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Unlock Basic Statistics

Statistics for college and university students. Contains descriptive statistics, probability theory, inferential statistics, hypothesis testing, data analysis and more.

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Chapter 1. Descriptive Statistics
Types of Data and Measurements
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Qualitative and Quantitative Variables
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Qualitative and Quantitative Variables
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3.
The Hierarchy of Measurement Scales
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The Hierarchy of Measurement Scales
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5.
Nominal Scale
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Nominal Scale
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Ordinal Scale
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Ordinal scale
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Interval Scale
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Interval scale
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Ratio Scale
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Ratio Scale
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Frequency Distributions
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Frequency Distributions
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Frequency Distribution Tables
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Frequency Distribution Graphs
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Shape of a Distribution
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Measures of Location I: Quantiles
Measures of Central Tendency
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Introduction to Central Tendency
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Mode
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Median
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Mean
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8.
Central Tendency and the Shape of a Distribution
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Central Tendency and the Shape of a Distribution
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10.
Sensitivity to Outliers
Measures of Variability
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Range, Interquartile Range, and the Five-Number Summary
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Range, Interquartile Range, and the Five-Number Summary
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3.
Interquartile Range Rule for Identifying Outliers
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4.
Interquartile Range Rule for Identifying Outliers
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5.
Deviation from the Mean and the Sum of Squares
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6.
Deviation from the Mean and Sum of Squares
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7.
Variance and Standard Deviation
Measures of Location II: z-Scores
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1.
Z-scores
Chapter 2. Correlation
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Introduction to Correlation
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Displaying the Relationship Between Two Variables
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Displaying the Relationship Between Two Variables
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Measuring the Relationship Between Two Variables
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Measuring the Relationship Between Two Variables
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6.
Direction of a Linear Relationship: Covariance
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Direction of a Linear Relationship: Covariance
3
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Strength of a Linear Relationship: Pearson Correlation Coefficient
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Strength of a Linear Relationship: Pearson Correlation Coefficient
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Hypothesis Test for the Pearson Correlation Coefficient
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Hypothesis Test for the Pearson Correlation Coefficient
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Chapter 3. Probability
Randomness
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Sets, Subsets and Elements
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Sets, Subsets and Elements
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Random experiments
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Sample space
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Sample space
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6.
Events
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Events
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Relationships between Events
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Complement of an Event
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Mutual Exclusivity
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Difference
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Intersection
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Union
Probability
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Definition of Probability
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Probability of the Complement
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Conditional Probability
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Independence
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Probability of the Intersection
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Probability of the Union
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Probability of the Difference
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Law of Total Probability
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Bayes' Theorem
Contingency Tables
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Interpreting Contingency Tables
Chapter 4. Probability Distributions
Probability Models
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Discrete Probability Models
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Discrete Probability Models
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Continuous Probability Models
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Continuous Probability Models
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Random Variables
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Random Variables
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Probability Distributions
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Expected Value of a Random Variable
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Variance of a Random Variable
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9.
Sums of Random Variables
Discrete Probability Distributions
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The Bernoulli Probability Distribution
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The Binomial Probability Distribution
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The Geometric Probability Distribution
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The Poisson Probability Distribution
Continuous Probability Distributions
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The Normal Distribution
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The Normal Probability Distribution
Chapter 5. Sampling
Sampling and Sampling Methods
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Sampling and Unbiased Sampling Methods
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Sampling and Unbiased Sampling Methods
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Biased Sampling Methods
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Sampling Methods
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Sampling Distributions
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Sampling Distributions
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3.
Sampling Distribution of the Sample Mean
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4.
Sampling Distribution of the Sample Mean
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5.
Sampling Distribution of the Sample Proportion
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6.
Sampling Distribution of the Sample Proportion
Unlock full access Chapter 6. Parameter Estimation and Confidence Intervals
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Parameter Estimation
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Parameter Estimation
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Constructing a 95% Confidence Interval for the Population Mean
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Constructing a 95% Confidence Interval for the Population Mean
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Confidence Interval for the Population Mean
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Confidence Interval for the Population Mean
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Confidence Interval for the Population Proportion
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Confidence Interval for the Population Proportion
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Chapter 7. Hypothesis Testing
Introduction to Hypothesis Testing (p-value Approach)
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Hypothesis Testing Procedure
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Hypothesis Testing Procedure
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Formulating the Research Hypotheses
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Formulating the Research Hypotheses
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Two-tailed vs. One-tailed Testing
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Two-tailed vs. One-tailed Testing
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Setting the Criteria for a Decision
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Setting the Criteria for a Decision
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Computing the Test Statistic
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Computing the Test Statistic
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Computing the p-value and Making a Decision
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Computing the p-value and Making a Decision
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13.
Assumptions of the Z-test
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Assumptions of the Z-test
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Connection between Hypothesis Testing and Confidence Intervals
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Connection between Hypothesis Testing and Confidence Intervals
5
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Errors in Decision Making
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Errors in Decision Making
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Statistical Power
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Statistical Power
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Introduction to Hypothesis Testing (Critical Region Approach
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Hypothesis Testing Procedure
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Formulating the Research Hypotheses
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Determining the Critical Region
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7.
Computing the Test Statistic and Making a Decision
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8.
Computing the Test Statistic and Making a Decision
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9.
Assumptions of the z-test
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11.
Connection between Hypothesis Testing and Confidence Intervals
PRACTICE
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12.
Connection between Hypothesis Testing and Confidence Intervals
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13.
Errors in Decision Making
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15.
Statistical Power
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17.
One-tailed Tests
Hypothesis Test for a Population Proportion
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Hypotheses of a Population Proportion Test
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2.
Hypotheses of a Population Proportion Test
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3.
Large-sample Proportion Test: Test Statistic and p-value
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4.
Large-sample Proportion Test: Test Statistic and p-value
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5.
Small-sample Proportion Test: Test Statistic and p-value
PRACTICE
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6.
Small-sample Proportion Test: Test Statistic and p-value
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7.
Hypothesis Test for a Proportion and Confidence Intervals
PRACTICE
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8.
Hypothesis Test for a Proportion and Confidence Intervals
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One-sample t-test: Purpose, Hypotheses, and Assumptions
PRACTICE
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2.
One-sample t-test: Purpose, Hypotheses, and Assumptions
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3.
One-sample t-test: Test Statistic and p-value
PRACTICE
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4.
One-sample t-test: Test Statistic and p-value
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5.
Confidence Interval for μ when σ is Unknown
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6.
Confidence Interval for μ when σ is Unknown
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Paired Samples t-test
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Paired Samples t-test: Purpose, Hypotheses, and Assumptions
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Paired Samples t-test: Purpose, Hypotheses, and Assumptions
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Paired Samples t-test: Test Statistic and p-value
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Paired Samples t-test: Test Statistic and p-value
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5.
Confidence Interval for a Mean Difference
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Confidence Interval for a Mean Difference
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Independent Samples t-test
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Independent Samples t-test: Purpose, Hypotheses, and Assumptions
PRACTICE
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2.
Independent Samples t-test: Purpose, Hypotheses, and Assumptions
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3.
Independent Samples t-test: Test Statistic and p-value
PRACTICE
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4.
Independent Samples t-test: Test Statistic and p-value
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5.
Confidence Interval for the Difference Between Two Independent Means
PRACTICE
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6.
Confidence Interval for the Difference Between Two Independent Means
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THEORY
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1.
Independent Proportions Z-test: Purpose, Hypotheses, and Assumptions
PRACTICE
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2.
Independent Proportions Z-test: Purpose, Hypotheses, and Assumptions
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3.
Independent Proportions Z-test: Test Statistic and p-value
PRACTICE
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4.
Independent Proportions Z-test: Test Statistic and p-value
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5.
Confidence Interval for the Difference Between Two Independent Proportions
PRACTICE
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6.
Confidence Interval for the Difference Between Two Independent Proportions
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Chi-Square Goodness of Fit Test
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1.
Chi-Square Goodness of Fit Test: Purpose, Hypotheses, and Assumptions
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2.
Chi-Square Goodness of Fit Test: Purpose, Hypotheses, and Assumptions
3
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3.
Chi-Square Goodness of Fit Test: Test Statistic and p-value
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4.
Chi-Square Goodness of Fit Test: Test Statistic and p-value
15
Chi-Square Test for Independence
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1.
Chi-Square Test for Independence: Purpose, Hypotheses, and Assumptions
PRACTICE
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2.
Chi-Square Test for Independence: Purpose, Hypotheses, and Assumptions
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3.
Chi-Square Test for Independence: Test Statistic and p-value
PRACTICE
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4.
Chi-Square Test for Independence: Test Statistic and p-value
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One-way Analysis of Variance
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1.
Introduction to Analysis of Variance
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2.
Introduction to Analysis of Variance
5
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3.
One-way ANOVA: Hypotheses and Logic
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One-way ANOVA: Hypotheses and Logic
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5.
One-way ANOVA: Test Statistic
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6.
One-way ANOVA: Test Statistic
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7.
One-way ANOVA: Model and Assumptions
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8.
One-way ANOVA: Model and Assumptions
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One-way ANOVA: Post Hoc Tests
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10.
One-way ANOVA: Post Hoc Tests
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11.
One-way ANOVA: Using R
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12.
One-way ANOVA: Using R
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Chapter 11. Regression Analysis
Simple Linear Regression
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Introduction to Regression Analysis
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2.
Introduction to Regression Analysis
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Residuals and Total Squared Error
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Residuals and Total Squared Error
2
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5.
Finding the Regression Equation
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6.
Finding the Regression Equation
1
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The Coefficient of Determination
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The Coefficient of Determination
1
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9.
Regression Analysis and Causality
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10.
Regression Analysis and Causality
2
Multiple Linear Regression
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Multiple Linear Regression
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3.
Overfitting and Multicollinearity
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5.
Dummy Variables
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