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ch-19, online questions
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Ch 7 & 8
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xxxxxxxx xxxxxxxxxxxx were xxxx of Hemoglobin4 xxxxxx and packed cell xxxxxx xxx 21 patients aged xx to xx xxxxx xxxx xxx xx CE6. x
xx Make a scatterplot xx xxx xxxxx xxxxxxxxxxx xxx as the xxxxxxxxxxx xxxxxxxx and Hb xx xxx xxxxxxxxx xxxxxxxxx
Scatter plot is xxx best way xx start xxxxxxxxx then xxxxxxxxxxxx xxxxxxx 2 quantitative xxxxxxxxxx Direction xxxxx xxxx an xxxxxx plot means xxxx as PCV increases, Hb xxxx xxxxxxxxxx This xx a sign xx xxxxxxxx xxxxxxxx xxxxxxx xxx and xxx xxx form of relationship looks like a linear xxxxxxxxx xxx xxxx xxxxxx is xxx strong the xxxxxxxxxxxx xxxxxx From xxxx diagram , xxx xxxxxxxxxxxx xxxxx xxx too xxxxxx but xxxxxxxxxx xx is xxx weak.
b. Calculate xxx xxxxxxxxxxx xxxxxxxxxxx between PCV xxx Hb.
Simple linear regression xxxxxxxx Dependent Variable: xxxxxxxxxxxxxx Variable: PCV
xx = xxxxxxxxx x xxxxxxxxxx PCV
xxxxxx size: xx
r (correlation xxxxxxxxxxxx x xxxxxx
xxx value of x ranges xxxx 0 xxxx xxxx shows a strong xxxxxxxx
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ch -19, 3 questions
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In 1998, a san xxxxx reproductive xxxxxx xxxxxxxx 49 xxxx birth to xxx xxxxx xxxxx the age of 40 who xxx xxxxxxxxxx xxxx unable xx conceive.
Find 90% xxxxxxxxxx interval xxx xxx success xxxx at this clinic
Interpret xxxx interval in xxxx context
xxxxxxx what 90% xxxxxxxxxx xxxxx
xx these xxxx refute xxx xxxxxx’x claim of x 25% xxxxxxx rate? Explain.
x
xx have
p x 49/207 = xxxx zc = 1.645 xxx n x 207
We xxxxxxxxxx
xxxx +/- xxxxx sq xxx 0.24 x 0.76)/207 =
xxxx +/-1.645 x xxxxx xxxx x 0.049 xxx xxxx -0.049
xxxxx and xxxxx
b) We are 90% xxxxxxxxxx based on xxxx xxxxxxx xxxx the proportion xx xxx women xxx xxxx xxxxxxxxxx unable to xxxxxxxx will xxxxxxx a xxxxxxx xxxx at xxxx xxxxxx between 19.1% xxx xxxxxx
c) xx we were to select repeated xxxxxx like this xx would xxxxxx xxxxx xxx of the confidence xxxxxxxxx we created xx xxxxxxx xxx true xxxxxxxxxxx
d) No. xxxxx on x 90% xxxxxxxxxx interval, xxx falls within xxx interval.
xxxxxx routine
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ch-18, 2 questions
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4
xx xxxx
xxxxx
xxxx x 0.04
xx = sq xx (pq/n)=0.007
1) xxxxxx is xxxxxx
xx 10% xxxxxxxxx x number of xxxxxxxxx x xxx x xxxx xxx and xxx of failures =0.96 x xxx = 702
xx model xxx p is xxxxxxx xxxxxxx
xxxxxxxxxx that in 732 xxxxxxx 20 are xxxxxx the gene
Probability = xxxxxx = xxxx
xxx xxx xxxxxxx 18 and xx years, xxxxx cholesterol levels xxx xxxxxxxxx have x xxxx of xxxxx xxx a standard xxxxxxxxx xx 40.7 xxxxxx xx data xxxx xxx National Health xxxxxxxx
Mean = xxxxx
SD= xxxx
Find the percentage of xxxxx aged 23, who xxxx a serum xxxxxxxxxxx xxxxx xx xxxxxxxxxxx xxx above). Is this level unusually high?
x xx 210-178.1)/40.7 x 0.784
xxx = 0.784) =0.7823
x >x xxxxx will xx x – 0.7823 x xxxxxx
The xxxxxxxxxx xx males above 210mg/100ml xxxx be 21.77%
In a xxxxx of 25 xxxxx xxxx 23, xxxx is the probability xxxx the mean xxxxx cholesterol xxxxx xxxxxxxxxxxxx xxx above). xx this xxxxx unusually high?
xxxxxxxxxxx that mean serum xxxxxxxxxxx level xxxxxxxxxxxxx or xxxxx = 21.65%
Discuss the xxxxxxxxxxx xxx xxxxxxxxxx xxxx support xxxx xxxxxx and draw a xxxxxxxx xxxxxxx diagram.
1) sample is random
2) 10% xxxxxxxxx x number of successes and xxx of failures xxx more than 10
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ch 5 & 6
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xx xxx book, there xx xxx with xxxxxxxxx multimedia xxxxxxxxxx xxx x = xxxxx xxx find the xxxx
x have mentioned this line in xx xx xxx can xxxx xxxx xx xxxx xx xxxx book and xxxx use it. x have xxxx the table and find xxx xxxxx
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ch-17 2 questions
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xx xxxx x Probability xxxx the citizen xxxxx xxxxx dentist twice x year=p= xxxxxx
Q= xxxxxxx xxxxx n= 10
xxxxxxx x successes in x trial xx 10 xxxxxx x successes and x xxxxxxx
xxxx x xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx . xxxxxxxxxxx
Probability xxxx exactly six see their dentist xxxxx x year x xxx x (0.60)6 x (0.40)4
= 210 x xxxxxxxx x 0.0256 x 0.25 x xxx
At xxxxx xxx means P(6)+P(7)+P(8)+P(9)+P(10)
xxxxx statcrunch, P=>6, 0.633
xxx At xxxx 6, xxxxx xxxxxxxxxx P<=6, 0.62
xxxxx np = 10 x 0.6= 6
SD x sq rt xxxxx x xx rt xxx x .6 x xxxx 1.55
xx HE x
N= xxxxx P=(0.6) , xxxxxxx
Mean = xxx 1000 x xxxx 600
xx x xx rt(npq)= sq xx (1000 x xxx x xxxxx xxxxx
x x xxxxxx xxxxx is a close xxxxxx xxxxxxxxxxxxx xxxx xxx x xxxxx number of trial
x xxxxxxxx model is approximately normal, xx we expect xx xxxxx xx successes xxx xx xxxxxxxx
np≥10 and xx ≥xx
xx this case np= 1000x xxxx 600 which xx ≥10 and xxx xxx ≥10, xx xx xxx say xxxxxx model approximates xxxxx
68 – xx – 99.7 rule
The
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ch-4 2 questions
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xxxxxxxxx xxxxx x snapshot of the xxxxxxxxx xx xxx variable xxxxxxxxx at various value but xxx xxxx and xxxx xxxxxxx is xxxxxx xxxxxx as xx depicts xxx xxxxxxxxxx xxxxxxx
Variable: xxxx xxxxxxx xxxxx xx x digit(s) to the xxxxx of xxx xxxxxx 2 : xxx x x x 568 4 : x : 558889 5 : 123 5 : xxxx : xxxx : 6 7 x xx
Stem xxx xxxx display organizes data into groups called xxxxx and the xxxxxx xxxxxx xxxx xxxxx xxxxxxxx out xx thr right on each xxxx xxxx xxxxx xxx the data xx distributed and where concentration of data occurs. In xxxx xxxxx it xx xx the xxxx of 40’xx
xx xxxxx xxxxx
median= xx
c) xxxxxxxx
xxxxxxxxx 138.58
SD= xxxxx
Summary xxxxxxxxxxx
xxColumn | Mean | xxxxxxxx | xxxx Dev. | Median | Range |
var1 | xxxxx | 138.57631 | 11.771844 | 50 | 45 |
xxx xxxxxxx xxxxx is xxxx xxxxxxx xxxxxx xxx xx the line xx 40 xxx mean, median are xxxxx xxx xxxxxx xxxxxxx
Q4B
b)The space between histogram are xxxxxx gap xx the xxxxx indicating x region xxxxx xxxxx are xx xxxxxxx
c) xxxx number
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There are 2 question of Ch-2 and 2 of Ch-3
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Ch-2 x 4, xxxxxx xx a quantitative variable
xx xx 5W’s – xxxx xxxxx When, xxxx xxx Why
Who- xxxxxxx researchers at a large xxxxxxxxx
What- xxxxxxxx of xxxxxxxx to xxx xxxxxxxxx
xxxx – from xxxx xx 2005
xxxxx xxxxxx patients undergoing xxxxxxxxxxxx
Why- - Impact xx new type xx xxxxxxxxxxxx on four xxxxx of blood xxxxxx
xxx data xx categorical, xx categories on xxx basis xx age, sex, xxxx xx cancer, stage xxx xxxxxxx person responded to xxx treatment.
xxxxxxx 3: Displaying xxx Describing xxxxxxxxxxx Data
xxxx
xxxxxxxxxxx xxxxxxx | 27.9 |
xxxxxx | 22.8 |
Circulatory diseases xxx Stroke | xxx |
Respiratory Diseases | 5.2 |
xxxxxxxxx | xxx |
Other xxxxxx | 33.2 |
xxx chart
Q3 xxx
Age
xxxxxxxxxxxxxxxxxBlood Pressure | Under xx | xxxxx | 0ver xx | xxxxx |
Low | xx | 37 | 31 | 95 |
xxxxxx | 48 | xx | 93 | xxx |
xxxx | 23 | 51 | 73 | 147 |
total | xx | xxx | 197 | xxx |
Percentage of xxxxxxxx over 50 x 197/474*100 x xxxxx
Percentage of xxxxxxxx having xxxx xxxxx xxxxxxxx x 147/474*100 =31.0%
xxxxxxxxxx xx xxxxxxxx xxxx xx xxx with xxxx blood pressure = xxxxxxxxxxxxxxxx
Percentage xx patients xxxx xxxx xxxxx xxxxxxxx xxxx over 50 x 73/147*100=49.7%
Percentage xx xxxxxxxx over 50 xxx High xxxxx xxxxxxxx = xxxxxxxxxxxxxxxx
Yes, xxxxx xx xxxxxxxxxxxx xxxxxxx age xxx xxxxx xxxxxxxx xxxxxxx xxxxx patients are under xxx 37.8% xxxxxxxx are between 30-49 and xxxxx xxxxxxxx are above xxx xx xxx age xxxxxxxxxx blood xxxxxxxx patients also increase.
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ch 16, 2 question in attached file
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16.HE.A:
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxX | xxxx | xx xxxxxxxxx | Deviation xxxxxxx | xxxxxxx xxxxxxxxx | variance | xx |
x | xxxx | 0 | xxxxxx | x | xxx | 0.45 |
1 | xxxx | xxxx | 1-2=-1 | 1 | xxxx | 0.5 |
x | xxxx | 0.7 | 2-2=0 | x | x | 0 |
x | 0.15 | xxxx | 3-2=1 | x | 0.15 | 0.39 |
4 | xxxx | xxx | 4-2=2 | 4 | xxx | xxxx |
x | xxxx | xxxx | xxx =3 | x | 0.45 | 0.67 |
Total x xxxxxxxxxxxxxxxxxx
xxxxxxxx value xx a xxxxxx variable xx xxxxx by xxxxxxxxxxx xxx value xx x with the probability xxxx xxx adding them.
Expected number of patients can xx xxxxx xx xxx table in 3rd xxxxxxx xx xxxxxx xxxxxx be xx fraction, we round xxxx
xxxxx in xxxxx
The xx xxxxx xxxxxx expect x patients to be admitted on a xxxxx day.
xxxxxxxx x 1.65, xxx xx xx xxxxxxxx = 1.28
x
xxxxxxxxxxxxxxxxxxX | xxxx | E(x) | deviation | Squared xxxxxxxxx | variance |
0 | 0.0000 | 0 | xxxxxxxxx -3.9598 | 15.68 | 0 |
x | 0.0001 | xxxxxx | 1-3.9598= -2.9598 | xxxx | xxxxxxxx |
2 | xxxxxx | xxxxxx | xxxxxxxxx xxxxxxx | 3.84 | 0.002304 |
3 | 0.0387 | 0.1161 | 3-3.9598= xxxxxxx | 0.92 | 0.035604 |
4 | xxxxxx | xxxxxx | xxxxxxxxx xxxxxx | 0.0016 | xxxxxxxx |
xxxxx xxxxxxxxxxxxxxxxxxxxxx
Expected value= xxxxxx
xx x 0.2
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final submission
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x have xxx done xx C
22.HE.A:
xxxx xxxxxxx x 95% xxxxxxxxxx xxxxxxxx xxx xxxxxxx the xxxxxxxx xx xxxxxxxx
a) xxxxxxx xxxxx design xxx xxxx to collect xxx xxxx
xxxxx
xxx xxxx xx xxxxxxxxxx x 67 xxxxx p1= 0.19, xxx xxxx
xx xxxxxxxx xxxx xxxxx xxxxxxxxxxx 26 died. p2= 0.29, xxx 0.71
xx xxx xxxxx xx xx xxxxxxxxxxx between xxxxxxxxxx and death
xxx people xxxx depression xxx xxxxx
c) xxxxxxxxxxx are
Independence Assumption: xxxxxx each group, xxx data xxxxxx xx based on xxxxxxx for xxxxxxxxxxx xxxxxxxxxxxx
xxxxxxxxxxxxx xxxxxxxxxx The xxxx xx xxxx group should be xxxxx xxxxxxxxxxxxx xxx at random from x homogeneous xxxxxxxxxx or generated by a xxxxxxxxxx xxxxxxxxxxx xxxxxxxxxxx
xx xxx xxxx xxx xxxxxxx xxxxxxx xxxxxxxxxxxx xxx sample should not xxxxxx xxx of xxx population.
xxxx xxxxxx xxxxxx xx xxx enough that xx xxxxx xx xxxxxxx xxx 10 xxxxxxxx xxx there.
d) SD1= √xxxxxxxxx √0.19 x 0.81/361=0.0261
xxxx √p2.q2/n2= √xxxx x 0.71/89=0.0481
xxxxxxxx error in xxx difference xx two sample
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