Use this female adult literacy data to answer the following questions in part I. The female adult literacy rate is the percentage of adult women who can read.
Location | Adult literacy rate (female) |
---|---|
American Samoa |
97 |
CNMI | 96 |
Fiji | 89 |
French Polynesia | 98 |
FSM | 88 |
Guam | 99 |
Kiribati | 90 |
Marshall Islands | 88 |
Nauru | 99 |
Palau | 90 |
Samoa | 97 |
Tonga | 99 |
Tuvalu | 45 |
Vanuatu | 48 |
Wallis Futuna | 50 |
Bins | Frequency | Relative Frequency f/n |
---|---|---|
70 | _________ | _________ |
75 | _________ | _________ |
80 | _________ | _________ |
85 | _________ | _________ |
90 | _________ | _________ |
95 | _________ | _________ |
100 | _________ | _________ |
Sums: | _________ | _________ |
For this part use the following data. The adult female literacy rate is the percentage of women that country who can read. The infant mortality rate is the number of babies per one thousand live births who die before they reach one year in age. So, for example, in the FSM 88% of the adult women in the country can read and of 1000 babies born in the FSM in the year 2000, 33 will die before they reach their first birthday (the infant mortality estimates are for the year 2000). Note that the table continues across the page break.
Location | Adult literacy rate (female) | Infant Mortality per 1000 live births |
---|---|---|
American Samoa |
97 | 11 |
CNMI | 99 | 6 |
Fiji | 89 | 17 |
French Polynesia | 98 | 9 |
FSM | 88 | 33 |
Guam | 99 | 7 |
Kiribati | 90 | 55 |
Marshall Islands | 88 | 41 |
Nauru | 99 | 11 |
Palau | 90 | 17 |
Samoa | 97 | 33 |
Tonga | 99 | 14 |
Tuvalu | 45 | 40 |
Vanuatu | 48 | 63 |
Wallis Futuna | 50 | 25 |
Statistic or Parameter | Symbol | Equations | Excel |
---|---|---|---|
Square root | =SQRT(number) | ||
Sample size | n | =COUNT(data) | |
Minimum | =MIN(data) | ||
Maximum | =MAX(data) | ||
Median | =MEDIAN(data) | ||
Mode | =MODE(data) | ||
Sample mean | Sx/n | =AVERAGE(data) | |
Population mean | m | SX/N x P(x) n p (binomial) |
=AVERAGE(data) |
Sample standard deviation | sx | =STDEV(data) | |
Population standard deviation | s | (binomial) |
=STDEVP(data) |
Sample variance | (sx)² | =VAR(data) | |
Population variance | s² | =VARP(data) | |
Sample Coefficient of Variation | CV | 100(sx/) | =100*STDEV(data)/AVERAGE(data) |
Slope | b | =SLOPE(y data, x data) | |
Intercept | a | =INTERCEPT(y data, x data) | |
Correlation | r | =CORREL(y data, x data) | |
Coefficient of Determination | r² | =(CORREL(y data, x data))^2 | |
Binomial probability | _{n}C_{r} p^{r} q^{(n-r)} | =COMBIN(n,r)*p^r*q^(n-r) | |
Calculate a z value from an x | z | ^{= } | =STANDARDIZE(x, m, s) |
Calculate an x value from a z | x | = s z + m | = s*z+m |
Calculate a z-statistic from an value given m and s | z | =STANDARDIZE(, m, s/SQRT(n)) | |
Calculate a t-statistic or t-ratio or t_{data} | t | =STANDARDIZE(, m, sx/SQRT(n)) | |
Find a probability p from a z value | =NORMSDIST(z) | ||
Find a z value from a probability p | =NORMSINV(p) | ||
Standard error of the population mean | SE | ||
Standard error of the sample mean | SE | ||
Determining z critical from a for confidence intervals. | z_{c} | =NORMSINV(1-a/2) | |
Error tolerance E of a mean for n ³ 30 using s | E | =CONFIDENCE(a,s,n) | |
Error tolerance E of a mean for n ³ 30 using sx | E | =CONFIDENCE(a,sx,n) | |
Error tolerance E of a mean for n < 30. Can also be used for n ³ 30. | E | [no Excel function, determine t_{c} and then multiply by standard error of the mean as shown in the equation] | |
Determining t_{c} from a and the degrees of freedom df for a confidence interval. | t_{c} | =TINV(a,df) | |
Calculate an value from a t_{c}, sx, n, and m | =+ m | ||
Calculate a confidence interval for a population mean m from a sample mean and an error tolerance E | -E< m <+E |
Determining z_{c} from a for a TWO-tail hypothesis test. | =NORMSINV(a/2) [returns only the negative value for z_{c}] |
Determining z_{c} from a for a ONE-tail hypothesis test. | =NORMSINV(a) [returns only the negative value for z_{c}] |
Determining t_{c} from a and degrees of freedom df for a TWO-tail hypothesis test. | =TINV(a, df) [returns only the positive value for t_{c}] |
Determining t_{c} from a and degrees of freedom df for a ONE-tail hypothesis test. | =TINV(2a, df) [returns only the positive value for t_{c}] |
Determining the p-value from a z-statistic, ONE tail | =1-NORMSDIST(ABS(z)) |
Determining the p-value from a z-statistic, TWO tail | =2*(1-NORMSDIST(ABS(z))) |
Determining the p-value from a t-statistic, ONE tail | =TDIST(ABS(t),df,1) |
Determining the p-value from a t-statistic, TWO tail | =TDIST(ABS(t),df,2) |
Standard normal distribution information:
The standard normal Excel functions such as NORMSDIST and NORMSINV use "left" to z as shown at the right below:
Sources:
http://www.odci.gov/cia/publications/factbook/fields/infant_mortality_rate.html
http://www.overpopulation.com/267
http://www.cia.gov/cia/publications/factbook/indexgeo.html
http://www.adb.org/Statistics/Poverty/TUV.asp
http://www.mrdowling.com/800growth.html
http://www.unescap.org/stat/statdata/kiribati.htm
http://www.immigration-usa.com/wfb/wallis_and_futuna_people.html
http://www.overpopulation.com/1507
http://www.library.uu.nl/wesp/populstat/Oceania/naurug.htm
http://www.unicef.org/statis/Country_1Page179.html
http://www.unctad.org/en/docs/ldc99stat_tuv.en.pdf
http://www.abc.net.au/ra/pacific/places/infant_mortality.htm
http://www.adb.org/Statistics/Poverty/KIR.asp