Linear Regression
Linear Regression
Linear Regression
ATS
Outcomes
Performed regression analysis using
SPSS
Analyzed data using regression /
multiple regression
Regression Analysis
Is a statistical tool for the investigation of
relationships between variables
Usually, the researcher seeks to ascertain the
cause effect of one variable upon another
Examples:
The effect of Organizational Climate on Employee
Satisfaction
The effects of Advertisement and Price on Sales
Relationship between Mothers Age, Weight,
History of Hypertension, and Smoking Status and
on Babys Birth Weight
Linear Regression
It is used when we want to predict the
value of a variable based on the value of
another variable
The value we want to predict is the
DEPENDENT VARIABLE
(OUTCOME/RESPONSE), and the variable
we are using to predict the other
variables value is called the
INDEPENDENT VARIABLE
(PREDICTOR/EXPLANTORY)
INDEPENDE
DEPENDENT
NT
VARIABLE
VARIABLE/S
X, Y,
EXPLANATO OUTCOME,RESPON
RY, SE, PREDICTED
Six Assumptions for Linear
Regression
1. the variables are continuous
2. there is a linear relation
(correlation)
3. there should be no significant
outliers
4. there should be independence of
observation
5. the data needs to show
homoscedasticity
6. the residuals of the of the
LINEAR REGRESSION
IT IS A PARAMETRIC STATISTICS
Self-esteem Life
Perceived control
Optimism Satisfaction
Explanatory (X)
Response (Y)
Predictor
Predicted
Essential feature
Ho= B1=B2=B3 = 0
VARIABLE RANGE
Self esteem: 1-30; 15-25 within normal range;
below 15 suggest low esteem, above 25
suggest high esteem (Rosenberg)
Perceived control: 1-30-:1-10- low; 11-20-
medium; 21-30 - high
Optimism:1-40; 20 and above optimistic; 19
and below- pessimistic
Life-satisfaction: 1 -35; 31-35- very high; 26-30-
high;20-25-average;15-19-sightly below
average;10-14- dissatisfied;5-9- extremely
dissatisfied (Diener et al)
Scatter Plot
Pearson R Correlation
0.785193 0.59555
2 0.9064
40
40
97
40
35
35
30 35
30
25 30
25
20 25
15 20
20
10 15
15
5 10
10
0 5
5
5 10 15 20 25 30 35
0
0
14 16 18 20 22 24 26 28 30 32
12 14 16 18 20 22 24 26 28 30 32
Migrate from Excel to SPSS, then
name and label the 4 variables
<Analyze<Regression<
linear
Transfer LS to the dependent box and SE, PC
and OP to the Independent box, < Statistics
Check estimate, confidence interval, model fit <
continue (note: you may check other data as needed)
<Continue<OK
Important Outputs
P 7
STATISTICAL MODEL
IMPLICATION/CONCLUSION
In order to improve ones satisfaction
in life, one must be able to improve
perceived control, self-esteem and
most especially optimism.
Interventions to improve
simultaneously these three
categories in a person, would allow a
person to be satisfied in life.