,Week 4
Simple linear Regression
/
-
Cantril ladder & Happiness Index
1
POPULATION SAMPLE
·
entire collection of ·
Subsetofpopulation
Observed
· Numerical descriptor Numerical
· description:Stat
-
parameter · Approx population parameter
statistical inference:
>attemptto reach a conclusion concerning a complete setof
Observations (population), butonly using a subset
there of (sample)
Parameter Statistic
population size N n
mean M it
variance 02 Sh
Standard deviation ⑧ S
, HYPOTHESIS TESTING
> Enables statistical inference
> Involves six-pointplan
STEP 1:Define null hypothesis (HO)
STEP 2:Define alternative hypothesis (H1)
Step 3:Define type I error (prob falsely
of rejecting Ho) or sign.
level a
teststatistic
Step 4:Calculate
Step 5:Find approx. precise p-value (Prob getting
of as
a result or
more extreme observed,
that assuming to is true)
Step itp-valued
6:Conclusion: a, rejectHo.
↳) Otherwise fail to rejectHo.
Simple linear Regression
/
-
Cantril ladder & Happiness Index
1
POPULATION SAMPLE
·
entire collection of ·
Subsetofpopulation
Observed
· Numerical descriptor Numerical
· description:Stat
-
parameter · Approx population parameter
statistical inference:
>attemptto reach a conclusion concerning a complete setof
Observations (population), butonly using a subset
there of (sample)
Parameter Statistic
population size N n
mean M it
variance 02 Sh
Standard deviation ⑧ S
, HYPOTHESIS TESTING
> Enables statistical inference
> Involves six-pointplan
STEP 1:Define null hypothesis (HO)
STEP 2:Define alternative hypothesis (H1)
Step 3:Define type I error (prob falsely
of rejecting Ho) or sign.
level a
teststatistic
Step 4:Calculate
Step 5:Find approx. precise p-value (Prob getting
of as
a result or
more extreme observed,
that assuming to is true)
Step itp-valued
6:Conclusion: a, rejectHo.
↳) Otherwise fail to rejectHo.
