7.3		Statistics (also see 9.10)

Cauchy's Inequality and the Least Squares Line, William Stenger, 6:1, 1975, 2-4
Random Charity: A Stochastic Sieving Problem and its Connection with the
Euclidean Algorithm, Roland Engdahl and Karl Greger, 6:4, 1975, 4-9
Statistical Inference for the General Education StudentIt Can Be Done, Allen H.
Holmes, Walter Sanders and John LeDuc, 8:4, 1977, 223-230
The Use of Sports Data for Integrating Topics in Introductory Statistics,
Robert L. Heiny, 9:1, 1978, 28-33
Classroom Demonstration of a Confidence Interval, Wayne Andrepont and Peter
Dickinson, 9:1, 1978, 34-36
The Range of the Standard Deviation, Lawrence Sher, 10:1, 1979, 33, C
How Close are the Mean and the Median?, Stephen A. Book, 10:3, 1979, 202-204, C
An Expected Value Problem, Harris S. Schultz, 10:4, 1979, 277-278, C
Why n-1 in the Formula for the Sample Standard Deviation?, Stephen A. Book,
10:5, 1979, 330-333
Bounds for the Sum of Absolute Standard Scores, Lawrence Sher, 10:5, 1979,
351-353, C
CorrelationA Vector Approach, Kenneth R. Kundert, 11:1, 1980, 52, C, 5.5
An Expected Value Problem Revisited, W. J. Hall, 11:3, 1980, 204-205
An Analytic Geometry Approach to the Least Squares Line of Best Fit, Stewart
Venit and Richard Katz, 11:4, 1980, 270-272, C, 0.5
A Bound for Standard Scores, Lawrence Sher, 11:2, 1980, 334-335, C
A Mean Generating Function, Jack C. Slay and J.L.Solomon, 12:1, 1981, 27-29,
5.1.2
Partial and Semipartial CorrelationA Vector Approach, John Huber, 12:2, 1981,
151-153, C
Another Look at the Mean, Median, and Standard Deviation, Ruma Falk, 12:3,
1981, 207-208, C
Bounds for the Ratio of the Arithmetic Mean to the Geometric Mean, M.
Perisastry and V.N.Murty, 13:2, 1982, 160-161, C
Nearness Relations Among Measures of Central Tendency and Dispersion: Part 1,
Warren Page and V.N.Murty, 13:5, 1982, 315-326
Nearness Relations Among Measures of Central Tendency and Dispersion: Part 2,
Warren Page and V.N.Murty, 14:1, 1983, 8-17
Another Proof of the Inequality (n^2)(sigma)^2 < (n^2/4)(R^2), V.N.Murty and M.
Perisastry, 14:1, 1983, 61-63, C
Interfractile Ranges, Warren Page, 14:2, 1983, 170-172, C
Uncertainty in Science and Statistics, Clifford H. Wagner, 14:4, 1983, 360-363
Computer Simulations to Clarify Key Ideas of Statistics, Thomas Kersten, 14:5,
1983, 416-420
Some Breakthroughs in Statistical Methodology, Herbert Robbins, 15:1, 1984,
25-29
On the Mean and Standard Deviation of a Random Sample, Vedula N. Murty, 15:1,
1984, 60-62
A Geometrical Interpretation of the Weighted Mean, Larry Hoehn, 15:2, 1984,
135-139, 0.2, 0.4
The Electronic Spreadsheet and Mathematical Algorithms, Deane E. Arganbright,
15:2, 1984, 148-157, 4.1, 5.4.1, 9.6
On the Natural Density of the Niven Numbers, Robert E. Kennedy and Curtis N.
Cooper, 15:4, 1984, 309-312, 9.3
Accurate Computation of Variance, Jerry A. Roberts, 16:2, 1985, 149-150
Instances of Simpson's Paradox, Thomas R. Knapp, 16:3, 1985, 209-211, C, 0.2
The Probability that the "Sum of the Rounds" Equals the "Round of the Sum",
Roger B. Nelsen and James E. Schultz, 18:5, 1987, 390-396, 7.2, 9.10
Should Mathematicians Teach Statistics?, David S. Moore, 19:1, 1988, 3-7, 1.2
Should Mathematicians Teach Statistics? (Response), A. Blanton Godfrey, 19:1,
1988, 8-32, 1.2
No! But Who Should Teach Statistics?, Judith Tanur, 19:1, 1988, 8-32, 1.2
Statistics Teachers need Experience With Data, R. Gnanadesikan and J. R.
Kettenring, 19:1, 1988, 8-32, 1.2
The Mathematicians' Statistics Has a Subsidiary Role, Barbara A. Bailar, 19:1,
1988, 8-32, 1.2
Growth and Advances in Statistics, Frederick Mosteller, 19:1, 1988, 8-32, 1.2
Statistician, Examine Thyself, Gudmund R. Iversen, 19:1, 1988, 8-32, 1.2
It's Not "By Whom" But Rather "How", John E. Freund, 19:1, 1988, 8-32, 1.2
The Need for Good Teaching of Statistics, Henry L. Alder, 19:1, 1988, 8-32, 1.2
Let the Experts Teach and Judge, David L. Hanson, 19:1, 1988, 8-32, 1.2
Who Teachers What to Whom?, Michail Reed, 19:1, 1988, 8-32, 1.2
What Should the Introductory Statistics Course Contain?, Gerald J. Hahn, 19:1,
1988, 8-32, 1.2
Mathematics is Only One Tool that Statisticians Use, Ronald D. Snee, 19:1,
1988, 8-32, 1.2
Reaction to Responses to "Should Mathematicians Teach Statistics?", David S.
Moore, 19:1, 1988, 32-34, 1.2
Readers' Responses to the January 1988 Forum: "Should Mathematicians Teach
Statistics?", Joseph B. Kadane and William A. Golomsky and Daniel A. Sankowsky
and Benjamin M. Perles, 19:2, 1988, 164-165, 1.2
Theory, Simulation and Reality, Peter Flusser, 19:3, 1988, 210-222, 9.10, 7.2
Using Leverage and Influence to Introduce Regression Diagnostics, David C.
Hoaglin, 19:5, 1988, 387-401
Conditional Expectations and the Correlation Function, Barthel W. Huff, 20:1,
1989, 55-57, C
A Note on Pascal's Triangle and Simple Random Sampling, Tommy Wright, 20:1,
1989, 59-66
Using Median Splits to Motivate Learning, David P. Doane, 20:3, 1989, 228-229,
C
Sensitive Questions and Randomized Response Techniques, Kenneth R. Kundert,
20:5, 1989, 409-411, C
The Longest Run of Heads, Mark F. Schilling, 21:3, 1990, 196-207
Bernoulli Trials and the Central Limit Theorem, David P. Kraines and Vivian Y.
Kraines and David A. Smith, 21:5, 1990, 415-416, C
Using Simulation to Study Linear Regression, LeRoy A. Franklin, 23:4, 1992,
290-295, 9.10
Least Squares and Quadric Surfaces, Donald Teets, 24:3, 1993, 243-244, C,
5.7.1, 5.6.2
Determining Sample Sizes for Monte Carlo Integration, David Neal, 24:3, 1993,
254-262, C, 5.2.2, 9.10
Quadratic Confidence Intervals, Neil C. Schwertman and Larry R. Dion, 24:5,
1993, 453-457, C
Chebyshev's Theorem: A Geometric Approach, Pat Touhey, 26:2, 1995, 139-141, C
MAD Property of Medians: An Induction Proof, Eugene F. Schuster, 26:5, 1995, 387-389, C, 0.9
Will the Real Best Fit Curve Please Stand Up?, Helen Skala, 27:3, 1996, 220-223, C, 5.7.1
What is the Margin of Error of a Poll?, Bennett Eisenberg, 28:3, 1997, 201-203, C
What is the Margin of Error of a Poll?, Bennett Eisenberg, 28:3, 1997, 201-203, C
Student’s t and Crackers, Paul M. Sommers, 30:1, 1999, 32-34
Recommendations for Teaching the Reasoning of Statistical Inference, Allan Rossman and Beth Chance, 30:4, 1999, 297-305, 1.1
Getting Normal Probability Approximations Without Using Normal Tables, Peter Thompson and Lorrie Lendvoy, 31:1, 2000, 51-54, C