Concordia University - COMM215: Business Statistics (Solutions)

- COMM 215: Business Statistics is a core course at Concordia University that introduces the fundamentals of statistics as applied to various areas of business and administration. The course covers a wide range of topics, including descriptive statistics, probability, probability distributions, estimation, hypothesis testing, chi-square tests, and regression analysis.

✔ (Summary) Key Areas of Study:

• Business Statistics & Analytics: The central point of this course is to provide an overview of the field of business statistics and analytics, emphasizing the significance of data-driven decision-making in business and administration.
• Tabular and Graphical Methods: Covering the concept of summarizing and organizing data using tables and graphs, this section explores methods such as frequency tables, bar plots, histograms, and box plots.
• Numerical Methods: Focusing on numerical methods for summarizing data, this section includes measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation).
• Probability & Probability Models: Introducing the concept of probability and various probability models, this section discusses discrete and continuous random variables and their applications in business and administration.
• Sampling Distributions: The core idea of this section is the concept of sampling distributions, which are probability distributions of sample-based statistics, and their importance in statistical inference.
• Confidence Intervals: Dealing with the construction and interpretation of confidence intervals, this section explores ranges of estimates for unknown population parameters, calculated at a designated confidence level.
• Hypothesis Testing: This section introduces the process of statistical hypothesis testing, which is used to make probabilistic statements about population parameters based on sample data.
• Chi-Square Tests: Focusing on the use of chi-square tests for analyzing the relationship between categorical variables in contingency tables and for assessing the goodness-of-fit of observed data to expected data.
• SLR Analysis: Covering the basics of simple linear regression (SLR) analysis, this section includes the calculation of regression coefficients, the interpretation of regression results, and the assessment of the strength and direction of the relationship between two variables.
• Multiple Linear Regression & Model Building: This section extends the concepts of simple linear regression to multiple linear regression, which involves the analysis of the relationship between a dependent variable and multiple independent variables, as well as the process of model building and selection.