STAT 213: Introduction to Statistics I, University of Calgary introduces students to probability, including Bayes' law, expectations, and distributions. The course covers discrete and continuous random variables, as well as properties of the normal curve.
✔ (Summary) What You Will Learn:
 Population and Sample: The population of interest refers to the entire group in a research context that a researcher wants to study or generalize the findings to, while a sample is a subset of the population. A parameter is a characteristic of the entire population, and a statistic is a characteristic of a sample.
 Quantitative and Qualitative Variables: Quantitative variables are numerical and can be measured, while qualitative variables are categorical and describe nonnumeric characteristics.
 Distribution Shape, Center, and Spread: The shape of a distribution can be described by its symmetry or skewness. The center of a distribution can be measured using various statistics like mean, median, or mode. The spread of a distribution can be described by its range, variance, or standard deviation.
 Probability: Probabilities can be computed for simple events (single outcomes) and compound events (combinations of outcomes). Examples of mutually exclusive events, independent events, and conditional events can be used to illustrate these concepts.
 Random Variables: A random variable is a variable whose possible values are numerical outcomes of a random experiment. Random variables can have a distribution, a measure of center (expected value), and a measure of dispersion (variance and standard deviation).
 Expected Value, Variance, and Standard Deviation: The expected value, variance, and standard deviation can be computed for both discrete and continuous random variables. These summary statistics have the same meaning for both types of random variables.
 Central Limit Theorem (CLT): The Central Limit Theorem can be applied to both the sample mean and sample proportion to determine how likely they are to fall within a given range of values.
 Bivariate Data Analysis: Bivariate data analysis involves determining the strength of a linear relationship between two variables of interest based on a scatter plot and the correlation coefficient, building a simple linear regression line, interpreting the slope and intercept parameter estimates, outlining and checking assumptions behind the simple linear model, and finding the coefficient of determination and explaining its meaning.
 Confidence Intervals (Interval Estimation): Confidence intervals can be constructed and interpreted for a population mean and a population proportion. Confidence interval estimation of the population mean emphasizes the use of the Student's Tdistribution.
 Sample Size Calculation: The required sample size can be computed for a given confidence level and tolerable amount of sampling error when the statistical investigation involves estimation of either a population mean or a population proportion.
 Hypothesis Testing: Hypothesis testing for a population mean and a population proportion involves setting up the null and alternative hypotheses, identifying the appropriate test statistic, computing the value of the test statistic, stating the rejection region, calculating the Pvalue, determining whether the data supports the null hypothesis or not, and interpreting the meaning of the Pvalue in the context of the data.
✔ Knowledge & Skills Gained:
By the end of STAT 213, you will have gained a solid understanding of the fundamental concepts of probability and statistics. You will be able to apply these concepts to realworld problems, such as calculating probabilities, determining expectations, and working with various types of distributions. Additionally, you will be familiar with the properties of the normal curve and its applications in statistical analysis.
