Formulas and Tables by Mario F. Triola Copyright 2010 ...
Formulas and Tables by Mario F. Triola Copyright 2010 Pearson Education, Inc.
Ch. 3: Descriptive Statistics
x ?x Mean n
?f # x
x
Mean (frequency table)
?f
?1x - x22
sB n - 1
Standard deviation
n 1?x 22 - 1?x22 Standard deviation
s B n 1n - 12
(shortcut)
n 3 ?1 f # x224 - 3 ?1 f # x242 Standard deviation
sB
n 1n - 12
(frequency table)
variance s 2
Ch. 4: Probability
P 1A or B2 = P 1A2 + P 1B2 if A, B are mutually exclusive P 1A or B2 = P 1A2 + P 1B2 - P 1A and B2
if A, B are not mutually exclusive
P 1A and B2 = P 1A2 # P 1B2 if A, B are independent
P 1A and B2 = P 1A2 # P1B A2 if A, B are dependent
P 1A2 = 1 - P 1A2 Rule of complements n!
nPr = 1n - r2! Permutations (no elements alike) n!
n 1! n 2! . . . n k! Permutations (n1 alike, ? ) n!
nCr = 1n - r2! r ! Combinations
Ch. 5: Probability Distributions
m = ?x # P 1x2 Mean (prob. dist.)
# s = 2?3x2 P 1x24 - m2 Standard deviation (prob. dist.)
# # P 1x2
=
1n
n! - x2! x !
px qn-x
Binomial probability
m = n #p
Mean (binomial)
s2 = n # p # q
Variance (binomial)
s = 2n # p # q
Standard deviation (binomial)
# mx e -m
P 1x2 = x!
Poisson distribution where e 2.71828
Ch. 6: Normal Distribution
x-x x-m
z=
or
Standard score
s
s
mx = m Central limit theorem
s Central limit theorem
sx = 2n
(Standard error)
Ch. 7: Confidence Intervals (one population)
^p E p ^p E Proportion
pNqN where E = z a>2B n
x - E 6 m 6 x + E Mean
s where E = z a>2 1n (s known)
s or E = ta>2 1n
(s unknown)
1n - 12s2
1n - 12s2
xR2
6 s2 6
x
2 L
Variance
Ch. 7: Sample Size Determination
3z a>242 . 0.25
n= E2
Proportion
3z a>242pNqN
n=
E2
Proportion (^p and q^ are known)
2
z a>2s
n = B R Mean
E
Ch. 9: Confidence Intervals (two populations)
1pN 1 - pN 22 - E 6 1p1 - p22 6 1pN 1 - pN 22 + E
pN 1qN 1 pN 2qN 2 where E = z a>2B n 1 + n 2
1x 1 - x 22 - E 6 1m1 - m22 6 1x 1 - x 22 + E (Indep.)
< where E
=
t a>2B ns 211
+
s
2 2
n2
(df smaller of n1 1, n2 1)
(s1 and s2 unknown and not assumed equal)
< sp2
sp2
E = ta>2Bn 1 + n 2
1df = n 1 + n 2 - 22
sp2
=
1n 1
-
12s
2 1
1n 1 - 12
+ +
1n 2 1n 2
-
12s
2 2
12
(s1 and s2 unknown but assumed equal)
< E
=
z
s 12 a>2B n 1
+
s22 n2
(s1, s2 known)
d - E 6 md 6 d + E (Matched pairs)
where E
=
t
a>2
sd 1n
(df n 1)
Formulas and Tables by Mario F. Triola Copyright 2010 Pearson Education, Inc.
<
Ch. 8: Test Statistics (one population)
pN - p
z=
Proportion--one population
pq
Bn
x - m Mean--one population
z= s> 1n
( known)
x - m Mean--one population
t= s> 1n
( unknown)
1n - 12s2
x2 =
s2
Standard deviation or variance-- one population
Ch. 9: Test Statistics (two populations)
1pN 1 - pN 22 - 1p1 - p22 z=
< pq pq
Bn1 + n2
Two proportions p = x1 + x2 n1 + n2
1x 1 - x 22 - 1m1 - m22
t=
s
2 1
s
2 2
+
Bn1 n2
df smaller of n1 1, n2 1
Two means--independent; s1 and s2 unknown, and not assumed equal.
1x 1 - x 22 - 1m1 - m22
t=
< sp2
sp2
(df n1 n2 2)
+ Bn1 n2
sp2
=
1n 1
-
12s
2 1
n1 +
+ 1n 2 n2 - 2
12s
2 2
Two means--independent; s1 and s2 unknown, but assumed equal.
1x 1 - x 22 - 1m1 - m22
z=
s
2 1
s22
Bn1 + n2
Two means--independent; 1, 2 known.
t = d - md sd> 1n
Two means--matched pairs (df n 1)
F
=
s
2 1
s
2 2
Standard deviation or variance--
two
populations
(where
s
2 1
s
22)
Ch. 11: Goodness-of-Fit and Contingency Tables
1O - E22 Goodness-of-fit
x2 = g E
(df k 1)
1O - E22 Contingency table
x2 = g E
[df (r 1)(c 1)]
1row total21column total2
where E =
1grand total2
1b - c - 122 McNemar's test for matched
x2 =
b+c
pairs (df 1)
Ch. 10: Linear Correlation/Regression
n?xy - 1?x21?y2 Correlation r = 2n1?x 22 - 1?x22 2n1?y22 - 1?y22
a AzxzyB
or r = n-1
where z x = z score for x z y = z score for y
Slope:
n?xy - 1?x21?y2 b1 = n 1?x 22 - 1?x22
sy or b1 = r sx
y-Intercept:
1?y21?x 22 - 1?x21?xy2
b0 = y - b1x or b0 =
n 1?x 22 - 1?x22
yN = b0 + b1x Estimated eq. of regression line
r 2 = explained variation total variation
se
=
?1y - yN22 B n-2
?y2 or B
-
b0?y n-2
b1?xy
yN - E 6 y 6 yN + E Prediction interval
1
n1x 0 - x22
where E = ta>2se B1 + n + n1?x 22 - 1?x22
Ch. 12: One-Way Analysis of Variance
Procedure for testing H0: m1 = m2 = m3 = ? 1. Use software or calculator to obtain results. 2. Identify the P-value. 3. Form conclusion:
If P-value a, reject the null hypothesis of equal means.
If P-value a, fail to reject the null hypothesis of equal means.
Ch. 12: Two-Way Analysis of Variance
Procedure:
1. Use software or a calculator to obtain results. 2. Test H0: There is no interaction between the row factor and
column factor. 3. Stop if H0 from Step 2 is rejected.
If H0 from Step 2 is not rejected (so there does not appear to be an interaction effect), proceed with these two tests:
Test for effects from the row factor. Test for effects from the column factor.
<
Formulas and Tables by Mario F. Triola Copyright 2010 Pearson Education, Inc.
Ch. 13: Nonparametric Tests
1x + 0.52 - 1n>22
z=
Sign test for n 25
1n>2
z=
T - n 1n + 12>4 Wilcoxon signed ranks n 1n + 1212n + 12 (matched pairs and n 30)
B
24
n 11n 1 + n 2 + 12
z = R - mR = R -
2
sR
n 1n 21n 1 + n 2 + 12
B
12
Wilcoxon rank-sum (two independent samples)
H
=
12 N1N +
a
R
2 1
12 n 1
+
R
2 2
n2
+
. . .
+
R
2 k
b
nk
-
31N
+
12
Kruskal-Wallis (chi-square df k 1)
6?d 2 rs = 1 - n1n2 - 12 Rank correlation
acritical value for n 7 30: ; z b 1n - 1
z = G - mG = sG
G - a 2n 1n 2 + 1 b n1 + n2
Runs test
12n 1n 2212n 1n 2 - n 1 - n 22 for n 20
B 1n 1 + n 2221n 1 + n 2 - 12
Ch. 14: Control Charts
R chart: Plot sample ranges
UCL: D4R Centerline: R
LCL: D3R x chart: Plot sample means
UCL:xx + A2R Centerline: xx LCL: xx - A2R
p chart: Plot sample proportions pq
UCL: p + 3 B n
Centerline: p pq
LCL: p - 3 B n
TABLE A-6 Critical Values of the Pearson Correlation
Coefficient r
n
a = .05
4
.950
5
.878
6
.811
7
.754
8
.707
9
.666
10
.632
11
.602
12
.576
13
.553
14
.532
15
.514
16
.497
17
.482
18
.468
19
.456
20
.444
25
.396
30
.361
35
.335
40
.312
45
.294
50
.279
60
.254
70
.236
80
.220
90
.207
100
.196
a = .01
.990 .959 .917 .875 .834 .798 .765 .735 .708 .684 .661 .641 .623 .606 .590 .575 .561 .505 .463 .430 .402 .378 .361 .330 .305 .286 .269 .256
NOTE: To test H0: r = 0 against H1: r Z 0, reject H0 if the absolute value of r is greater than the critical value in the table.
Control Chart Constants
Subgroup Size n
A2
D3
2
1.880 0.000
3
1.023 0.000
4
0.729 0.000
5
0.577 0.000
6
0.483 0.000
7
0.419 0.076
D4
3.267 2.574 2.282 2.114 2.004 1.924
General considerations ? Context of the data ? Source of the data ? Sampling method ? Measures of center ? Measures of variation ? Nature of distribution ? Outliers ? Changes over time ? Conclusions ? Practical implications
FINDING P-VALUES
HYPOTHESIS TEST: WORDING OF FINAL CONCLUSION
Inferences about M: choosing between t and normal distributions
t distribution:
s not known and normally distributed population
or s not known and n 30
Normal distribution: or
s known and normally distributed population s known and n 30
Nonparametric method or bootstrapping: Population not normally distributed and n 30
NEGATIVE z Scores
z
0
TABLE A-2 Standard Normal (z) Distribution: Cumulative Area from the LEFT
z
- 3.50 and lower - 3.4 - 3.3 - 3.2 - 3.1 - 3.0 - 2.9 - 2.8 - 2.7 - 2.6 - 2.5 - 2.4 - 2.3 - 2.2 - 2.1 - 2.0 - 1.9 - 1.8 - 1.7 - 1.6 - 1.5 - 1.4 - 1.3 - 1.2 - 1.1 - 1.0 - 0.9 - 0.8 - 0.7 - 0.6 - 0.5 - 0.4 - 0.3 - 0.2 - 0.1 - 0.0
.00
.0001 .0003 .0005 .0007 .0010 .0013 .0019 .0026 .0035 .0047 .0062 .0082 .0107 .0139 .0179 .0228 .0287 .0359 .0446 .0548 .0668 .0808 .0968 .1151 .1357 .1587 .1841 .2119 .2420 .2743 .3085 .3446 .3821 .4207 .4602 .5000
.01
.02
.03
.04
.05
.06
.07
.08
.0003 .0005 .0007 .0009 .0013 .0018 .0025 .0034 .0045 .0060 .0080 .0104 .0136 .0174 .0222 .0281 .0351 .0436 .0537 .0655 .0793 .0951 .1131 .1335 .1562 .1814 .2090 .2389 .2709 .3050 .3409 .3783 .4168 .4562
.4960
.0003 .0005 .0006 .0009 .0013 .0018 .0024 .0033 .0044 .0059 .0078 .0102 .0132 .0170 .0217 .0274 .0344 .0427 .0526 .0643 .0778 .0934 .1112 .1314 .1539 .1788 .2061 .2358 .2676 .3015 .3372 .3745 .4129 .4522
.4920
.0003 .0004 .0006 .0009 .0012 .0017 .0023 .0032 .0043 .0057 .0075 .0099 .0129 .0166 .0212 .0268 .0336 .0418 .0516 .0630 .0764 .0918 .1093 .1292 .1515 .1762 .2033 .2327 .2643 .2981 .3336 .3707 .4090 .4483
.4880
.0003 .0004
.0003 .0004
.0006 .0006
.0008 .0012 .0016
.0008 .0011 .0016
.0023 .0022
.0031 .0041 .0055
.0030 .0040 .0054
.0073 .0071
.0096 .0125 .0162
.0094 .0122 .0158
.0207
.0202
.0262 .0329 .0409 .0505 .0618 .0749 .0901
.0256 .0322 .0401
* .0495
.0606 .0735 .0885
.1075
.1056
.1271 .1492 .1736
.1251 .1469 .1711
.2005 .1977
.2296 .2611 .2946
.2266 .2578 .2912
.3300 .3264
.3669 .4052 .4443
.3632 .4013 .4404
.4840 .4801
.0003 .0004 .0006 .0008 .0011 .0015 .0021 .0029 .0039 .0052 .0069 .0091 .0119 .0154 .0197 .0250 .0314 .0392 .0485 .0594 .0721 .0869 .1038 .1230 .1446 .1685 .1949 .2236 .2546 .2877 .3228 .3594 .3974 .4364
.4761
.0003 .0004
.0003 .0004
.0005 .0005
.0008 .0011 .0015
.0007 .0010 .0014
.0021
.0020
.0028 .0038 .0051 .0068
.0027 .0037
* .0049
.0066
.0089 .0116 .0150
.0087 .0113 .0146
.0192
.0188
.0244 .0307 .0384
.0239 .0301 .0375
.0475
.0465
.0582 .0708 .0853
.0571 .0694 .0838
.1020
.1003
.1210 .1423 .1660
.1190 .1401 .1635
.1922
.1894
.2206 .2514 .2843
.2177 .2483 .2810
.3192
.3156
.3557 .3936 .4325
.3520 .3897 .4286
.4721
.4681
NOTE: For values of z below - 3.49, use 0.0001 for the area. *Use these common values that result from interpolation:
z score Area - 1.645 0.0500 - 2.575 0.0050
.09
.0002 .0003 .0005 .0007 .0010 .0014 .0019 .0026 .0036 .0048 .0064 .0084 .0110 .0143 .0183 .0233 .0294 .0367 .0455 .0559 .0681 .0823 .0985 .1170 .1379 .1611 .1867 .2148 .2451 .2776 .3121 .3483 .3859 .4247 .4641
................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related download
- chapter 2 descriptive statistics
- chapter 2 exam a
- formulas and tables by mario f triola copyright 2010
- section 2 2 frequency distributions
- for elementary statistics eighth edition by mario f
- triola chapter 2 csun
- triola elementary statistics 5th edition solutions manual
- for elementary statistics tenth edition by mario f
- and the triola statistics series
- chapter 2 describing exploring and comparing data
Related searches
- financial ratios formulas and explanations
- accounting ratios formulas and meaning
- list of excel formulas and functions
- general chemistry formulas and equations
- formulas and functions in ms excel
- complex excel formulas and functions
- probability formulas and examples
- probability formulas and examples statistics
- elementary statistics formulas and examples
- spreadsheet help with formulas and functions
- motor formulas and calculations
- formulas and variables calculator