Statistics for management / by Gerald Keller

contents📜
1. what is statistics?
-key statistical concept;
-statistical applications in business;
-statistics and the computer;
-world wide web and learning center;
2. graphical and tabular descriptive techniques;
-types of data and information;
-graphical and tabular techniques to describe and nominal data;
-graphical techniques to describable interval data ;
-describing time series data;
-describing the relationship between two nominal variables and comparing two or more nominal data set;
-describing the relationship between two interval variables;
3. art and science of graphical presentation;
-graphical excellence;
-graphical deception;
-presentation statistics: written reports and oral presentation;
4: numerical descriptive techniques;
-measures of central location;
-measures of variability;
-measures of relative standing and box plots;
-measures of linear relationship;
-comparing graphical and numerical techniques;
-general guidelines for exploring data:
5-data collection and sample;
-methods of collection data;
-sampling;
-sampling and non-sampling errors;
-6. probability:
-assigning probability to events;
-probability rules and trees;
-Bayles's law;
-identifying the correct method;
7- random variables and discrete probability;
-random variables and probability distributions;
-bivariate distributions;
-binomial distribution;
-Poisson distribution;
8: continuous probability distributions;
-probability density function:
-normal distribution:
-(optional) exponential distribution;
-other continuous distributions;
9- sampling distributions;
-sampling distribution of the mean;
-sampling distribution of a proportion;
-sampling distribution of the difference;
-from here to interference;
10. introduction to estimation;
-concepts of estimation;
-estimating the population mean when the population standard deviation is known;
-selecting the sample size;
11- introduction to hypotheses testing;
-concepts of hypotheses testing;
-testing the population mean when the population standard deviation is known;
-calculating the probability of a type;
12- inference about a population;
-inference about a population mean;
-inference about a population variance;
-inference about the population proportion;
-applications in marketing;
-applications in accounting;
14. analysis of variance;
-one-way analysis of variance;
-multiple comparisons;
-analysis of variance experimental designs;
-randomized block(two -way )analysis of variance;
15. chi-squared tests;
-chi-squared goodness-of-fit test;
-chi-squared test of a contingency table;
-summary of test on nominal data;
-16-simple linear regression and correction;
-model;
-estimating the coefficients;
-error variable: required conditions;
-assessing the model;
-using the regression equation
-regression diagnostics;
17. multiple regressions;
-model and requirement conditions;
-estimating the coefficients;
-error variable: required conditions;
-assessing the models;
-using the regression equation;
-regression diagnostics;
18. model building;
-polynomial models;
-nominal independent variables;
-applications in human resources;
-logistics regression;
-stepwise regression;
-model building
19. non-parametric statistics;
-Wilcoxon rank sum test;
-sign test and Wilcoxon signed rank sum test;
-Kruskal-Wallis test;
-Friedman test;
-spearman rank correlation coefficient;
20-time-series analysis and forecasting;
-time series components;
-smoothing techniques;
-trend and seasonal effects;
-introduction to forecasting;
-forecasting models;
21. statistical process control;
-process variation;
-control charts;
-control charts;
-control charts for variables.
2.decision analysis;
-decision problem;
-acquiring, using and evaluating additional information;