Between Certainty and Uncertainty

Written by leading experts in the field , here is a short statistic course with notes on historical origins. It serves as reference book in the field of Intelligent Systems.

Between Certainty & Uncertainty is a one-ofa-kind short course on statistics for students, engineers and researchers. It is a fascinating introduction to statistics and probability with notes on historical origins and 80 illustrative numerical examples organized in the five units:

· Chapter 1 Descriptive Statistics: Compressing small samples, basic averages - mean and variance, their main properties including God's proof; linear transformations and z-scored statistics .

· Chapter 2 Grouped data: Udny Yule's concept of qualitative and quantitative variables. Grouping these two kinds of data. Graphical tools. Combinatorial rules and qualitative variables. Designing frequency histogram. Direct and coded evaluation of quantitative data. Significance of percentiles.

· Chapter 3 Regression and correlation: Geometrical distance and equivalent distances in two orthogonal directions as a prerequisite to the concept of two regression lines. Misleading in interpreting two regression lines. Derivation of the two regression lines. Was Hubble right? Houbolt's cloud. What in fact measures the correlation coefficient?

· Chapter 4 Binomial distribution: Middle ages origins of the binomials; figurate numbers and combinatorial rules. Pascal's Arithmetical Triangle. Bernoulli's or Poisson Trials? John Arbuthnot curing binomials. How Newton taught S. Pepys probability. Jacob Bernoulli's Weak Law of Large Numbers and others.

· Chapter 5 Normal distribution and binomial heritage Tables of the normal distribution. Abraham de Moivre and the second theorem of de Moivre-Laplace.

· Chapter 1 Descriptive Statistics: Compressing small samples, basic averages - mean and variance, their main properties including God's proof; linear transformations and z-scored statistics .

· Chapter 2 Grouped data: Udny Yule's concept of qualitative and quantitative variables. Grouping these two kinds of data. Graphical tools. Combinatorial rules and qualitative variables. Designing frequency histogram. Direct and coded evaluation of quantitative data. Significance of percentiles.

· Chapter 3 Regression and correlation: Geometrical distance and equivalent distances in two orthogonal directions as a prerequisite to the concept of two regression lines. Misleading in interpreting two regression lines. Derivation of the two regression lines. Was Hubble right? Houbolt's cloud. What in fact measures the correlation coefficient?

· Chapter 4 Binomial distribution: Middle ages origins of the binomials; figurate numbers and combinatorial rules. Pascal's Arithmetical Triangle. Bernoulli's or Poisson Trials? John Arbuthnot curing binomials. How Newton taught S. Pepys probability. Jacob Bernoulli's Weak Law of Large Numbers and others.

· Chapter 5 Normal distribution and binomial heritage Tables of the normal distribution. Abraham de Moivre and the second theorem of de Moivre-Laplace.

· Chapter 5 Normal distribution and binomial heritage Tables of the normal distribution. Abraham de Moivre and the second theorem of de Moivre-Laplace.

Short statistic course with notes on historical origins Reference book in the field of Intelligent System Written by leading experts in the field

Autorentext
Prof. Dr hab. in . Ludomir M. Laudäski Rzeszow Technical University ul. Wincentego Pola 2 35-959 Rzeszow Poland ludek@prz.edu.pl

Klappentext

"Between Certainty & Uncertainty" is a one-of-a-kind short course on statistics for students, engineers and researchers. It is a fascinating introduction to statistics and probability with notes on historical origins and 80 illustrative numerical examples organized in the five units: · Chapter 1 Descriptive Statistics: Compressing small samples, basic averages - mean and variance, their main properties including God's proof; linear transformations and z-scored statistics . · Chapter 2 Grouped data: Udny Yule's concept of qualitative and quantitative variables. Grouping these two kinds of data. Graphical tools. Combinatorial rules and qualitative variables. Designing frequency histogram. Direct and coded evaluation of quantitative data. Significance of percentiles. · Chapter 3 Regression and correlation: Geometrical distance and equivalent distances in two orthogonal directions as a prerequisite to the concept of two regression lines. Misleading in interpreting two regression lines. Derivation of the two regression lines. Was Hubble right? Houbolt's cloud. What in fact measures the correlation coefficient? · Chapter 4 Binomial distribution: Middle ages origins of the binomials; figurate numbers and combinatorial rules. Pascal's Arithmetical Triangle. Bernoulli's or Poisson Trials? John Arbuthnot curing binomials. How Newton taught S. Pepys probability. Jacob Bernoulli's Weak Law of Large Numbers and others. · Chapter 5 Normal distribution and binomial heritage - Tables of the normal distribution. Abraham de Moivre and the second theorem of de Moivre-Laplace. · Chapter 1 Descriptive Statistics: Compressing small samples, basic averages - mean and variance, their main properties including God's proof; linear transformations and z-scored statistics . · Chapter 2 Grouped data: Udny Yule's concept of qualitative and quantitative variables. Grouping these two kinds of data. Graphical tools. Combinatorial rules and qualitative variables. Designing frequency histogram. Direct and coded evaluation of quantitative data. Significance of percentiles. · Chapter 3 Regression and correlation: Geometrical distance and equivalent distances in two orthogonal directions as a prerequisite to the concept of two regression lines. Misleading in interpreting two regression lines. Derivation of the two regression lines. Was Hubble right? Houbolt's cloud. What in fact measures the correlation coefficient? · Chapter 4 Binomial distribution: Middle ages origins of the binomials; figurate numbers and combinatorial rules. Pascal's Arithmetical Triangle. Bernoulli's or Poisson Trials? John Arbuthnot curing binomials. How Newton taught S. Pepys probability. Jacob Bernoulli's Weak Law of Large Numbers and others. · Chapter 5 Normal distribution and binomial heritage - Tables of the normal distribution. Abraham de Moivre and the second theorem of de Moivre-Laplace. · Chapter 5 Normal distribution and binomial heritage - Tables of the normal distribution. Abraham de Moivre and the second theorem of de Moivre-Laplace.

Zusammenfassung

Between Certainty & Uncertainty is a one-ofa-kind short course on statistics for students, engineers and researchers. It is a fascinating introduction to statistics and probability with notes on historical origins and 80 illustrative numerical examples organized in the five units:

· Chapter 1 Descriptive Statistics: Compressing small samples, basic averages - mean and variance, their main properties including God's proof; linear transformations and z-scored statistics .

· Chapter 2 Grouped data: Udny Yule's concept of qualitative and quantitative variables. Grouping these two kinds of data. Graphical tools. Combinatorial rules and qualitative variables. Designing frequency histogram. Direct and coded evaluation of quantitative data. Significance of percentiles.

· Chapter 3 Regression and correlation: Geometrical distance and equivalent distances in two orthogonal directions as a prerequisite to the concept of two regression lines. Misleading in interpreting two regression lines. Derivation of the two regression lines. Was Hubble right? Houbolt's cloud. What in fact measures the correlation coefficient?

· Chapter 4 Binomial distribution: Middle ages origins of the binomials; figurate numbers and combinatorial rules. Pascal's Arithmet…