ALEKS for Business Statistics User's Guide and Access Code (Stand Alone for 1 Semester) / Edition 1

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More About This Textbook

Overview

ALEKS (Assessment and LEarning in Knowledge Spaces) is an artificial intelligence-based system for individualized learning, available from McGraw-Hill over the World Wide Web. ALEKS delivers precise, qualitative diagnostic assessments of students' knowledge, guides them in the selection of appropriate new study material, and records their progress toward mastery of curricular goals in a robust classroom management system. ALEKS interacts with the student much as a skilled human tutor would, moving between explanation and practice as needed, correcting and analyzing errors, defining terms and changing topics on request. By sophisticated modeling of a student’s "knowledge state" for a given subject matter, ALEKS can focus clearly on what the student is most ready to learn next. When a student focuses on exactly what they are ready to learn, they build learning momentum and success. ALEKS is both comprehensive and flexible. Although it can serve as a resource replacement for a traditional textbook and can be used as a full course solution in business statistics, ALEKS can be used effectively in tandem with any business statistics textbook as well. As a tool for individual assessment and self-study, ALEKS is designed to be used in parallel with the material covered in standard business statistics course―not in addition to it. By assessing each student and developing an individualized plan of self-study for each, ALEKS frees the instructor to focus on those areas of study that are central to business statistics, leaving it to ALEKS to attend to the computational study needs of individual students.

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Product Details

  • ISBN-13: 9780072857757
  • Publisher: McGraw-Hill Higher Education
  • Publication date: 9/11/2002
  • Edition description: New Edition
  • Edition number: 1
  • Product dimensions: 7.40 (w) x 9.10 (h) x 0.20 (d)

Table of Contents


Descriptive statistics
Histograms and frequency polygons
Histograms for grouped data
Relative frequency polygons for grouped data
Mean
Weighted mean: Problem type 1
Weighted mean: Problem type 2
Median
Percentiles
Modes of a data set
Population standard deviation
Sample standard deviation
Interpreting relative frequency histograms
Cumulative relative frequency
Calculating relative frequencies
Probability
Counting techniques
Factorial expressions
Evaluating finite sums
Permutations and combinations: Problem type 1
Permutations and combinations: Problem type 2
Permutations and combinations: Problem type 3
Events and probability
Venn Diagrams: Problem type 1
Venn Diagrams: Problem type 2
Venn Diagrams: Problem type 3
Probability of events
Die tossing
Counting probability: Problem type 1
Counting probability: Problem type 2
Probability of the intersection of two events
Probability of the intersection and union of two independent events
Independent events and mutually exclusive events
Mutually Exclusive Events: Problem type 1
Mutually exclusive events: Problem type 2
Independent Events: Problem type 1
Independent events: Problem type 2
The curious die
Conditional probability
Elementary conditional probabilities
Intersection of events and conditional probability
Conditional probability of mutually exclusive events
Conditional probability of independent events
Law of total probabilities
Bayes' theorem
Random variables
Random variablefunction: Problem type 1
Random variable function: Problem type 2
Discrete and continuous random variables
Probability mass function
Probability distribution of a random variable: Problem type 1
Probability distribution of a random variable: Problem type 2
Marginal distributions of two discrete random variables
Product of two independent random variables
Probabilities of two random variables given their joint distribution
Conditional probabilities of two random variables given their joint distribution
Cumulative distribution function
Expectation and variance of a random variable
Calculating probabilities given the expectation
Linear transformations of expectation and variance
Distributions
Basics
Comparing means without calculation
Comparing variances without calculation
Fundamental distributions
Binomial expectation and variance
Binomial distribution: Problem type 1
Binomial distribution: Problem type 2
Standard normal probabilities
Z-scores of a standard normal distribution
Normal density functions
Normal probabilities
X-scores of a normal distribution
Mean and deviation of a normal distribution
Proportions in a normally distributed population
Student's distribution probabilities
Chi-square distribution probabilities
Fisher's distribution probabilities
Normal approximation to binomial
Normal approximation to binomial with continuity correction: Problem type 1
Normal approximation to binomial with continuity correction: Problem type 2
Central limit theorem
Central limit theorem: Problem type 1
Central limit theorem: Problem type 2
Central limit theorem and sample proportion
Inferential statistics
Confidence intervals
Selecting a distribution for inferences on population mean
Confidence interval for the population mean: Problem type 1
Confidence interval for population mean: Problem type 2
Confidence interval for population proportion
Confidence interval for population variance
Confidence interval for the difference in population means: Problem type 1
Confidence interval for the difference in population means: Problem type 2
Confidence interval for the difference of population proportions
Confidence interval for the ratio of population variances
Hypothesis testing
Determining statistical hypotheses
Hypothesis test for population mean: Problem type 1
Hypothesis test for population mean: Problem type 2
Hypothesis test for population proportion
Hypothesis test for population variance
Hypothesis test for the difference in population means with independent samples: Problem type 1
Hypothesis test for the difference in population means with independent samples: Problem type 2
Hypothesis test for the difference in population means with paired samples
Hypothesis test for the difference of population proportions
Hypothesis test for the ratio of population variances
Anova, goodness of fit, and non-parametric tests
Goodness of fit
Contingency table
Analysis of variance
Signed test
Regression and correlation
Simple linear regression and correlation
Ordering scatter diagrams by increasing correlation coefficients
Calculating the correlation coefficient for bivariate data
Sketching a regression line
Calculating the regression line for bivariate data
Testing the null correlation of bivariate data
Testing the null slope of a regression line
Confidence interval for a value of the dependant variable
Wilcoxon signed-rank test
Multiple regression
Interpretating the regression coefficients
Identifying degrees of freedom
ANOVA table: Problem type 1
ANOVA table: Problem type 2
F test
t test
Interpreting t tests
Time series and quality control
Time series
Trend lines for yearly data
Seasonal indexes
Moving averages and forecasting
Ratio to moving average method
Exponential smoothing
Regression with seasonal indicators
Quality control
Interpreting a control chart
R charts
x-bar charts
p charts
c charts
Acceptance sampling
Estimating sigma from a R chart

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