Dane McGuckian
Connect Master for Business Statistics Online Access
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# Connect Master for Business Statistics Online Access

1^{st} Edition

By Dane McGuckian

Copyright: 2017

Copyright: 2017

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Connect Master Business Statistics is a digital-first product appropriate for the introductory undergraduate course. This product breaks free from the limitations and constraints of a traditional textbook’s structure and format. It uses digital adaptive software to assess how students learn, in turn providing them with an enhanced learning experience. This product is designed for instructors with technology-driven courses including hybrid, online, and those employing the flipped classroom model.

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The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.

The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.

- 1.1 The Discipline of Statistics
- 1.2 Types of Statistics
- 1.3 Sample Data vs. Population Data
- 1.4 Types of Data
- 1.5 Levels of Measurement

- 2.1 Summarizing Qualitative Data
- 2.2 Summarizing Quantitative Data
- 2.3 Summation Notation
- 2.4 Measures of Center
- 2.5 Measures of Dispersion
- 2.6 Chebyshev's Theorem
- 2.7 The Empirical Rule
- 2.8 Relative Position
- 2.9 Box Plots and Symmetry

- 3.1 Basic Probability
- 3.2 Counting Techniques
- 3.3 Addition Rule of Probability
- 3.4 Conditional Rule of Probability
- 3.5 Multiplication Rule of Probability
- 3.6 Using Complements

- 4.1 Probability Distributions for Discrete Random Variables
- 4.2 Expected Value, Variance, and Standard Deviation for Discrete Random Variables
- 4.3 The Binomial Probability Distribution
- 4.4 The Poisson Probability Distribution

Module 5: Continuous Random Variables

- 5.1 Continuous Random Variables
- 5.2 The Normal Distribution
- 5.3 Applications of the Normal Distribution
- 5.4 Normal Approximation to the Binomial Distribution

- 6.1 Point Estimators and Sampling Distributions
- 6.2 The Central Limit Theorem

- 7.1 Interval Estimate of the Population Mean with a Known Population Standard Deviation
- 7.2 Sample Size Requirements for Estimating the Population Mean
- 7.3 Interval Estimate of the Population Mean with an Unknown Population Standard Deviation
- 7.4 Interval Estimate of the Population Proportion
- 7.5 Sample Size Requirements for Estimating the Population Proportion

- 8.1 Testing Hypothesis about a Population Mean with a Known Population Standard Deviation
- 8.2 Testing Hypothesis about a Population Mean with an Unknown Population Standard Deviation
- 8.3 Testing Hypothesis about a Population Proportion

- 9.1 Comparisons of Two Population Means with Known Population Standard Deviations Based on Independent Samples
- 9.2 Comparisons of Two Population Means with Unknown Population Standard Deviations Based on Independent Samples with Equal Variances
- 9.3 Comparisons of Two Population Means with Unknown and Assumed Unequal Population Variances Based on Independent Samples
- 9.4 Inferences about Mean Differences (Dependent Samples)
- 9.5 Comparisons of Two Population Proportions Based on Independent Sampling
- 9.6 Comparisons of Two Population Variances Based on Independent Samples

- 10.1 The Chi-Square Goodness-of-Fit Test
- 10.2 The Chi-Square Test of Independence

- 11.1 ANOVA for Completely Randomized Design Experiments
- 11.2 The Coefficient of Correlation
- 11.3 Creating the Least Squares Equation
- 11.4 Making Inference about the Slope of the Regression Line
- 11.5 The Coefficient of Determination
- 11.6 Confidence and Prediction Intervals for Y

- 1.1 The Discipline of Statistics
- 1.2 Types of Statistics
- 1.3 Sample Data vs. Population Data
- 1.4 Types of Data
- 1.5 Levels of Measurement

- 2.1 Summarizing Qualitative Data
- 2.2 Summarizing Quantitative Data
- 2.3 Summation Notation
- 2.4 Measures of Center
- 2.5 Measures of Dispersion
- 2.6 Chebyshev's Theorem
- 2.7 The Empirical Rule
- 2.8 Relative Position
- 2.9 Box Plots and Symmetry

- 3.1 Basic Probability
- 3.2 Counting Techniques
- 3.3 Addition Rule of Probability
- 3.4 Conditional Rule of Probability
- 3.5 Multiplication Rule of Probability
- 3.6 Using Complements

- 4.1 Probability Distributions for Discrete Random Variables
- 4.2 Expected Value, Variance, and Standard Deviation for Discrete Random Variables
- 4.3 The Binomial Probability Distribution
- 4.4 The Poisson Probability Distribution

Module 5: Continuous Random Variables

- 5.1 Continuous Random Variables
- 5.2 The Normal Distribution
- 5.3 Applications of the Normal Distribution
- 5.4 Normal Approximation to the Binomial Distribution

- 6.1 Point Estimators and Sampling Distributions
- 6.2 The Central Limit Theorem

- 7.1 Interval Estimate of the Population Mean with a Known Population Standard Deviation
- 7.2 Sample Size Requirements for Estimating the Population Mean
- 7.3 Interval Estimate of the Population Mean with an Unknown Population Standard Deviation
- 7.4 Interval Estimate of the Population Proportion
- 7.5 Sample Size Requirements for Estimating the Population Proportion

- 8.1 Testing Hypothesis about a Population Mean with a Known Population Standard Deviation
- 8.2 Testing Hypothesis about a Population Mean with an Unknown Population Standard Deviation
- 8.3 Testing Hypothesis about a Population Proportion

- 9.1 Comparisons of Two Population Means with Known Population Standard Deviations Based on Independent Samples
- 9.2 Comparisons of Two Population Means with Unknown Population Standard Deviations Based on Independent Samples with Equal Variances
- 9.3 Comparisons of Two Population Means with Unknown and Assumed Unequal Population Variances Based on Independent Samples
- 9.4 Inferences about Mean Differences (Dependent Samples)
- 9.5 Comparisons of Two Population Proportions Based on Independent Sampling
- 9.6 Comparisons of Two Population Variances Based on Independent Samples

- 10.1 The Chi-Square Goodness-of-Fit Test
- 10.2 The Chi-Square Test of Independence

- 11.1 ANOVA for Completely Randomized Design Experiments
- 11.2 The Coefficient of Correlation
- 11.3 Creating the Least Squares Equation
- 11.4 Making Inference about the Slope of the Regression Line
- 11.5 The Coefficient of Determination
- 11.6 Confidence and Prediction Intervals for Y

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