STATISTICS FOUNDATIONS

• Discussion 1
• Exercises 1

KEYWORDS

Statistics, Descriptive Statistics, Inferential Statistics, Population, Census, Sample, Parameter, Statistic, Data, Quantitative Data, Categorical Data

LEARNING OBJECTIVES
Obtain an understanding of the difference between categorical and quantitative data.
Know the meaning of descriptive statistics and statistical inference.
Be able to distinguish between a population and a sample.
Understand the role a sample plays in making statistical inferences about the population.

• Discussion

• KEYWORDS
  Categorical Data, Bar Chart, Pie Chart, Quantitative Data, Histogram

LEARNING OBJECTIVES
Learn how to construct and interpret summarization procedures for categorical data such as: frequency distributions, bar charts and pie charts.
Learn how to construct a histogram as a graphical summary of quantitative data.
Learn how the shape of a data distribution is revealed by a histogram. Learn how to recognize when a data distribution is negatively skewed, symmetric, and positively skewed.

RUN TIME
2:20

• Discussion

KEYWORDS

Descriptive Measure, Mean, Median, Range, Variance, Standard Deviation, Correlation Coefficient, Symmetric, Skewness

LEARNING OBJECTIVES
Understand the purpose of measures of location.
Be able to compute the mean and median.
Understand the purpose of measures of variability.
Be able to compute the range, variance, and standard deviation.
Be able to interpret correlation as a measure of association between two variables.

RUN TIME
13:49

• Discussion

KEYWORDS

Event, Probability, Complement

LEARNING OBJECTIVES
Understand probability as a numerical measure of the likelihood of occurrence.

• Discussion 1
• Exercises 1
• Discussion 2
• Discussion 3

KEYWORDS

Random Variable, Discrete Random Variable, Continuous Random Variable, Discrete Probability Distribution, Expected Value, Poisson Distribution

LEARNING OBJECTIVES
Understand the concepts of a random variable and a probability distribution.
Be able to distinguish between discrete and continuous random variables.
Be able to compute and interpret the expected value, variance, and standard deviation for a discrete random variable.
Be able to compute and work with probabilities involving a Poisson probability distribution.

• Discussion 1

• Exercises 1

• Discussion 2

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• Exercises 2

• Discussion 3

• Exercises 3

• Discussion 4

• Exercises 4

KEYWORDS

Probability Density Function, Uniform Probability Distribution, Normal Probability Distribution, Standard Normal Probability Distribution, z-value, Exponential Probability Distribution

LEARNING OBJECTIVES
Understand the difference between how probabilities are computed for discrete and continuous random variables.
Know how to compute probability values for a continuous uniform probability distribution and be able to compute the expected value and variance for such a distribution.
Be able to compute probabilities using a normal probability distribution.  Understand the role of the standard normal distribution in this process.
Be able to compute probabilities using an exponential probability distribution.
Understand the relationship between the Poisson and exponential probability distributions.

• Discussion

KEYWORDS

Inferential Statistics, Simple Random Sample, Hypothesis Testing, Type I Error, Type II Error, p-value

LEARNING OBJECTIVES
Know what simple random sampling is and how simple random samples are selected.
Understand the types of errors possible when conducting a hypothesis test.
Know how to compute and interpret p-values.

Discussion

KEYWORDS

Dependent Variable, Independent Variable, Regression, Correlation Coefficient

LEARNING OBJECTIVES
Understand how regression analysis can be used to develop an equation that estimates mathematically how two variables are related.
Know how to fit an estimated regression equation to a set of sample data based upon the least-squares method.

RUN TIME
12:40