579.9
6
Coefficie
nts
Standa
rd
Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%

Interce
pt 4.579819
1.2083
18
3.7902
43
0.0002
19 2.1919
6.9677
37 2.1919
6.9677
37
32 0.324423
0.0412
97
7.8559
35
7.69E-
13
0.2428
11
0.4060
35
0.2428
11
0.4060
35
Interpretation: Regression method is a good statistical technique which can be advantageous
through a company to achieve the extent to which specific control variables influence dependent
variables. There are limitless different options for carrying out regression analyses to yield
worthwhile, business intelligence. In the company proposes a theory that one thing, whether or
not they can monitor the thing, influences a majority of the market, advises running a multiple
regression to decide how comfortable they can be about the theory. This will enable for even
more knowledgeable management decisions, more efficient allocation of resources.
Regression analysis between ABC Total and Statistical anxiety
Regression Statistics
Multiple R
0.42332
3
R Square
0.17920
2
Adjusted R
Square
0.17361
9
Standard
Error
4.30265
7
Observations 149
ANOVA
df SS MS F
Significan
ce F
Regressi
on 1
594.15
35
594.15
35
32.094
1 7.49E-08
Residual 147
2721.3
9
18.512
86
Total 148
3315.5
44
Coefficient
s
Standar
d Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercep
t 38.47805
1.47873
1 26.021
6.81E-
57
35.5557
4
41.4003
7
35.5557
4
41.4003
7
32 -0.28631
0.05053
8
-
5.6651
7
7.49E-
08
-
0.38618
-
0.18643
-
0.38618
-
0.18643