Course content
Chapter 1: Descriptive statistics
Types of data and measurement
Qualitative and quantitative variables
The hierarchy of measurement scales
Nominal scale
Ordinal scale
Interval scale
Ratio scale
Frequency distributions
Frequency distributions
Frequency distribution tables
Frequency distribution graphs
Shape of a distribution
Measures of location I: Quantiles
Measures of central tendency
Mode
Median
Mean
Central tendency and the shape of a distribution
Sensitivity to outliers
Measures of variability
Range, interquartile range , and the five-number summary
Interquartile range rule for identifying outliers
Deviation from the mean and the sum of squares
Variance and standard deviation
Measures of location II: Z-scores
Z-scores
Chapter 2: Correlation
Correlation
Displaying the relationship between two variables
Measuring the relationship between two variables
Direction of a linear relationship: Covariance
Strength of a linear relationship: Pearson
Chapter 3: Probability
Randomness
Sets, subsets and elements
Random experiments
Sample space
Events
Complement of an event
Relationship between events
Mutual exclusivity
Difference
Intersection
Union
Probability
Definition of probability
Probability of the complement
Conditional probability
Independence
Probability of the intersection
Probability of the union
Probability of the difference
Law of total probability
Bayes’ theorem
Contingency tables
Interpreting contingency tables
Chapter 4: Probability distributions
Probability models
Discrete probability models
Continuous probability models
Random variables
Random variables
Probability distributions
Expected value of the random variable
Variance of a random variable
Sums of random variables
Common distributions
The binomial distribution
Expected value and variance of a binomial random variable
The normal distribution
The normal probability distribution
Chapter 5: Sampling
Sampling and sampling methods
Sampling and unbiased sampling methods
Biased sampling methods
Sampling distributions
Sampling distributions
Sampling distribution of the sample mean
Sampling distribution of the sample proportion
Chapter 6: Parameter estimation confidence intervals
Parameter estimation and the confidence intervals
Parameter estimation
Constructing a 95% confidence interval for the population mean
Confidence interval for the population mean
Confidence interval for the population proportion
Chapter 7: Hypothesis testing
Hypothesis testing
Hypothesis testing procedure
Formulating the research hypothesis
Two-tailed vs one-tailed testing
Setting the criteria for a decision
Computing the test statistic
Computing the p-value and making a decision
Assumptions of the Z-test
Connection between hypothesis testing and confidence intervals
Errors in decision making
Statistical power
Hypothesis test for a population proportion
Hypotheses of a population proportion test
Large-sample proportion test: Test statistic and p-value
Small-sample proportion test: Test statistic and p-value
Hypothesis test for a proportion and confidence intervals
One-sample t-test
One-sample t-test: Purpose, hypotheses, and assumptions
One-sample t-test: Test statistic and p-value
Confidence interval for μ when σ is unkown
Chapter 8: Testing for differences in mean and proportion
Paired samples t-test
Paired samples t-test: Purpose, hypotheses and assumptions
Paired samples t-test: Test statistic and p-value
Confidence interval for a mean difference
Independent samples t-test
Independent samples t-test: Purpose, hypotheses and assumptions
Independent samples t-test: Test statistic and p-value
Confidence interval for the difference between two independent means
Independent proportions z-test
Independent proportion z-test: Purpose, hypotheses and assumptions
Independent proportion z-test: Test statistic and p-value
Confidence interval for the difference between two independent proportions
Chapter 9: Regression analysis
Simple linear regression
Introduction to regression analysis
Residuals and total squared error
Finding the regression equation
The coefficient of determination
Regression analysis and causality
Multiple linear regression
Multiple linear regression
Overfitting and multicollinearity
Dummy variables
Chapter 10: Categorical association
Chi-square godness of fit test
Chi-square goodness of fit test: Purpose, hypotheses and assumptions
Chi-square goodness of fit test: Test statistic and p-value
Chi-square test for independence
Chi-square test for independence: Purpose, hypotheses and assumptions
Chi-square test for independence: Test statistic and p-value
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