With these values, vve eempute hl:
The formulae for ealeulatihg the mean values of the variables is" and F:
With these values, eempute 432-0:
EDD = [155
The equatien of the regression line
[in] Regression to the mean :
On average, the observations tend to cluster around the mean,whether or not they follow a
really unusual value. It only becomes most obvious when a strange resultis
followed by something much more ordinary.
In this example, t
[1) The derived the probability distribution for X isp (I).
The number of breakdowns on a given day is
The value ofmean is given:I by
E[X) = [3.25
The exact answer is [3.25.
[2) The value ofE[X2:I=Z;r2-pl:x:|
[13) The standard normal distribution is
P|:2.25 is 31.25) = PIZE c:1.25)P|:z -=:2.25)
:3 3.33435 3.312224 [3inee on using eaeel funetion, = storm sinv|:pro3o3sfity):|
2 = 3.3321
Therefore, the probability is 3.3321
[13) The standard normal dist
IifJ'ne hundred sixty:r students are randomlyr assigned to reeeitre either the multivitamin
or a pl aeebo and are instrueted to take the as signed drug dailjgir for 20 days.
On dag:r 2E], eaeh student takes a standardized exam and the mean se
The range rule tells us that the standard deviation of a sample is approximatelv equal to one
fourth of the range of the data. In other words.s= 4
From the above range rule, we may not eonvinee that the distribution i
[raj The sample size is as: 3100.
The preparti an is; = [3.056.
The nieanis ELY): up
ZhEIZij = 3100 
The standard deviatien is J: upq
[hjl The page at 3100 eharaeters is feund te have the l
[1) The formula for calculating the range is
Rouge = Moat warm Mississasm
= %Zcfw_1261335)3 ans133.5113 HEB-133530 lo-13353
+(1ss 433.5113 +cfw_144 133.5)2
(n) Instruetinns tn nhtain Regression output using EKEElI
* Enter the variables SEEP and NE in the eelurnns ef the eseel werl-tsheet.
* ICheese Data A; Data Analysis %~Regressien.
* Seleet the Input range I" and and the input range X.
* Cliel-t Lahels
With these values, we compute b1 :
5, 5 34335
The formulae for calculating the mean values of the variables X and I":
(a) From the above regression output,
we note that the value of SSE = [3.1429
(2) From the above regression output,
we note that the value of S = [1138932 .
Step1: The null and alternative hvpothes es are as follows:
He : ,31 = 0 cfw_There is no linear re
The null and alternative hypetheaea are:
H0411: a: = 3
[There is no aignfi cant difference between the mean groups)
H1451 *5 #3 i #3
[There is aignfi cant difference between the mean greupa)
The level cf signicance is at = 0.05.
[22)The 11.9th percentile cfw_area belowr whieh 11.9% of eases
lie in a standard normal distribution is,from tables 1.18
[sinee on using exeel funetion = norm sin V(pfffifigfj:l .
PM = FEE -: it)
Therefore, the 11.9th pereentile for the distributi
(fl) The null and alternative hypotheses are:
There is no relationship in the population between the
H0: 531 = CI response: and predictors:
or the slope is zero
There is a positive relationship in the population between
H1 : :=- Cl the response? and predi
Instructions to obtain Regression output using Minitab 17":
* Store the variables Rates ofreturn ofthe index ICE) and Rates of return of the I:ornpan;=.:r stools, [1")
in the eolumns of the Mnitab worksheet.
* Choose Regression Regression Fit Regression M
With these values, we eornpute bl:
1:1 as 1663014
The formulae for ealeulating the mean values ofthe variables X and I":
With these values, eornpute EDD:
an = 4.36?123
Advocates claim this vaccine is 75% effective in the
preventing the flu. Therefore , a vaccinated individual
exposed to the virus has only a 1 in 4 chance of
contracting the flu. While there may be some sides
effects, do you think a flu shot is worthwhil
CH 7.6 POPULATION AND SAMPLE
Many surveys and experiments are conducted in
order to estimate a population proportion, the
true fraction of individuals or objects that exhibit
a specific characteristic.
For example: pollsters routinely estima
Inference in Large Samples.
Point and Interval Estimation. [Chapter 8]
Statistical inference-drawing conclusions about population
parameters from an analysis of the sample data.
Types of inference:
1.estimation of parameter(s)-obtain an estima
Estimating the Difference between Two Means
The descriptive analysis of the NIH blood pressure data given on the
CD-ROM (located in the Large Data Sets folder) found these results
for the samples of systolic blood pressures:
Testing of Hypothesis:
1) Test For Single Mean:
Example: A sample of 900 members as a mean of 3.4cms and a standard deviation of 2.6cms .Cane
the sample be regarded as drawn from the population with mean 3.25 or not at 5% l.o.s
Explanation: The sample can
1) Population:- A group of individuals under investigation is called population (or) A group of
animates (or) in animates under study.
The population may be finite (or) infinite. In the case of infinite population, a complete
Measures Of Dispersion
Measures of Dispersion
Advantages and Disadvantages
Properties of Dispersion
1) Explain the measures of dispersion.
A) The first important characteristic of a distribution is given by central tendency whereas the