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The number of children born under the Kibaki administration seemed to have reduced drastically as people focused more on work and business. Under Uhuru seems the children born have increased in economic hard times, it might also be that they are children of the 90s generation.
That does not explain why its just a steady growth from Moi Era…statistical just shows expanded growth from obakos time to mlevi mkuu
That does not explain why its just a steady growth from Moi Era…statistical just shows expanded growth from kibaki time to Mlevi mkuu time.
It is called population momentum. Even if we implemented a one child policy today, population would still grow for around 20 to 30 years if people are living longer(which has been the case since Moi left our life expectancy has grown from 49 years in 2000 to around 60-62 today) and also because most of the people are in their child bearing years.
The reverse also happens. Like in Russia.
Because those born between 1989 and 2000 were so few, even though Putin has managed to raise Russia’s birth rate, the cohort giving birth is so small it cannot offset the deaths of the large boomer population so even though right now Russia has a birth rate close to 2, it will experience population decline until all the Boomers die and the Gen Xers live longer.
Birth rates are not the only factor in population growth.
If that were the case, Central should have a smaller population than Western and Nyanza.
Kikuyus will remain the largest tribe by far for a long time simply because they have children who live past 5 and reach 18 and live longer than other parts of Kenya where there are higher birth rates, but also higher infant mortality rates and lower life expectancies.
A high population growth rate is the cause of poverty.
It’s not vice versa as pop culture would have you believe
skewed maths
How did you get to that conclussion as you still have to test your null hypothesis?
(1) In statistical correlation, you need 2 sets of values which we will call
(x), (y)
In laymans language, this should translate the number of children born during (Moi, Kibaki) (Moi, Uhuru) (Kibaki, Uhuru).
(2) The sum Σ= (x)+(y) [SIZE=1]Example Σ=20 when x=10 and y=10[/SIZE]
Then you will need Variance (S) for both (x) & (y)
for (x) => S[SIZE=1]xx[/SIZE]
for (y) => S[SIZE=1]yy[/SIZE]
[SIZE=4]Variance is the square of the standard deviation, the second central moment of a distribution, and the CoVariance of the random variable with itself.
ie (Moi) Sqr of babies born in year n= 1, 2,3,…24
(Kibaki) sqr ----"----------year 1, 2, 3, 4,…10 etc[/SIZE]
SIZE=4 ρ= sample in a population [/SIZE]
Now we should compare the 2 variances and compute to find
CoVariance S[SIZE=1]xy[/SIZE]
In probability theory and statistics, covariance is a measure of how
much two random variables change together.
A need to further establish the respective mean values of both these X and Y-values is necessary at this juncture.
Validity of the correlation
Now, at this juncture all the necessary values are present to go ahead and examine if there exists
a linear correlation. A linear correlation coefficient, measures the strength and the direction of a linear relationship between two variables. It is also necessary to define a correlation coefficient
The -1 < r < +1 are the signs used for positive and negative linear.
S[SIZE=1]xy[/SIZE]
r = ------------
SQR(S[SIZE=1]xx[/SIZE] * S[SIZE=1]yy[/SIZE])
If r =1 then the correlation is very strong!!
(it’s easier to get Safaricom bundles in Jupiter than getting r=1)
The t-value for null hypothesis
r - [SIZE=4] ρ
t= ------------[/SIZE]
SQR[SIZE=4](1- r^[/SIZE][SIZE=1]2[/SIZE][SIZE=4] / n-2) r to the power of 2[/SIZE]
Therein, one can answer if there is an apparent correlation or the existence of such is simply a coincidence!
Examination if the correlation coefficient obtained is different from 0 gives.
For the null-hypothesis
H[SIZE=1]0[/SIZE][SIZE=4] : ρ=[/SIZE]0
Much much later, thid null hyoothesid can further be tested by examining if the trend line/regression line
Yr=b[SIZE=1]0[/SIZE] + b[SIZE=1]1[/SIZE]x
b[SIZE=1]1[/SIZE]= the slope
S[SIZE=1]xy[/SIZE]
b[SIZE=1]1 [/SIZE]= -------
S[SIZE=1]xx[/SIZE]
tuendelee baadaye ukielewa hizi babaa
^^ Heh…okay. Mungai Kihanya kumbe uko hapa
:meffi::meffi: mushenzi
population growth(illiteracy,tribal custom misconseption of having bazillion kids without a job is a must,rural urban migration and laziness)+poor governance=poverty en masse, hio hesabu ya @Regis2812 ni mathogothanio tu
yes, it’s mathogothanio kama huisomi!
But @KamauL made a claim that there exists a Correlation bila ushahidi.
Now, a correlation is NOT complete without testing the Correlations validiity.
Yaani -1 < r < +1
So, here, if you get an
“r”= 0.32 we say correlation is very weak
“r”= 0.5 we say correlation is weak
“r”= 0.87 we say correlation is Very strong
“r”= 1 ngai fafa :oops:
Thats all for now fratha…but I do not see need to expand kama jamaa hatokei kukubali au kupinga na hiyo ndio the null-hypothesis given as (below)
H[SIZE=1]0[/SIZE] : ρ=0
Clearly, you have the brain capacity of a Pangolin
Exactly on population momentum.
"There is something more stronger in the bedroom than Mao Tse Dong
"
Professor Hans Rosling (R.IP+)
The number of births per woman in the reproductive age bracket is only one of two drivers that matter here. The second one is the number of women in the reproductive age bracket.
The total fertility rate at which a population replaces itself from one generation to the next is called the replacement fertility rate. If no children died before they grew up to have children themselves the replacement fertility rate would be 2. Because some children die before they grow into reproduction bracket, the global replacement fertility rate is currently 2.3 and therefore only slightly lower than the actual global fertility rate.
Excellent points.
bb(eye) or bb(why), yote yawezekana bila kimundu kirea kia kiherehere:(
boss yangu why lie,mi siko poa hesabu ziko na ABCD,naiangalia kama vile mi huangalia na mshangao food kunguru imenipikia eti ni surprise:D:D
bila mambo mengi, tafadhali jisomee kisha uelewe maana yake r ilituweze kwendelesha mjadala huu.
Kiherehere mingi ya kusema nacho sema ni ukweli amauongo hakina manufaa.
Chamuhimu hapa ni kuelewa ama kupata hamu ya kuelewa.
i hope inkusaidia kifedha otherwise ni upuss tu unaongea .good day to you
Again, this is what happens when one punches beyond one’s paygrade.
In my field, this is elememtary, in other fields I would be clueless. That is how life has been, is and will be many centuries after the current 7 billion population has longtime moved on.
Barikiwa