This course is a short series of lectures on Introductory Statistics. Topics
covered are listed in the Table of Contents. The notes were prepared by EwaPaszek and Marek Kimmel.
The development of this course has been supported by NSF 0203396 grant.
Hypotheses testing - examples.
We have tossed a coin 50 times and we got
k = 19 heads . Should we accept/reject the hypothesis that
p = 0.5, provided taht the coin is fair?
Null versus alternative hypothesis:
Null hypothesis
Alternative hypothesis
Significance level
= Probability of Type I error = Pr[rejecting
|
true]
P [
or
]
.
If
or
]
, then under the null hypothesis the observed event falls into rejection region with the probability
We want
as small as possible.
No evidence to reject the null hypothesis.
We have tossed a coin 50 times and we got
k = 10 heads . Should we accept/reject the hypothesis that
p = 0.5, provided taht the coin is fair?
P [
or
]
We could
reject hypothesis
at a significance level as low as
p -value is the lowest attainable significance level.
In STATISTICS, to prove something =
reject the hypothesis that converse is true.
We know that on average mouse tail is 5 cm long. We have a group of 10 mice, and give to each of them a dose of vitamin
T everyday, from the birth, for the period of 6 months.
We want to prove that vitamin
X makes mouse tail longer. We measure tail lengths of out group and we get the following sample:
Table 1
5.5
5.6
4.3
5.1
5.2
6.1
5.0
5.2
5.8
4.1
Hypothesis
- sample = sample from normal distribution with
= 5 cm.
Alternative
- sample = sample from normal distribution with
>5 cm.
We do not know population variance, and/or we suspect that vitamin treatment may change the variance - so we use
t distribution .
test (K. Pearson, 1900)
To test the hypothesis that a given data actually come from a population with the proposed distribution. Data is given in the
Table 2 .
Data
0.4319
0.6874
0.5301
0.8774
0.6698
1.1900
0.4360
0.2192
0.5082
0.3564
1.2521
0.7744
0.1954
0.3075
0.6193
0.4527
0.1843
2.2617
0.4048
2.3923
0.7029
0.9500
0.1074
3.3593
0.2112
0.0237
0.0080
0.1897
0.6592
0.5572
1.2336
0.3527
0.9115
0.0326
0.2555
0.7095
0.2360
1.0536
0.6569
0.0552
0.3046
1.2388
0.1402
0.3712
1.6093
1.2595
0.3991
0.3698
0.7944
0.4425
0.6363
2.5008
2.8841
0.9300
3.4827
0.7658
0.3049
1.9015
2.6742
0.3923
0.3974
3.3202
3.2906
1.3283
0.4263
2.2836
0.8007
0.3678
0.2654
0.2938
1.9808
0.6311
0.6535
0.8325
1.4987
0.3137
0.2862
0.2545
0.5899
0.4713
1.6893
0.6375
0.2674
0.0907
1.0383
1.0939
0.1155
1.1676
0.1737
0.0769
1.1692
1.1440
2.4005
2.0369
0.3560
1.3249
0.1358
1.3994
1.4138
0.0046
-
-
-
-
-
-
-
-
Are these data sampled from population with exponential p.d.f.?
Are these data sampled from population with exponential p.d.f.?
Bacteria doesn't produce energy they are dependent upon their substrate in case of lack of nutrients they are able to make spores which helps them to sustain in harsh environments
_Adnan
But not all bacteria make spores, l mean Eukaryotic cells have Mitochondria which acts as powerhouse for them, since bacteria don't have it, what is the substitution for it?
Assimilatory nitrate reduction is a process that occurs in some microorganisms, such as bacteria and archaea, in which nitrate (NO3-) is reduced to nitrite (NO2-), and then further reduced to ammonia (NH3).
Elkana
This process is called assimilatory nitrate reduction because the nitrogen that is produced is incorporated in the cells of microorganisms where it can be used in the synthesis of amino acids and other nitrogen products
There are nothing like emergency disease but there are some common medical emergency which can occur simultaneously like Bleeding,heart attack,Breathing difficulties,severe pain heart stock.Hope you will get my point .Have a nice day ❣️
_Adnan
define infection ,prevention and control
Innocent
I think infection prevention and control is the avoidance of all things we do that gives out break of infections and promotion of health practices that promote life